type of theory in research

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

4 Theories in scientific research

As we know from previous chapters, science is knowledge represented as a collection of ‘theories’ derived using the scientific method. In this chapter, we will examine what a theory is, why we need theories in research, the building blocks of a theory, how to evaluate theories, how can we apply theories in research, and also present illustrative examples of five theories frequently used in social science research.

Theories are explanations of a natural or social behaviour, event, or phenomenon. More formally, a scientific theory is a system of constructs (concepts) and propositions (relationships between those constructs) that collectively presents a logical, systematic, and coherent explanation of a phenomenon of interest within some assumptions and boundary conditions (Bacharach 1989). [1]

Theories should explain why things happen, rather than just describe or predict. Note that it is possible to predict events or behaviours using a set of predictors, without necessarily explaining why such events are taking place. For instance, market analysts predict fluctuations in the stock market based on market announcements, earnings reports of major companies, and new data from the Federal Reserve and other agencies, based on previously observed correlations . Prediction requires only correlations. In contrast, explanations require causations , or understanding of cause-effect relationships. Establishing causation requires three conditions: one, correlations between two constructs, two, temporal precedence (the cause must precede the effect in time), and three, rejection of alternative hypotheses (through testing). Scientific theories are different from theological, philosophical, or other explanations in that scientific theories can be empirically tested using scientific methods.

Explanations can be idiographic or nomothetic. Idiographic explanations are those that explain a single situation or event in idiosyncratic detail. For example, you did poorly on an exam because: you forgot that you had an exam on that day, you arrived late to the exam due to a traffic jam, you panicked midway through the exam, you had to work late the previous evening and could not study for the exam, or even your dog ate your textbook. The explanations may be detailed, accurate, and valid, but they may not apply to other similar situations, even involving the same person, and are hence not generalisable. In contrast, nomothetic explanations seek to explain a class of situations or events rather than a specific situation or event. For example, students who do poorly in exams do so because they did not spend adequate time preparing for exams or because they suffer from nervousness, attention-deficit, or some other medical disorder. Because nomothetic explanations are designed to be generalisable across situations, events, or people, they tend to be less precise, less complete, and less detailed. However, they explain economically, using only a few explanatory variables. Because theories are also intended to serve as generalised explanations for patterns of events, behaviours, or phenomena, theoretical explanations are generally nomothetic in nature.

While understanding theories, it is also important to understand what theories are not. A theory is not data, facts, typologies, taxonomies, or empirical findings. A collection of facts is not a theory, just as a pile of stones is not a house. Likewise, a collection of constructs (e.g., a typology of constructs) is not a theory, because theories must go well beyond constructs to include propositions, explanations, and boundary conditions. Data, facts, and findings operate at the empirical or observational level, while theories operate at a conceptual level and are based on logic rather than observations.

There are many benefits to using theories in research. First, theories provide the underlying logic for the occurrence of natural or social phenomena by explaining the key drivers and outcomes of the target phenomenon, and the underlying processes responsible for driving that phenomenon. Second, they aid in sense-making by helping us synthesise prior empirical findings within a theoretical framework and reconcile contradictory findings by discovering contingent factors influencing the relationship between two constructs in different studies. Third, theories provide guidance for future research by helping identify constructs and relationships that are worthy of further research. Fourth, theories can contribute to cumulative knowledge building by bridging gaps between other theories and by causing existing theories to be re-evaluated in a new light.

However, theories can also have their own share of limitations. As simplified explanations of reality, theories may not always provide adequate explanations of the phenomenon of interest based on a limited set of constructs and relationships. Theories are designed to be simple and parsimonious explanations, while reality may be significantly more complex. Furthermore, theories may impose blinders or limit researchers’ ‘range of vision’, causing them to miss out on important concepts that are not defined by the theory.

Building blocks of a theory

David Whetten (1989) [2] suggests that there are four building blocks of a theory: constructs, propositions, logic, and boundary conditions/assumptions. Constructs capture the ‘what’ of theories (i.e., what concepts are important for explaining a phenomenon?), propositions capture the ‘how’ (i.e., how are these concepts related to each other?), logic represents the ‘why’ (i.e., why are these concepts related?), and boundary conditions/assumptions examines the ‘who, when, and where’ (i.e., under what circumstances will these concepts and relationships work?). Though constructs and propositions were previously discussed in Chapter 2, we describe them again here for the sake of completeness.

Constructs are abstract concepts specified at a high level of abstraction that are chosen specifically to explain the phenomenon of interest. Recall from Chapter 2 that constructs may be unidimensional (i.e., embody a single concept), such as weight or age, or multi-dimensional (i.e., embody multiple underlying concepts), such as personality or culture. While some constructs, such as age, education, and firm size, are easy to understand, others, such as creativity, prejudice, and organisational agility, may be more complex and abstruse, and still others such as trust, attitude, and learning may represent temporal tendencies rather than steady states. Nevertheless, all constructs must have clear and unambiguous operational definitions that should specify exactly how the construct will be measured and at what level of analysis (individual, group, organisational, etc.). Measurable representations of abstract constructs are called variables . For instance, IQ score is a variable that is purported to measure an abstract construct called ‘intelligence’. As noted earlier, scientific research proceeds along two planes: a theoretical plane and an empirical plane. Constructs are conceptualised at the theoretical plane, while variables are operationalised and measured at the empirical (observational) plane. Furthermore, variables may be independent, dependent, mediating, or moderating, as discussed in Chapter 2. The distinction between constructs (conceptualised at the theoretical level) and variables (measured at the empirical level) is shown in Figure 4.1.

Propositions are associations postulated between constructs based on deductive logic. Propositions are stated in declarative form and should ideally indicate a cause-effect relationship (e.g., if X occurs, then Y will follow). Note that propositions may be conjectural but must be testable, and should be rejected if they are not supported by empirical observations. However, like constructs, propositions are stated at the theoretical level, and they can only be tested by examining the corresponding relationship between measurable variables of those constructs. The empirical formulation of propositions, stated as relationships between variables, are called hypotheses . The distinction between propositions (formulated at the theoretical level) and hypotheses (tested at the empirical level) is depicted in Figure 4.1.

The third building block of a theory is the logic that provides the basis for justifying the propositions as postulated. Logic acts like a ‘glue’ that connects the theoretical constructs and provides meaning and relevance to the relationships between these constructs. Logic also represents the ‘explanation’ that lies at the core of a theory. Without logic, propositions will be ad hoc, arbitrary, and meaningless, and cannot be tied into the cohesive ‘system of propositions’ that is the heart of any theory.

Finally, all theories are constrained by assumptions about values, time, and space, and boundary conditions that govern where the theory can be applied and where it cannot be applied. For example, many economic theories assume that human beings are rational (or boundedly rational) and employ utility maximisation based on cost and benefit expectations as a way of understand human behaviour. In contrast, political science theories assume that people are more political than rational, and try to position themselves in their professional or personal environment in a way that maximises their power and control over others. Given the nature of their underlying assumptions, economic and political theories are not directly comparable, and researchers should not use economic theories if their objective is to understand the power structure or its evolution in an organisation. Likewise, theories may have implicit cultural assumptions (e.g., whether they apply to individualistic or collective cultures), temporal assumptions (e.g., whether they apply to early stages or later stages of human behaviour), and spatial assumptions (e.g., whether they apply to certain localities but not to others). If a theory is to be properly used or tested, all of the implicit assumptions that form the boundaries of that theory must be properly understood. Unfortunately, theorists rarely state their implicit assumptions clearly, which leads to frequent misapplications of theories to problem situations in research.

Attributes of a good theory

Theories are simplified and often partial explanations of complex social reality. As such, there can be good explanations or poor explanations, and consequently, there can be good theories or poor theories. How can we evaluate the ‘goodness’ of a given theory? Different criteria have been proposed by different researchers, the more important of which are listed below:

Logical consistency: Are the theoretical constructs, propositions, boundary conditions, and assumptions logically consistent with each other? If some of these ‘building blocks’ of a theory are inconsistent with each other (e.g., a theory assumes rationality, but some constructs represent non-rational concepts), then the theory is a poor theory.

Explanatory power: How much does a given theory explain (or predict) reality? Good theories obviously explain the target phenomenon better than rival theories, as often measured by variance explained (R-squared) value in regression equations.

Falsifiability: British philosopher Karl Popper stated in the 1940s that for theories to be valid, they must be falsifiable. Falsifiability ensures that the theory is potentially disprovable, if empirical data does not match with theoretical propositions, which allows for their empirical testing by researchers. In other words, theories cannot be theories unless they can be empirically testable. Tautological statements, such as ‘a day with high temperatures is a hot day’ are not empirically testable because a hot day is defined (and measured) as a day with high temperatures, and hence, such statements cannot be viewed as a theoretical proposition. Falsifiability requires the presence of rival explanations, it ensures that the constructs are adequately measurable, and so forth. However, note that saying that a theory is falsifiable is not the same as saying that a theory should be falsified. If a theory is indeed falsified based on empirical evidence, then it was probably a poor theory to begin with.

Parsimony: Parsimony examines how much of a phenomenon is explained with how few variables. The concept is attributed to fourteenth century English logician Father William of Ockham (and hence called ‘Ockham’s razor’ or ‘Occam’s razor’), which states that among competing explanations that sufficiently explain the observed evidence, the simplest theory (i.e., one that uses the smallest number of variables or makes the fewest assumptions) is the best. Explanation of a complex social phenomenon can always be increased by adding more and more constructs. However, such an approach defeats the purpose of having a theory, which is intended to be a ‘simplified’ and generalisable explanation of reality. Parsimony relates to the degrees of freedom in a given theory. Parsimonious theories have higher degrees of freedom, which allow them to be more easily generalised to other contexts, settings, and populations.

Approaches to theorising

How do researchers build theories? Steinfeld and Fulk (1990) [3] recommend four such approaches. The first approach is to build theories inductively based on observed patterns of events or behaviours. Such an approach is often called ‘grounded theory building’, because the theory is grounded in empirical observations. This technique is heavily dependent on the observational and interpretive abilities of the researcher, and the resulting theory may be subjective and non-confirmable. Furthermore, observing certain patterns of events will not necessarily make a theory, unless the researcher is able to provide consistent explanations for the observed patterns. We will discuss the grounded theory approach in a later chapter on qualitative research.

The second approach to theory building is to conduct a bottom-up conceptual analysis to identify different sets of predictors relevant to the phenomenon of interest using a predefined framework. One such framework may be a simple input-process-output framework, where the researcher may look for different categories of inputs, such as individual, organisational, and/or technological factors potentially related to the phenomenon of interest (the output), and describe the underlying processes that link these factors to the target phenomenon. This is also an inductive approach that relies heavily on the inductive abilities of the researcher, and interpretation may be biased by researcher’s prior knowledge of the phenomenon being studied.

The third approach to theorising is to extend or modify existing theories to explain a new context, such as by extending theories of individual learning to explain organisational learning. While making such an extension, certain concepts, propositions, and/or boundary conditions of the old theory may be retained and others modified to fit the new context. This deductive approach leverages the rich inventory of social science theories developed by prior theoreticians, and is an efficient way of building new theories by expanding on existing ones.

The fourth approach is to apply existing theories in entirely new contexts by drawing upon the structural similarities between the two contexts. This approach relies on reasoning by analogy, and is probably the most creative way of theorising using a deductive approach. For instance, Markus (1987) [4] used analogic similarities between a nuclear explosion and uncontrolled growth of networks or network-based businesses to propose a critical mass theory of network growth. Just as a nuclear explosion requires a critical mass of radioactive material to sustain a nuclear explosion, Markus suggested that a network requires a critical mass of users to sustain its growth, and without such critical mass, users may leave the network, causing an eventual demise of the network.

Examples of social science theories

In this section, we present brief overviews of a few illustrative theories from different social science disciplines. These theories explain different types of social behaviors, using a set of constructs, propositions, boundary conditions, assumptions, and underlying logic. Note that the following represents just a simplistic introduction to these theories. Readers are advised to consult the original sources of these theories for more details and insights on each theory.

Agency theory. Agency theory (also called principal-agent theory), a classic theory in the organisational economics literature, was originally proposed by Ross (1973) [5] to explain two-party relationships—such as those between an employer and its employees, between organisational executives and shareholders, and between buyers and sellers—whose goals are not congruent with each other. The goal of agency theory is to specify optimal contracts and the conditions under which such contracts may help minimise the effect of goal incongruence. The core assumptions of this theory are that human beings are self-interested individuals, boundedly rational, and risk-averse, and the theory can be applied at the individual or organisational level.

The two parties in this theory are the principal and the agent—the principal employs the agent to perform certain tasks on its behalf. While the principal’s goal is quick and effective completion of the assigned task, the agent’s goal may be working at its own pace, avoiding risks, and seeking self-interest—such as personal pay—over corporate interests, hence, the goal incongruence. Compounding the nature of the problem may be information asymmetry problems caused by the principal’s inability to adequately observe the agent’s behaviour or accurately evaluate the agent’s skill sets. Such asymmetry may lead to agency problems where the agent may not put forth the effort needed to get the task done (the moral hazard problem) or may misrepresent its expertise or skills to get the job but not perform as expected (the adverse selection problem). Typical contracts that are behaviour-based, such as a monthly salary, cannot overcome these problems. Hence, agency theory recommends using outcome-based contracts, such as commissions or a fee payable upon task completion, or mixed contracts that combine behaviour-based and outcome-based incentives. An employee stock option plan is an example of an outcome-based contract, while employee pay is a behaviour-based contract. Agency theory also recommends tools that principals may employ to improve the efficacy of behaviour-based contracts, such as investing in monitoring mechanisms—e.g. hiring supervisors—to counter the information asymmetry caused by moral hazard, designing renewable contracts contingent on the agent’s performance (performance assessment makes the contract partially outcome-based), or by improving the structure of the assigned task to make it more programmable and therefore more observable.

Theory of planned behaviour. Postulated by Azjen (1991), [6] the theory of planned behaviour (TPB) is a generalised theory of human behaviour in social psychology literature that can be used to study a wide range of individual behaviours. It presumes that individual behaviour represents conscious reasoned choice, and is shaped by cognitive thinking and social pressures. The theory postulates that behaviours are based on one’s intention regarding that behaviour, which in turn is a function of the person’s attitude toward the behaviour, subjective norm regarding that behaviour, and perception of control over that behaviour (see Figure 4.2). Attitude is defined as the individual’s overall positive or negative feelings about performing the behaviour in question, which may be assessed as a summation of one’s beliefs regarding the different consequences of that behaviour, weighted by the desirability of those consequences. Subjective norm refers to one’s perception of whether people important to that person expect the person to perform the intended behaviour, and is represented as a weighted combination of the expected norms of different referent groups such as friends, colleagues, or supervisors at work. Behavioural control is one’s perception of internal or external controls constraining the behaviour in question. Internal controls may include the person’s ability to perform the intended behaviour (self-efficacy), while external control refers to the availability of external resources needed to perform that behaviour (facilitating conditions). TPB also suggests that sometimes people may intend to perform a given behaviour but lack the resources needed to do so, and therefore posits that behavioural control can have a direct effect on behaviour, in addition to the indirect effect mediated by intention.

TPB is an extension of an earlier theory called the theory of reasoned action, which included attitude and subjective norm as key drivers of intention, but not behavioural control. The latter construct was added by Ajzen in TPB to account for circumstances when people may have incomplete control over their own behaviours (such as not having high-speed Internet access for web surfing).

Innovation diffusion theory. Innovation diffusion theory (IDT) is a seminal theory in the communications literature that explains how innovations are adopted within a population of potential adopters. The concept was first studied by French sociologist Gabriel Tarde, but the theory was developed by Everett Rogers in 1962 based on observations of 508 diffusion studies. The four key elements in this theory are: innovation, communication channels, time, and social system. Innovations may include new technologies, new practices, or new ideas, and adopters may be individuals or organisations. At the macro (population) level, IDT views innovation diffusion as a process of communication where people in a social system learn about a new innovation and its potential benefits through communication channels—such as mass media or prior adopters— and are persuaded to adopt it. Diffusion is a temporal process—the diffusion process starts off slow among a few early adopters, then picks up speed as the innovation is adopted by the mainstream population, and finally slows down as the adopter population reaches saturation. The cumulative adoption pattern is therefore an s-shaped curve, as shown in Figure 4.3, and the adopter distribution represents a normal distribution. All adopters are not identical, and adopters can be classified into innovators, early adopters, early majority, late majority, and laggards based on the time of their adoption. The rate of diffusion also depends on characteristics of the social system such as the presence of opinion leaders (experts whose opinions are valued by others) and change agents (people who influence others’ behaviours).

At the micro (adopter) level, Rogers (1995) [7] suggests that innovation adoption is a process consisting of five stages: one, knowledge : when adopters first learn about an innovation from mass-media or interpersonal channels, two, persuasion : when they are persuaded by prior adopters to try the innovation, three, decision : their decision to accept or reject the innovation, four,: their initial utilisation of the innovation, and five, confirmation : their decision to continue using it to its fullest potential (see Figure 4.4). Five innovation characteristics are presumed to shape adopters’ innovation adoption decisions: one, relative advantage : the expected benefits of an innovation relative to prior innovations, two, compatibility : the extent to which the innovation fits with the adopter’s work habits, beliefs, and values, three, complexity : the extent to which the innovation is difficult to learn and use, four, trialability : the extent to which the innovation can be tested on a trial basis, and five, observability : the extent to which the results of using the innovation can be clearly observed. The last two characteristics have since been dropped from many innovation studies. Complexity is negatively correlated to innovation adoption, while the other four factors are positively correlated. Innovation adoption also depends on personal factors such as the adopter’s risk-taking propensity, education level, cosmopolitanism, and communication influence. Early adopters are venturesome, well educated, and rely more on mass media for information about the innovation, while later adopters rely more on interpersonal sources—such as friends and family—as their primary source of information. IDT has been criticised for having a ‘pro-innovation bias’—that is for presuming that all innovations are beneficial and will be eventually diffused across the entire population, and because it does not allow for inefficient innovations such as fads or fashions to die off quickly without being adopted by the entire population or being replaced by better innovations.

Elaboration likelihood model . Developed by Petty and Cacioppo (1986), [8] the elaboration likelihood model (ELM) is a dual-process theory of attitude formation or change in psychology literature. It explains how individuals can be influenced to change their attitude toward a certain object, event, or behaviour and the relative efficacy of such change strategies. The ELM posits that one’s attitude may be shaped by two ‘routes’ of influence: the central route and the peripheral route, which differ in the amount of thoughtful information processing or ‘elaboration required of people (see Figure 4.5). The central route requires a person to think about issue-related arguments in an informational message and carefully scrutinise the merits and relevance of those arguments, before forming an informed judgment about the target object. In the peripheral route, subjects rely on external ‘cues’ such as number of prior users, endorsements from experts, or likeability of the endorser, rather than on the quality of arguments, in framing their attitude towards the target object. The latter route is less cognitively demanding, and the routes of attitude change are typically operationalised in the ELM using the argument quality and peripheral cues constructs respectively.

Whether people will be influenced by the central or peripheral routes depends upon their ability and motivation to elaborate the central merits of an argument. This ability and motivation to elaborate is called elaboration likelihood . People in a state of high elaboration likelihood (high ability and high motivation) are more likely to thoughtfully process the information presented and are therefore more influenced by argument quality, while those in the low elaboration likelihood state are more motivated by peripheral cues. Elaboration likelihood is a situational characteristic and not a personal trait. For instance, a doctor may employ the central route for diagnosing and treating a medical ailment (by virtue of his or her expertise of the subject), but may rely on peripheral cues from auto mechanics to understand the problems with his car. As such, the theory has widespread implications about how to enact attitude change toward new products or ideas and even social change.

General deterrence theory. Two utilitarian philosophers of the eighteenth century, Cesare Beccaria and Jeremy Bentham, formulated general deterrence theory (GDT) as both an explanation of crime and a method for reducing it. GDT examines why certain individuals engage in deviant, anti-social, or criminal behaviours. This theory holds that people are fundamentally rational (for both conforming and deviant behaviours), and that they freely choose deviant behaviours based on a rational cost-benefit calculation. Because people naturally choose utility-maximising behaviours, deviant choices that engender personal gain or pleasure can be controlled by increasing the costs of such behaviours in the form of punishments (countermeasures) as well as increasing the probability of apprehension. Swiftness, severity, and certainty of punishments are the key constructs in GDT.

While classical positivist research in criminology seeks generalised causes of criminal behaviours, such as poverty, lack of education, psychological conditions, and recommends strategies to rehabilitate criminals, such as by providing them job training and medical treatment, GDT focuses on the criminal decision-making process and situational factors that influence that process. Hence, a criminal’s personal situation—such as his personal values, his affluence, and his need for money—and the environmental context—such as how protected the target is, how efficient the local police are, how likely criminals are to be apprehended—play key roles in this decision-making process. The focus of GDT is not how to rehabilitate criminals and avert future criminal behaviours, but how to make criminal activities less attractive and therefore prevent crimes. To that end, ‘target hardening’ such as installing deadbolts and building self-defence skills, legal deterrents such as eliminating parole for certain crimes, ‘three strikes law’ (mandatory incarceration for three offences, even if the offences are minor and not worth imprisonment), and the death penalty, increasing the chances of apprehension using means such as neighbourhood watch programs, special task forces on drugs or gang-related crimes, and increased police patrols, and educational programs such as highly visible notices such as ‘Trespassers will be prosecuted’ are effective in preventing crimes. This theory has interesting implications not only for traditional crimes, but also for contemporary white-collar crimes such as insider trading, software piracy, and illegal sharing of music.

• Bacharach, S.B. (1989). Organizational theories: some criteria for evaluation. Academy of Management Review , 14(4), 496-515. ↵
• Whetten, D. (1989). What constitutes a theoretical contribution? Academy of Management Review , 14(4), 490-495. ↵
• Steinfield, C.W. and Fulk, J. (1990). The theory imperative. In J. Fulk & C.W. (Eds.), Organizations and communications technology (pp. 13–26). Newsburt Park, CA: Sage Publications. ↵
• Markus, M.L. (1987). Toward a ‘critical mass’ theory of interactive media: universal access, interdependence and diffusion. Communication Research , 14(5), 491-511. ↵
• Ross, S.A. (1973). The economic theory of agency: The principal’s problem. American Economic , 63(2), 134-139 ↵
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes , (50), 179–211. ↵
• Rogers, E. (1995). Diffusion of innovations (4th ed.). New York: Free Press. ↵
• Petty, R.E. and Cacioppo, J.T. (1986). C ommunication and persuasion: Central and peripheral routes to attitude change . New York: Springer-Verlag. ↵

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

afrihost - pure internet joy

Domain inactive, sorry, this site is currently not available., what's going on.

You are seeing this page instead of the one you are looking for because of a current technical or administrative problem with the site in question.

The site will be available once the problem has been resolved.

If you are the webmaster of this site, please Contact Afrihost as soon as possible.

• Library databases
• Library website

Theories and Frameworks: Introduction

Theoretical & conceptual frameworks.

The terms theoretical framework and conceptual framework are often used interchangeably to mean the same thing. Although they are both used to understand a research problem and guide the development, collection, and analysis of research, it's important to understand the difference between the two. When working on coursework or dissertation research, make sure to clarify what is being asked and any specific course or program requirements. 

Theoretical framework 

A theoretical framework is a single formal theory. When a study is designed around a theoretical framework, the theory is the primary means in which the research problem is understood and investigated. Although theoretical frameworks tend to be used in quantitative studies, you will also see this approach in qualitative research.  

Conceptual framework

A conceptual framework includes one or more formal theories (in part or whole) as well as other concepts and empirical findings from the literature. It is used to show relationships among these ideas and how they relate to the research study. Conceptual frameworks are commonly seen in qualitative research in the social and behavioral sciences, for example, because often one theory cannot fully address the phenomena being studied.

Investigate theory

Identifying and learning about theories requires a different search strategy than other types of research. Even though the steps are different, you will still use many of the same skills and tools you’ve used for other library research.

• psychology:  human development, cognition, personality, motivation
• sociology:  social change, race, class, gender
• business:  leadership, management
• health:  patient care, well-being, environment
• course textbooks
• encyclopedias and handbooks
• credible websites

Theory in doctoral research

Identifying a theory that aligns with your dissertation or doctoral study takes time. It’s never too early to start exploratory research. The process of identifying an appropriate theory can seem daunting, so try breaking down the process into smaller steps.

• your theory courses
• completed dissertations and doctoral studies
• the scholarly literature on your topic
• Keep a list of theories and take notes on how and why they were used.
• Identify and learn more about relevant theories.
• Locate influential and seminal works  related to those theories.
• Next Page: Discover Theories
• Office of Student Disability Services

Walden Resources

Departments.

• Academic Residencies
• Academic Skills
• Career Planning and Development
• Customer Care Team
• Field Experience
• Military Services
• Student Success Advising
• Writing Skills

Centers and Offices

• Center for Social Change
• Office of Academic Support and Instructional Services
• Office of Degree Acceleration
• Office of Research and Doctoral Services
• Office of Student Affairs

Student Resources

• Doctoral Writing Assessment
• Form & Style Review
• Quick Answers
• ScholarWorks
• SKIL Courses and Workshops
• Walden Bookstore
• Walden Catalog & Student Handbook
• Student Safety/Title IX
• Legal & Consumer Information
• Website Terms and Conditions
• Cookie Policy
• Accessibility
• Accreditation
• State Authorization
• Net Price Calculator
• Contact Walden

Walden University is a member of Adtalem Global Education, Inc. www.adtalem.com Walden University is certified to operate by SCHEV © 2024 Walden University LLC. All rights reserved.

Theory can be defined as “a formal logical explanation of some events that includes predictions of how things relate to one another” [1] .

Concepts are “collectives used to label certain bits of experience. In other words, they are elementary constructs by which reality is classified and categorised” [2] . Alternatively, concepts are “mental constructs or images developed to symbolise ideas, persons, things, or events” [3]

Model can be defined as “a set of ideas and numbers that describe the past, present or future state of something” [4]

A framework is “a system of rules, laws, agreements, etc. that establish the way that something operates” [5]

A tool is “a thing used to perform a job” [6] .

There are many theories, concepts, models, frameworks and tools in the area of business that you will have to use at some stage of your studies. The following are the most popular ones listed in alphabetical order:

• Kotter’s Eight Steps Model
• Lewin’s Force-Field Theory of Change
• Lewin’s Model of Change

Consumer behavior

• Behaviorist approach to consumer behavior
• Cognitive approach to consumer behavior
• Consumer buyer behavior
• Consumer decision making process
• Customer perceived value
• Economic Man approach to consumer behavior
• Model of Goal Directed Behavior
• Psychodynamic Approach to consumer behavior
• Theory of Trying

Corporate Social Responsibility

• Approaches to CSR
• Carrol’s CSR Pyramid
• Code of Ethics
• Cross-cultural competencies
• Cross-cultural management
• Elements of organizational culture
• Harrison’s Model of Culture
• Hofstede’s Cultural Dimensions
• Trompenaars and Hampden-Turner Cultural Dimensions
• Foreign Direct Investment
• Inter-industry and intra-industry trade
• Liberal and coordinated market economies
• Specialization for developing countries
• Theory of absolute advantage
• Theory of comparative advantage

Human Resources Management

• Cognitive Theory of Training Transfer
• Continuous Professional Development
• Equity Theory of Motivation
• Employee training and development
• Forms of training transfer
• Frederick Hertzberg’s Two Factor Theory
• Hertzberg’s Motivation and Hygiene Factors
• Honey and Mumford’s Learning Style
• Maslow’s Hierarchy of Needs
• McClelland’s Achievement Motivation
• Job analysis as an important HRM factor
• Recruitment and selection plan
• Stimulus Generalization Theory
• Tangible and intangible employee motivation tools
• Theory X and Theory Y
• The Principle of Identical Elements
• Three Cs of Intrinsic Motivation

Management and Leadership

• Belbin’s Team Roles Theory
• Critical path analysis
• Cycles of failure and cycles of success
• Executive information systems
• Fiedler’s Theory
• Functions of leadership
• Hersey and Blanchard’s Model of Leadership
• Leadership Continuum Theory
• Leadership vs Management
• Management information system
• Management structure
• Managerial Grid by Blake and Mouton
• Organization information needs
• Path-Goal Theory
• The Great Men Theory
• The importance of time management
• Transactional leadership style
• Tuckman Theory for Developing Teams
• Ansoff Growth Matrix
• Brand Essence Wheel
• Classification of viral marketing
• Integrated Marketing Communications
• Marketing Communication Mix
• Marketing strategy
• Porter’s Generic Strategies
• Product life cycle
• Product Placement
• Segmentation, Targeting & Positioning
• Self-Reference Criterion
• Standardization vs adaptation
• Strategic marketing process
• Viral marketing criticism
• Unique Selling Proposition
• 7 Ps of Marketing
• Gaps Model of Service Quality
• Greenfield investment
• McKinsey 7s Model
• New market entry strategies
• PESTEL analysis
• Porter’s Five Forces analysis
• Strategy as Revolution
• SWOT analysis
• “Think Globally, Act Locally”: a critical analysis
• Value chain analysis

Some of the above (hyperlinked) are explained in this portal. The portal will be expanded to include in-depth explanations of theories, concepts, models, frameworks and tools in the area of business studies in a regular manner.

[1] Zikmund, W.G., Babin, J., Carr, J. & Griffin, M. (2012) “Business Research Methods: with Qualtrics Printed Access Card” Cengage Learning  p.38

[2] Fox, W. & Bayat, M.S. (2007) “A Guide to Managing Research” Juta Publications  p.45

[3] Monette, D.R., Gullivan, T.J. & DeJong, C.R. (2011) “Applied Social Research: A Tool for the Human Resources” 8 th edition, Cengage Learning p.30

[4] Merriam-Webster (2015) Available at: http://www.merriam-webster.com/dictionary/model

[5] Macmillan Dictionary (2015) Available at: http://www.macmillandictionary.com/dictionary/british/framework

[6] Oxford Dictionaries (2015) Available at: http://www.macmillandictionary.com/dictionary/british/framework

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

4.2 The Variety of Theories in Psychology

Learning objectives.

• Describe three dimensions along which theories in psychology vary.
• Give examples of several different types of theories in psychology.

Researchers in psychology have found that many different types of theories can help them to organize phenomena, predict what will happen in new situations, and generate new research. It is important for beginning researchers to be aware of the different types so that they recognize theories when they see them in the research literature. (They are not always clearly labeled as “theories.”) It is also important for them to see that some types of theories are well within their ability to understand, use, and even construct. In this section, we look at the variety of psychological theories in terms of three important dimensions: formality, scope, and theoretical approach.

Psychological theories vary widely in their formality —the extent to which the components of the theory and the relationships among them are specified clearly and in detail. At the informal end of this dimension are theories that consist of simple verbal descriptions of a few important components and relationships. The habituation theory of expressive-writing effects on health is relatively informal in this sense. So is the drive theory of social facilitation and inhibition. At the more precise, formal end of this dimension are theories that are expressed in terms of mathematical equations or computer programs.

Formal Theories in Psychology

People who are not familiar with scientific psychology are sometimes surprised to learn that psychological theories can take the form of mathematical equations and computer programs. The following formal theories are among the best known and most successful in the field.

• ACT-R. A comprehensive theory of human cognition that is akin to a programming language, within which more specific models can be created. See http://act-r.psy.cmu.edu .
• Prospect theory. A formal theory of decision making under uncertainty. Psychologist Daniel Kahneman won the Nobel Prize in economics based in part on prospect theory. Read about Kahneman’s Nobel Prize work at http://www.nobelprize.org/nobel_prizes/economics/laureates/2002/kahneman-autobio.html .
• Rescorla-Wagner model. A theory of classical conditioning that features an equation describing how the strength of the association between unconditioned and conditioned stimuli changes when the two are paired. For more on this formal theory—including an interactive version—see http://psych.hanover.edu/javatest/rescrolawagner .

Both informal and formal theories have their place in psychological research. Informal theories tend to be easier to create and to understand but less precise in their predictions, which can make them more difficult to test. They are especially appropriate, however, in the early stages of research when the phenomena of interest have not yet been described in detail. Formal theories tend to be more difficult to create and to understand—sometimes requiring a certain amount of mathematical or computer programming background—but they also tend to be more precise in their predictions and therefore easier to test. They are especially appropriate in the later stages of research when the phenomena of interest have been described in detail

Theories in psychology also vary widely in their scope —the number and diversity of the phenomena they explain or interpret. Many early psychological theories were extremely broad in that they attempted to interpret essentially all human behavior. Freud and his followers, for example, applied his theory not only to understanding psychological disorders but also to slips of the tongue and other everyday errors, dreaming, sexuality, art, politics, and even civilization itself (Fine, 1979). Such theories have fallen out of favor in scientific psychology, however, because they tend to be imprecise and difficult to test. In addition, they have not been particularly successful at organizing or predicting the range and complexity of human behavior at the level of detail that scientific researchers usually seek.

Still, contemporary theories in psychology can vary in their scope. At the broad end of this dimension are theories that apply to many diverse phenomena. Cognitive dissonance theory, for example, assumes that when people hold inconsistent beliefs, this creates mental discomfort that they are motivated to reduce by changing one or both of the beliefs. This theory has been applied to a wide variety of phenomena, including the persistence of irrational beliefs and behaviors (e.g., smoking), the effectiveness of certain persuasion and sales techniques (e.g., asking for a small favor before asking for a big one), and even placebo effects. At the narrow end of this dimension are theories that apply to a small number of closely related phenomena. Consider, for example, a very specific quantitative ability called subitizing. This refers to people’s ability to quickly and accurately perceive the number of objects in a scene without counting them—as long as the number is four or fewer. Several theories have been proposed to explain subitizing. Among them is the idea that small numbers of objects are associated with easily recognizable patterns. For example, people know immediately that there are three objects in a scene because the three objects tend to form a “triangle” and it is this pattern that is quickly perceived (Logan & Sbrodoff, 2003).

As with informal and formal theories, both broad and narrow theories have their place in psychological research. Broad theories organize more phenomena but tend to be less formal and less precise in their predictions. Narrow theories organize fewer phenomena but tend to be more formal and more precise in their predictions.

Theoretical Approach

In addition to varying in formality and scope, theories in psychology vary widely in the kinds of theoretical ideas they are constructed from. We will refer to this as the theoretical approach .

Functional theories explain psychological phenomena in terms of their function or purpose. For example, one prominent theory of repeated self-injury (e.g., cutting) is that people do it because it produces a short-term reduction in the intensity of negative emotions that they are feeling (Tantam & Huband, 2009). Note that this theory does not focus on how this happens, but on the function of self-injury for the people who engage in it. Theories from the perspective of evolutionary psychology also tend to be functional—assuming that human behavior has evolved to solve specific adaptive problems faced by our distant ancestors. Consider the phenomenon of sex differences in human mating strategies (Buss & Schmitt, 1993). Men are somewhat more likely than women to seek short-term partners and to value physical attractiveness over material resources in a mate. Women are somewhat more likely than men to seek long-term partners and to value material resources over physical attractiveness in a mate. But why? The standard evolutionary theory holds that because the male investment in becoming a parent is relatively small, men reproduce more successfully by seeking several short-term partners who are young and healthy (which is signaled by physical attractiveness). But because the female investment in becoming a parent is quite large, women reproduce more successfully by seeking a long-term partner who has resources to contribute to raising the child.

Mechanistic theories , on the other hand, focus on specific variables, structures, and processes, and how they interact to produce the phenomena. The drive theory of social facilitation and inhibition and the multistore model of human memory are mechanistic theories in this sense. Figure 4.4 “Simplified Representation of One Contemporary Theory of Hypochondriasis” represents another example—a contemporary cognitive theory of hypochondriasis—an extreme form of health anxiety in which people misinterpret ordinary bodily symptoms (e.g., headaches) as signs of a serious illness (e.g., a brain tumor; Williams, 2004). This theory specifies several key variables and the relationships among them. Specifically, people who are high in the personality trait of neuroticism (also called negative emotionality) start to pay excessive attention to negative health information—especially if they have had a significant illness experience as a child (e.g., a seriously ill parent). This attention to negative health information then leads to health anxiety and hypochondriasis, especially among people who are low in effortful control, which is the ability to shift attention away from negative thoughts and feelings.

Figure 4.4 Simplified Representation of One Contemporary Theory of Hypochondriasis

This theory focuses on key variables and the relationships among them.

Mechanistic theories can also be expressed in terms of biological structures and processes. With advances in genetics and neuroscience, such theories are becoming increasingly common in psychology. For example, researchers are currently constructing and testing theories that specify the brain structures associated with the storage and rehearsal of information in the short-term store, the transfer of information to the long-term store, and so on. Theories of psychological disorders are also increasingly likely to focus on biological mechanisms. Schizophrenia, for example, has been explained in terms of several biological theories, including theories that focus on genetics, neurotransmitters, brain structures, and even prenatal exposure to infections.

Finally, there are also theoretical approaches that provide organization without necessarily providing a functional or mechanistic explanation. These include stage theories , which specify a series of stages that people pass through as they develop or adapt to their environment. Famous stage theories include Abraham Maslow’s hierarchy of needs and Jean Piaget’s theory of cognitive development. Typologies provide organization by categorizing people or behavior into distinct types. These include theories that identify several basic emotions (e.g., happiness, sadness, fear, surprise, anger, and disgust), several distinct types of intelligence (e.g., spatial, linguistic, mathematical, kinesthetic, musical, interpersonal, and intrapersonal), and distinct types of personalities (e.g., Type A vs. Type B).

Researchers in psychology have found that there is a place for all these theoretical approaches. In fact, multiple approaches are probably necessary to provide a complete understanding of any set of phenomena. A complete understanding of emotions, for example, is likely to require identifying the basic emotions that people experience, explaining why we have those emotions, and describing how those emotions work in terms of underlying psychological and biological variables, structures, and processes.

Key Takeaway

• Theories in psychology vary widely in terms of their formality, scope, and theoretical approach. The different types of theories all play important roles in psychological research.
• Practice: Find an empirical research report in a professional journal, identify a theory that the researchers present, and then describe the theory in terms of its formality (informal vs. formal), scope (broad vs. narrow), and theoretical approach (functional, mechanistic, etc.).
• Discussion: Do you think there will ever be a single theory that explains all psychological disorders? Why or why not?

Buss, D. M., & Schmitt, D. P. (1993). Sexual strategies theory: A contextual evolutionary analysis of human mating. Psychological Review, 100 , 204–232.

Fine, R. (1979). A history of psychoanalysis . New York, NY: Columbia University Press.

Logan, G. D., & Sbrodoff, N. J. (2003). Subitizing and similarity: Toward a pattern-matching theory of enumeration. Psychonomic Bulletin & Review, 10 , 676–682.

Tantam, D., & Huband, N. (2009). Understanding repeated self-injury: A multidisciplinary approach . New York, NY: Palgrave Macmillan.

Williams, P. G. (2004). The psychopathology of self-assessed health: A cognitive approach to health anxiety and hypochondriasis. Cognitive Therapy and Research, 28 , 629–644.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Theoretical Framework
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Theories are formulated to explain, predict, and understand phenomena and, in many cases, to challenge and extend existing knowledge within the limits of critical bounded assumptions or predictions of behavior. The theoretical framework is the structure that can hold or support a theory of a research study. The theoretical framework encompasses not just the theory, but the narrative explanation about how the researcher engages in using the theory and its underlying assumptions to investigate the research problem. It is the structure of your paper that summarizes concepts, ideas, and theories derived from prior research studies and which was synthesized in order to form a conceptual basis for your analysis and interpretation of meaning found within your research.

Abend, Gabriel. "The Meaning of Theory." Sociological Theory 26 (June 2008): 173–199; Kivunja, Charles. "Distinguishing between Theory, Theoretical Framework, and Conceptual Framework: A Systematic Review of Lessons from the Field." International Journal of Higher Education 7 (December 2018): 44-53; Swanson, Richard A. Theory Building in Applied Disciplines . San Francisco, CA: Berrett-Koehler Publishers 2013; Varpio, Lara, Elise Paradis, Sebastian Uijtdehaage, and Meredith Young. "The Distinctions between Theory, Theoretical Framework, and Conceptual Framework." Academic Medicine 95 (July 2020): 989-994.

Importance of Theory and a Theoretical Framework

Theories can be unfamiliar to the beginning researcher because they are rarely applied in high school social studies curriculum and, as a result, can come across as unfamiliar and imprecise when first introduced as part of a writing assignment. However, in their most simplified form, a theory is simply a set of assumptions or predictions about something you think will happen based on existing evidence and that can be tested to see if those outcomes turn out to be true. Of course, it is slightly more deliberate than that, therefore, summarized from Kivunja (2018, p. 46), here are the essential characteristics of a theory.

• It is logical and coherent
• It has clear definitions of terms or variables, and has boundary conditions [i.e., it is not an open-ended statement]
• It has a domain where it applies
• It has clearly described relationships among variables
• It describes, explains, and makes specific predictions
• It comprises of concepts, themes, principles, and constructs
• It must have been based on empirical data [i.e., it is not a guess]
• It must have made claims that are subject to testing, been tested and verified
• It must be clear and concise
• Its assertions or predictions must be different and better than those in existing theories
• Its predictions must be general enough to be applicable to and understood within multiple contexts
• Its assertions or predictions are relevant, and if applied as predicted, will result in the predicted outcome
• The assertions and predictions are not immutable, but subject to revision and improvement as researchers use the theory to make sense of phenomena
• Its concepts and principles explain what is going on and why
• Its concepts and principles are substantive enough to enable us to predict a future

Given these characteristics, a theory can best be understood as the foundation from which you investigate assumptions or predictions derived from previous studies about the research problem, but in a way that leads to new knowledge and understanding as well as, in some cases, discovering how to improve the relevance of the theory itself or to argue that the theory is outdated and a new theory needs to be formulated based on new evidence.

A theoretical framework consists of concepts and, together with their definitions and reference to relevant scholarly literature, existing theory that is used for your particular study. The theoretical framework must demonstrate an understanding of theories and concepts that are relevant to the topic of your research paper and that relate to the broader areas of knowledge being considered.

The theoretical framework is most often not something readily found within the literature . You must review course readings and pertinent research studies for theories and analytic models that are relevant to the research problem you are investigating. The selection of a theory should depend on its appropriateness, ease of application, and explanatory power.

The theoretical framework strengthens the study in the following ways :

• An explicit statement of  theoretical assumptions permits the reader to evaluate them critically.
• The theoretical framework connects the researcher to existing knowledge. Guided by a relevant theory, you are given a basis for your hypotheses and choice of research methods.
• Articulating the theoretical assumptions of a research study forces you to address questions of why and how. It permits you to intellectually transition from simply describing a phenomenon you have observed to generalizing about various aspects of that phenomenon.
• Having a theory helps you identify the limits to those generalizations. A theoretical framework specifies which key variables influence a phenomenon of interest and highlights the need to examine how those key variables might differ and under what circumstances.
• The theoretical framework adds context around the theory itself based on how scholars had previously tested the theory in relation their overall research design [i.e., purpose of the study, methods of collecting data or information, methods of analysis, the time frame in which information is collected, study setting, and the methodological strategy used to conduct the research].

By virtue of its applicative nature, good theory in the social sciences is of value precisely because it fulfills one primary purpose: to explain the meaning, nature, and challenges associated with a phenomenon, often experienced but unexplained in the world in which we live, so that we may use that knowledge and understanding to act in more informed and effective ways.

The Conceptual Framework. College of Education. Alabama State University; Corvellec, Hervé, ed. What is Theory?: Answers from the Social and Cultural Sciences . Stockholm: Copenhagen Business School Press, 2013; Asher, Herbert B. Theory-Building and Data Analysis in the Social Sciences . Knoxville, TN: University of Tennessee Press, 1984; Drafting an Argument. Writing@CSU. Colorado State University; Kivunja, Charles. "Distinguishing between Theory, Theoretical Framework, and Conceptual Framework: A Systematic Review of Lessons from the Field." International Journal of Higher Education 7 (2018): 44-53; Omodan, Bunmi Isaiah. "A Model for Selecting Theoretical Framework through Epistemology of Research Paradigms." African Journal of Inter/Multidisciplinary Studies 4 (2022): 275-285; Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Trochim, William M.K. Philosophy of Research. Research Methods Knowledge Base. 2006; Jarvis, Peter. The Practitioner-Researcher. Developing Theory from Practice . San Francisco, CA: Jossey-Bass, 1999.

Strategies for Developing the Theoretical Framework

I.  Developing the Framework

Here are some strategies to develop of an effective theoretical framework:

• Examine your thesis title and research problem . The research problem anchors your entire study and forms the basis from which you construct your theoretical framework.
• Brainstorm about what you consider to be the key variables in your research . Answer the question, "What factors contribute to the presumed effect?"
• Review related literature to find how scholars have addressed your research problem. Identify the assumptions from which the author(s) addressed the problem.
• List  the constructs and variables that might be relevant to your study. Group these variables into independent and dependent categories.
• Review key social science theories that are introduced to you in your course readings and choose the theory that can best explain the relationships between the key variables in your study [note the Writing Tip on this page].
• Discuss the assumptions or propositions of this theory and point out their relevance to your research.

A theoretical framework is used to limit the scope of the relevant data by focusing on specific variables and defining the specific viewpoint [framework] that the researcher will take in analyzing and interpreting the data to be gathered. It also facilitates the understanding of concepts and variables according to given definitions and builds new knowledge by validating or challenging theoretical assumptions.

II.  Purpose

Think of theories as the conceptual basis for understanding, analyzing, and designing ways to investigate relationships within social systems. To that end, the following roles served by a theory can help guide the development of your framework.

• Means by which new research data can be interpreted and coded for future use,
• Response to new problems that have no previously identified solutions strategy,
• Means for identifying and defining research problems,
• Means for prescribing or evaluating solutions to research problems,
• Ways of discerning certain facts among the accumulated knowledge that are important and which facts are not,
• Means of giving old data new interpretations and new meaning,
• Means by which to identify important new issues and prescribe the most critical research questions that need to be answered to maximize understanding of the issue,
• Means of providing members of a professional discipline with a common language and a frame of reference for defining the boundaries of their profession, and
• Means to guide and inform research so that it can, in turn, guide research efforts and improve professional practice.

Adapted from: Torraco, R. J. “Theory-Building Research Methods.” In Swanson R. A. and E. F. Holton III , editors. Human Resource Development Handbook: Linking Research and Practice . (San Francisco, CA: Berrett-Koehler, 1997): pp. 114-137; Jacard, James and Jacob Jacoby. Theory Construction and Model-Building Skills: A Practical Guide for Social Scientists . New York: Guilford, 2010; Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Sutton, Robert I. and Barry M. Staw. “What Theory is Not.” Administrative Science Quarterly 40 (September 1995): 371-384.

Structure and Writing Style

The theoretical framework may be rooted in a specific theory , in which case, your work is expected to test the validity of that existing theory in relation to specific events, issues, or phenomena. Many social science research papers fit into this rubric. For example, Peripheral Realism Theory, which categorizes perceived differences among nation-states as those that give orders, those that obey, and those that rebel, could be used as a means for understanding conflicted relationships among countries in Africa. A test of this theory could be the following: Does Peripheral Realism Theory help explain intra-state actions, such as, the disputed split between southern and northern Sudan that led to the creation of two nations?

However, you may not always be asked by your professor to test a specific theory in your paper, but to develop your own framework from which your analysis of the research problem is derived . Based upon the above example, it is perhaps easiest to understand the nature and function of a theoretical framework if it is viewed as an answer to two basic questions:

• What is the research problem/question? [e.g., "How should the individual and the state relate during periods of conflict?"]
• Why is your approach a feasible solution? [i.e., justify the application of your choice of a particular theory and explain why alternative constructs were rejected. I could choose instead to test Instrumentalist or Circumstantialists models developed among ethnic conflict theorists that rely upon socio-economic-political factors to explain individual-state relations and to apply this theoretical model to periods of war between nations].

The answers to these questions come from a thorough review of the literature and your course readings [summarized and analyzed in the next section of your paper] and the gaps in the research that emerge from the review process. With this in mind, a complete theoretical framework will likely not emerge until after you have completed a thorough review of the literature .

Just as a research problem in your paper requires contextualization and background information, a theory requires a framework for understanding its application to the topic being investigated. When writing and revising this part of your research paper, keep in mind the following:

• Clearly describe the framework, concepts, models, or specific theories that underpin your study . This includes noting who the key theorists are in the field who have conducted research on the problem you are investigating and, when necessary, the historical context that supports the formulation of that theory. This latter element is particularly important if the theory is relatively unknown or it is borrowed from another discipline.
• Position your theoretical framework within a broader context of related frameworks, concepts, models, or theories . As noted in the example above, there will likely be several concepts, theories, or models that can be used to help develop a framework for understanding the research problem. Therefore, note why the theory you've chosen is the appropriate one.
• The present tense is used when writing about theory. Although the past tense can be used to describe the history of a theory or the role of key theorists, the construction of your theoretical framework is happening now.
• You should make your theoretical assumptions as explicit as possible . Later, your discussion of methodology should be linked back to this theoretical framework.
• Don’t just take what the theory says as a given! Reality is never accurately represented in such a simplistic way; if you imply that it can be, you fundamentally distort a reader's ability to understand the findings that emerge. Given this, always note the limitations of the theoretical framework you've chosen [i.e., what parts of the research problem require further investigation because the theory inadequately explains a certain phenomena].

The Conceptual Framework. College of Education. Alabama State University; Conceptual Framework: What Do You Think is Going On? College of Engineering. University of Michigan; Drafting an Argument. Writing@CSU. Colorado State University; Lynham, Susan A. “The General Method of Theory-Building Research in Applied Disciplines.” Advances in Developing Human Resources 4 (August 2002): 221-241; Tavallaei, Mehdi and Mansor Abu Talib. "A General Perspective on the Role of Theory in Qualitative Research." Journal of International Social Research 3 (Spring 2010); Ravitch, Sharon M. and Matthew Riggan. Reason and Rigor: How Conceptual Frameworks Guide Research . Second edition. Los Angeles, CA: SAGE, 2017; Reyes, Victoria. Demystifying the Journal Article. Inside Higher Education; Trochim, William M.K. Philosophy of Research. Research Methods Knowledge Base. 2006; Weick, Karl E. “The Work of Theorizing.” In Theorizing in Social Science: The Context of Discovery . Richard Swedberg, editor. (Stanford, CA: Stanford University Press, 2014), pp. 177-194.

Writing Tip

Borrowing Theoretical Constructs from Other Disciplines

An increasingly important trend in the social and behavioral sciences is to think about and attempt to understand research problems from an interdisciplinary perspective. One way to do this is to not rely exclusively on the theories developed within your particular discipline, but to think about how an issue might be informed by theories developed in other disciplines. For example, if you are a political science student studying the rhetorical strategies used by female incumbents in state legislature campaigns, theories about the use of language could be derived, not only from political science, but linguistics, communication studies, philosophy, psychology, and, in this particular case, feminist studies. Building theoretical frameworks based on the postulates and hypotheses developed in other disciplinary contexts can be both enlightening and an effective way to be more engaged in the research topic.

CohenMiller, A. S. and P. Elizabeth Pate. "A Model for Developing Interdisciplinary Research Theoretical Frameworks." The Qualitative Researcher 24 (2019): 1211-1226; Frodeman, Robert. The Oxford Handbook of Interdisciplinarity . New York: Oxford University Press, 2010.

Another Writing Tip

Don't Undertheorize!

Do not leave the theory hanging out there in the introduction never to be mentioned again. Undertheorizing weakens your paper. The theoretical framework you describe should guide your study throughout the paper. Be sure to always connect theory to the review of pertinent literature and to explain in the discussion part of your paper how the theoretical framework you chose supports analysis of the research problem or, if appropriate, how the theoretical framework was found to be inadequate in explaining the phenomenon you were investigating. In that case, don't be afraid to propose your own theory based on your findings.

Yet Another Writing Tip

What's a Theory? What's a Hypothesis?

The terms theory and hypothesis are often used interchangeably in newspapers and popular magazines and in non-academic settings. However, the difference between theory and hypothesis in scholarly research is important, particularly when using an experimental design. A theory is a well-established principle that has been developed to explain some aspect of the natural world. Theories arise from repeated observation and testing and incorporates facts, laws, predictions, and tested assumptions that are widely accepted [e.g., rational choice theory; grounded theory; critical race theory].

A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, an experiment designed to look at the relationship between study habits and test anxiety might have a hypothesis that states, "We predict that students with better study habits will suffer less test anxiety." Unless your study is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your research.

The key distinctions are:

• A theory predicts events in a broad, general context;  a hypothesis makes a specific prediction about a specified set of circumstances.
• A theory has been extensively tested and is generally accepted among a set of scholars; a hypothesis is a speculative guess that has yet to be tested.

Cherry, Kendra. Introduction to Research Methods: Theory and Hypothesis. About.com Psychology; Gezae, Michael et al. Welcome Presentation on Hypothesis. Slideshare presentation.

Still Yet Another Writing Tip

Be Prepared to Challenge the Validity of an Existing Theory

Theories are meant to be tested and their underlying assumptions challenged; they are not rigid or intransigent, but are meant to set forth general principles for explaining phenomena or predicting outcomes. Given this, testing theoretical assumptions is an important way that knowledge in any discipline develops and grows. If you're asked to apply an existing theory to a research problem, the analysis will likely include the expectation by your professor that you should offer modifications to the theory based on your research findings.

Indications that theoretical assumptions may need to be modified can include the following:

• Your findings suggest that the theory does not explain or account for current conditions or circumstances or the passage of time,
• The study reveals a finding that is incompatible with what the theory attempts to explain or predict, or
• Your analysis reveals that the theory overly generalizes behaviors or actions without taking into consideration specific factors revealed from your analysis [e.g., factors related to culture, nationality, history, gender, ethnicity, age, geographic location, legal norms or customs , religion, social class, socioeconomic status, etc.].

Philipsen, Kristian. "Theory Building: Using Abductive Search Strategies." In Collaborative Research Design: Working with Business for Meaningful Findings . Per Vagn Freytag and Louise Young, editors. (Singapore: Springer Nature, 2018), pp. 45-71; Shepherd, Dean A. and Roy Suddaby. "Theory Building: A Review and Integration." Journal of Management 43 (2017): 59-86.

• << Previous: The Research Problem/Question
• Next: 5. The Literature Review >>
• Last Updated: Mar 21, 2024 9:59 AM
• URL: https://libguides.usc.edu/writingguide

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Verywell Mind Insights
• 2023 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

What Are Psychological Theories?

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Colleen Tighe 

5 Major Psychological Theories

• Types of Theories

Psychological theories are fact-based ideas that describe a phenomenon of human behavior. These theories are based on a hypothesis , which is backed by evidence. Thus, the two key components of a psychological theory are:

• It must describe a behavior.
• It must make predictions about future behaviors.

The term "theory" is used with surprising frequency in everyday language. It is often used to mean a guess, hunch, or supposition. You may even hear people dismiss certain information because it is "only a theory."

But in the realm of science, a theory is not merely a guess. A theory presents a concept or idea that is testable. Scientists can test a theory through empirical research and gather evidence that supports or refutes it.

As new evidence surfaces and more research is done, a theory may be refined, modified, or even rejected if it does not fit with the latest scientific findings. The overall strength of a scientific theory hinges on its ability to explain diverse phenomena.

Some of the best-known psychological theories stem from the perspectives of various branches within psychology . There are five major types of psychological theories.

Behavioral Theories

Behavioral psychology, also known as behaviorism, is a theory of learning based on the idea that all behaviors are acquired through conditioning.

Advocated by famous psychologists such as John B. Watson and B.F. Skinner , behavioral theories dominated psychology during the early half of the twentieth century. Today, behavioral techniques are still widely used by therapists to help clients learn new skills and behaviors.

Cognitive Theories

Cognitive theories of psychology are focused on internal states, such as motivation, problem-solving, decision-making , thinking, and attention. Such theories strive to explain different mental processes including how the mind processes information and how our thoughts lead to certain emotions and behaviors.

Humanistic Theories

Humanistic psychology theories began to grow in popularity during the 1950s. Some of the major humanist theorists included Carl Rogers and Abraham Maslow .

While earlier theories often focused on abnormal behavior and psychological problems, humanist theories about behavior instead emphasized the basic goodness of human beings.

Psychodynamic Theories

Psychodynamic theories examine the unconscious concepts that shape our emotions, attitudes, and personalities. Psychodynamic approaches seek to understand the root causes of unconscious behavior.

These theories are strongly linked with Sigmund Freud and his followers. The psychodynamic approach is seen in many Freudian claims—for instance, that our adult behaviors have their roots in our childhood experiences and that the personality is made up of three parts: the ID, the ego, and the superego.

Biological Theories

Biological theories in psychology attribute human emotion and behavior to biological causes. For instance, in the nature versus nurture debate on human behavior, the biological perspective would side with nature.

Biological theories are rooted in the ideas of Charles Darwin, who is famous for theorizing about the roles that evolution and genetics play in psychology.

Someone examining a psychological issue from a biological lens might investigate whether there are bodily injuries causing a specific type of behavior or whether the behavior was inherited.

Different Types of Psychological Theories

There are many psychology theories, but most can be categorized as one of four key types.

Developmental Theories

Theories of development provide a framework for thinking about human growth, development, and learning. If you have ever wondered about what motivates human thought and behavior, understanding these theories can provide useful insight into individuals and society.

Developmental theories provide a set of guiding principles and concepts that describe and explain human development. Some developmental theories focus on the formation of a particular quality, such as Kohlberg's theory of moral development. Other developmental theories focus on growth that happens throughout the lifespan, such as  Erikson's theory of psychosocial development .

Grand Theories

Grand theories are those comprehensive ideas often proposed by major thinkers such as Sigmund Freud,  Erik Erikson , and  Jean Piaget . Grand theories of development include psychoanalytic theory,  learning theory , and  cognitive theory .

These theories seek to explain much of human behavior, but are often considered outdated and incomplete in the face of modern research. Psychologists and researchers often use grand theories as a basis for exploration, but consider smaller theories and recent research as well.

Mini-Theories

Mini-theories describe a small, very particular aspect of development. A mini-theory might explain relatively narrow behaviors, such as how self-esteem is formed or early childhood socialization. These theories are often rooted in the ideas established by grand theories, but they do not seek to describe and explain the whole of human behavior and growth.

Emergent Theories

Emergent theories are those that have been created relatively recently. They are often formed by systematically combining various mini-theories. These theories draw on research and ideas from different disciplines but are not yet as broad or far-reaching as grand theories. The  sociocultural theory  proposed by Lev Vygotsky  is a good example of an emergent theory of development.

The Purpose of Psychological Theories

You may find yourself questioning how necessary it is to learn about different psychology theories, especially those that are considered inaccurate or outdated.

However, theories provide valuable information about the history of psychology and the progression of thought on a particular topic. They also allow a deeper understanding of current theories. Each one helps contribute to our knowledge of the human mind and behavior.

By understanding how thinking has progressed, you can get a better idea not only of where psychology has been, but where it might be going in the future.

Studying scientific theories can improve your understanding of how scientific explanations for behavior and other phenomena in the natural world are formed, investigated, and accepted by the scientific community.

While debates continues to rage over hot topics, it is worthwhile to study science and the psychological theories that have emerged from such research, even when what is often revealed might come as a harsh or inconvenient truth.

As Carl Sagan once wrote, "It is far better to grasp the universe as it really is than to persist in delusion, however satisfying and reassuring."

Examples of Psychological Theories

These are a few examples of psychological theories that have maintained relevance, even today.

Maslow's Hierarchy of Needs

Maslow's hierarchy of needs theory is commonly represented by a pyramid, with five different types of human needs listed. From bottom to top, these needs are:

• Physiological : Food, water, shelter
• Safety needs : Security, resources
• Belongingness and love : Intimate relationships
• Esteem needs : Feeling accomplished
• Self-actualization : Living your full potential creatively and spiritually

According to Maslow, these needs represent what humans require to feel fulfilled and lead productive lives. However, one must satisfy these needs from the bottom up, according to Maslow.

For instance, the most basic and most immediate needs are physiological. Once those are met, you can focus on subsequent needs like relationships and self-esteem.

Piaget's Theory of Cognitive Development

Piaget's theory of cognitive development focuses on how children learn and evolve in their understanding of the world around them. According to his theory, there are four stages children go through during cognitive development:

• Sensorimotor stage : This stage lasts from birth to age two. Infants and toddlers learn about the world around them through reflexes, their five senses, and motor responses.
• Preoperational stage : This stage occurs from two to seven years old. Kids start to learn how to think symbolically, but they struggle to understand the perspectives of others.
• Concrete operational stage : This stage lasts from seven to 11 years old. Kids begin to think logically and are capable of reasoning from specific information to form a general principle.
• Formal operational stage : This stage starts at age 12 and continues from there. This is when we begin to think in abstract terms, such as contemplating moral, philosophical, and political issues.

Freud's Psychoanalytic Theory

Still widely discussed today is Freud's famous psychoanalytic theory . In his theory, Freud proposed that a human personality is made up of the id, the ego, and the superego.

The id, according to Freud, is a primal component of personality. It is unconscious and desires pleasure and immediate gratification. For instance, an infant crying because they're hungry is an example of the id at work. In order to get their needs met, they respond to hunger by crying.

The ego is responsible for managing the impulses of the id so they conform to the norms of the outside world. As you age, your ego develops.

For instance, as an adult, you know that crying doesn't get you the same type of attention and care that it did as an infant. So the ego manages the id's primal impulses, while making sure your responses are appropriate for the time and place.

The superego is made up of what we internalize to be right and wrong based on what we've been taught (our conscience is part of the superego). The superego works to make our behavior acceptable and it urges the ego to make decisions based on what's idealistic (not realistic).

A Word From Verywell

Much of what we know about human thought and behavior has emerged thanks to various psychology theories. For example, behavioral theories demonstrated how conditioning can be used to promote learning. By learning more about these theories, you can gain a deeper and richer understanding of psychology's past, present, and future.

Borghi AM, Fini C. Theories and explanations in psychology . Front Psychol. 2019;10:958. doi:10.3389/fpsyg.2019.00958

Schwarzer R, Frensch P, eds. Personality, Human Development, and Culture: International Perspectives on Psychological Science, vol. 2 . Psychology Press.

American Psychological Association. Cognitive theories .

Brady-Amoon P, Keefe-Cooperman K. Psychology, counseling psychology, and professional counseling: Shared roots, challenges, and opportunities . Eur J Couns Psychol. 2017;6(1). doi:10.5964/ejcop.v6i1.105

American Psychological Association. Psychodynamic approach .

Giacolini T, Sabatello U. Psychoanalysis and affective neuroscience. The motivational/emotional system of aggression in human relations . Front Psychol . 2019;9. doi:10.3389/fpsyg.2018.02475

D’Hooge R, Balschun D. Biological psychology . In: Runehov ALC, Oviedo L, eds. Encyclopedia of Sciences and Religions . 2013:231-239. doi:10.1007/978-1-4020-8265-8_240

Walrath R. Kohlberg’s Theory of Moral Development In: Goldstein S, Naglieri JA, eds. Encyclopedia of Child Behavior and Development . Springer.

Gilleard C, Higgs P. Connecting life span development with the sociology of the life course: A new direction . Sociology . 2016;50(2):301-315. doi:10.1177/0038038515577906

Cvencek D, Greenwald A, Meltzoff A. Implicit measures for preschool children confirm self-esteem’s role in maintaining a balanced identity . J Exp Psychol . 2016(62):50-57. doi:10.1016/j.jesp.2015.09.015

Benson J, Haith M, eds. Social and Emotional Development in Infancy and Early Childhood . Elsevier.

Sagan C. The Demon-Haunted World: Science as a Candle in the Dark . Random House.

Taormina RJ, Gao JH. Maslow and the motivation hierarchy: Measuring satisfaction of the needs . American J Psychol. 2013;126(2):155-177. doi:10.5406/amerjpsyc.126.2.0155

Rabindran, Madanagopal D. Piaget’s theory and stages of cognitive development- An overview . SJAMS. 2020;8(9):2152-2157. doi:10.36347/sjams.2020.v08i09.034

Boag S.  Ego, drives, and the dynamics of internal objects.   Front Psychol.  2014;5:666. doi:10.3389/fpsyg.2014.00666

McComas WF. The Language of Science Education . Springer Science & Business Media.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• SAGE Open Med

Grounded theory research: A design framework for novice researchers

Ylona chun tie.

1 Nursing and Midwifery, College of Healthcare Sciences, James Cook University, Townsville, QLD, Australia

Melanie Birks

Karen francis.

2 College of Health and Medicine, University of Tasmania, Australia, Hobart, TAS, Australia

Background:

Grounded theory is a well-known methodology employed in many research studies. Qualitative and quantitative data generation techniques can be used in a grounded theory study. Grounded theory sets out to discover or construct theory from data, systematically obtained and analysed using comparative analysis. While grounded theory is inherently flexible, it is a complex methodology. Thus, novice researchers strive to understand the discourse and the practical application of grounded theory concepts and processes.

The aim of this article is to provide a contemporary research framework suitable to inform a grounded theory study.

This article provides an overview of grounded theory illustrated through a graphic representation of the processes and methods employed in conducting research using this methodology. The framework is presented as a diagrammatic representation of a research design and acts as a visual guide for the novice grounded theory researcher.

Discussion:

As grounded theory is not a linear process, the framework illustrates the interplay between the essential grounded theory methods and iterative and comparative actions involved. Each of the essential methods and processes that underpin grounded theory are defined in this article.

Conclusion:

Rather than an engagement in philosophical discussion or a debate of the different genres that can be used in grounded theory, this article illustrates how a framework for a research study design can be used to guide and inform the novice nurse researcher undertaking a study using grounded theory. Research findings and recommendations can contribute to policy or knowledge development, service provision and can reform thinking to initiate change in the substantive area of inquiry.

Introduction

The aim of all research is to advance, refine and expand a body of knowledge, establish facts and/or reach new conclusions using systematic inquiry and disciplined methods. 1 The research design is the plan or strategy researchers use to answer the research question, which is underpinned by philosophy, methodology and methods. 2 Birks 3 defines philosophy as ‘a view of the world encompassing the questions and mechanisms for finding answers that inform that view’ (p. 18). Researchers reflect their philosophical beliefs and interpretations of the world prior to commencing research. Methodology is the research design that shapes the selection of, and use of, particular data generation and analysis methods to answer the research question. 4 While a distinction between positivist research and interpretivist research occurs at the paradigm level, each methodology has explicit criteria for the collection, analysis and interpretation of data. 2 Grounded theory (GT) is a structured, yet flexible methodology. This methodology is appropriate when little is known about a phenomenon; the aim being to produce or construct an explanatory theory that uncovers a process inherent to the substantive area of inquiry. 5 – 7 One of the defining characteristics of GT is that it aims to generate theory that is grounded in the data. The following section provides an overview of GT – the history, main genres and essential methods and processes employed in the conduct of a GT study. This summary provides a foundation for a framework to demonstrate the interplay between the methods and processes inherent in a GT study as presented in the sections that follow.

Glaser and Strauss are recognised as the founders of grounded theory. Strauss was conversant in symbolic interactionism and Glaser in descriptive statistics. 8 – 10 Glaser and Strauss originally worked together in a study examining the experience of terminally ill patients who had differing knowledge of their health status. Some of these suspected they were dying and tried to confirm or disconfirm their suspicions. Others tried to understand by interpreting treatment by care providers and family members. Glaser and Strauss examined how the patients dealt with the knowledge they were dying and the reactions of healthcare staff caring for these patients. Throughout this collaboration, Glaser and Strauss questioned the appropriateness of using a scientific method of verification for this study. During this investigation, they developed the constant comparative method, a key element of grounded theory, while generating a theory of dying first described in Awareness of Dying (1965). The constant comparative method is deemed an original way of organising and analysing qualitative data.

Glaser and Strauss subsequently went on to write The Discovery of Grounded Theory: Strategies for Qualitative Research (1967). This seminal work explained how theory could be generated from data inductively. This process challenged the traditional method of testing or refining theory through deductive testing. Grounded theory provided an outlook that questioned the view of the time that quantitative methodology is the only valid, unbiased way to determine truths about the world. 11 Glaser and Strauss 5 challenged the belief that qualitative research lacked rigour and detailed the method of comparative analysis that enables the generation of theory. After publishing The Discovery of Grounded Theory , Strauss and Glaser went on to write independently, expressing divergent viewpoints in the application of grounded theory methods.

Glaser produced his book Theoretical Sensitivity (1978) and Strauss went on to publish Qualitative Analysis for Social Scientists (1987). Strauss and Corbin’s 12 publication Basics of Qualitative Research: Grounded Theory Procedures and Techniques resulted in a rebuttal by Glaser 13 over their application of grounded theory methods. However, philosophical perspectives have changed since Glaser’s positivist version and Strauss and Corbin’s post-positivism stance. 14 Grounded theory has since seen the emergence of additional philosophical perspectives that have influenced a change in methodological development over time. 15

Subsequent generations of grounded theorists have positioned themselves along a philosophical continuum, from Strauss and Corbin’s 12 theoretical perspective of symbolic interactionism, through to Charmaz’s 16 constructivist perspective. However, understanding how to position oneself philosophically can challenge novice researchers. Birks and Mills 6 provide a contemporary understanding of GT in their book Grounded theory: A Practical Guide. These Australian researchers have written in a way that appeals to the novice researcher. It is the contemporary writing, the way Birks and Mills present a non-partisan approach to GT that support the novice researcher to understand the philosophical and methodological concepts integral in conducting research. The development of GT is important to understand prior to selecting an approach that aligns with the researcher’s philosophical position and the purpose of the research study. As the research progresses, seminal texts are referred back to time and again as understanding of concepts increases, much like the iterative processes inherent in the conduct of a GT study.

Genres: traditional, evolved and constructivist grounded theory

Grounded theory has several distinct methodological genres: traditional GT associated with Glaser; evolved GT associated with Strauss, Corbin and Clarke; and constructivist GT associated with Charmaz. 6 , 17 Each variant is an extension and development of the original GT by Glaser and Strauss. The first of these genres is known as traditional or classic GT. Glaser 18 acknowledged that the goal of traditional GT is to generate a conceptual theory that accounts for a pattern of behaviour that is relevant and problematic for those involved. The second genre, evolved GT, is founded on symbolic interactionism and stems from work associated with Strauss, Corbin and Clarke. Symbolic interactionism is a sociological perspective that relies on the symbolic meaning people ascribe to the processes of social interaction. Symbolic interactionism addresses the subjective meaning people place on objects, behaviours or events based on what they believe is true. 19 , 20 Constructivist GT, the third genre developed and explicated by Charmaz, a symbolic interactionist, has its roots in constructivism. 8 , 16 Constructivist GT’s methodological underpinnings focus on how participants’ construct meaning in relation to the area of inquiry. 16 A constructivist co-constructs experience and meanings with participants. 21 While there are commonalities across all genres of GT, there are factors that distinguish differences between the approaches including the philosophical position of the researcher; the use of literature; and the approach to coding, analysis and theory development. Following on from Glaser and Strauss, several versions of GT have ensued.

Grounded theory represents both a method of inquiry and a resultant product of that inquiry. 7 , 22 Glaser and Holton 23 define GT as ‘a set of integrated conceptual hypotheses systematically generated to produce an inductive theory about a substantive area’ (p. 43). Strauss and Corbin 24 define GT as ‘theory that was derived from data, systematically gathered and analysed through the research process’ (p. 12). The researcher ‘begins with an area of study and allows the theory to emerge from the data’ (p. 12). Charmaz 16 defines GT as ‘a method of conducting qualitative research that focuses on creating conceptual frameworks or theories through building inductive analysis from the data’ (p. 187). However, Birks and Mills 6 refer to GT as a process by which theory is generated from the analysis of data. Theory is not discovered; rather, theory is constructed by the researcher who views the world through their own particular lens.

Research process

Before commencing any research study, the researcher must have a solid understanding of the research process. A well-developed outline of the study and an understanding of the important considerations in designing and undertaking a GT study are essential if the goals of the research are to be achieved. While it is important to have an understanding of how a methodology has developed, in order to move forward with research, a novice can align with a grounded theorist and follow an approach to GT. Using a framework to inform a research design can be a useful modus operandi.

The following section provides insight into the process of undertaking a GT research study. Figure 1 is a framework that summarises the interplay and movement between methods and processes that underpin the generation of a GT. As can be seen from this framework, and as detailed in the discussion that follows, the process of doing a GT research study is not linear, rather it is iterative and recursive.

Research design framework: summary of the interplay between the essential grounded theory methods and processes.

Grounded theory research involves the meticulous application of specific methods and processes. Methods are ‘systematic modes, procedures or tools used for collection and analysis of data’. 25 While GT studies can commence with a variety of sampling techniques, many commence with purposive sampling, followed by concurrent data generation and/or collection and data analysis, through various stages of coding, undertaken in conjunction with constant comparative analysis, theoretical sampling and memoing. Theoretical sampling is employed until theoretical saturation is reached. These methods and processes create an unfolding, iterative system of actions and interactions inherent in GT. 6 , 16 The methods interconnect and inform the recurrent elements in the research process as shown by the directional flow of the arrows and the encompassing brackets in Figure 1 . The framework denotes the process is both iterative and dynamic and is not one directional. Grounded theory methods are discussed in the following section.

Purposive sampling

As presented in Figure 1 , initial purposive sampling directs the collection and/or generation of data. Researchers purposively select participants and/or data sources that can answer the research question. 5 , 7 , 16 , 21 Concurrent data generation and/or data collection and analysis is fundamental to GT research design. 6 The researcher collects, codes and analyses this initial data before further data collection/generation is undertaken. Purposeful sampling provides the initial data that the researcher analyses. As will be discussed, theoretical sampling then commences from the codes and categories developed from the first data set. Theoretical sampling is used to identify and follow clues from the analysis, fill gaps, clarify uncertainties, check hunches and test interpretations as the study progresses.

Constant comparative analysis

Constant comparative analysis is an analytical process used in GT for coding and category development. This process commences with the first data generated or collected and pervades the research process as presented in Figure 1 . Incidents are identified in the data and coded. 6 The initial stage of analysis compares incident to incident in each code. Initial codes are then compared to other codes. Codes are then collapsed into categories. This process means the researcher will compare incidents in a category with previous incidents, in both the same and different categories. 5 Future codes are compared and categories are compared with other categories. New data is then compared with data obtained earlier during the analysis phases. This iterative process involves inductive and deductive thinking. 16 Inductive, deductive and abductive reasoning can also be used in data analysis. 26

Constant comparative analysis generates increasingly more abstract concepts and theories through inductive processes. 16 In addition, abduction, defined as ‘a form of reasoning that begins with an examination of the data and the formation of a number of hypotheses that are then proved or disproved during the process of analysis … aids inductive conceptualization’. 6 Theoretical sampling coupled with constant comparative analysis raises the conceptual levels of data analysis and directs ongoing data collection or generation. 6

The constant comparative technique is used to find consistencies and differences, with the aim of continually refining concepts and theoretically relevant categories. This continual comparative iterative process that encompasses GT research sets it apart from a purely descriptive analysis. 8

Memo writing is an analytic process considered essential ‘in ensuring quality in grounded theory’. 6 Stern 27 offers the analogy that if data are the building blocks of the developing theory, then memos are the ‘mortar’ (p. 119). Memos are the storehouse of ideas generated and documented through interacting with data. 28 Thus, memos are reflective interpretive pieces that build a historic audit trail to document ideas, events and the thought processes inherent in the research process and developing thinking of the analyst. 6 Memos provide detailed records of the researchers’ thoughts, feelings and intuitive contemplations. 6

Lempert 29 considers memo writing crucial as memos prompt researchers to analyse and code data and develop codes into categories early in the coding process. Memos detail why and how decisions made related to sampling, coding, collapsing of codes, making of new codes, separating codes, producing a category and identifying relationships abstracted to a higher level of analysis. 6 Thus, memos are informal analytic notes about the data and the theoretical connections between categories. 23 Memoing is an ongoing activity that builds intellectual assets, fosters analytic momentum and informs the GT findings. 6 , 10

Generating/collecting data

A hallmark of GT is concurrent data generation/collection and analysis. In GT, researchers may utilise both qualitative and quantitative data as espoused by Glaser’s dictum; ‘all is data’. 30 While interviews are a common method of generating data, data sources can include focus groups, questionnaires, surveys, transcripts, letters, government reports, documents, grey literature, music, artefacts, videos, blogs and memos. 9 Elicited data are produced by participants in response to, or directed by, the researcher whereas extant data includes data that is already available such as documents and published literature. 6 , 31 While this is one interpretation of how elicited data are generated, other approaches to grounded theory recognise the agency of participants in the co-construction of data with the researcher. The relationship the researcher has with the data, how it is generated and collected, will determine the value it contributes to the development of the final GT. 6 The significance of this relationship extends into data analysis conducted by the researcher through the various stages of coding.

Coding is an analytical process used to identify concepts, similarities and conceptual reoccurrences in data. Coding is the pivotal link between collecting or generating data and developing a theory that explains the data. Charmaz 10 posits,

codes rely on interaction between researchers and their data. Codes consist of short labels that we construct as we interact with the data. Something kinaesthetic occurs when we are coding; we are mentally and physically active in the process. (p. 5)

In GT, coding can be categorised into iterative phases. Traditional, evolved and constructivist GT genres use different terminology to explain each coding phase ( Table 1 ).

Comparison of coding terminology in traditional, evolved and constructivist grounded theory.

Adapted from Birks and Mills. 6

Coding terminology in evolved GT refers to open (a procedure for developing categories of information), axial (an advanced procedure for interconnecting the categories) and selective coding (procedure for building a storyline from core codes that connects the categories), producing a discursive set of theoretical propositions. 6 , 12 , 32 Constructivist grounded theorists refer to initial, focused and theoretical coding. 9 Birks and Mills 6 use the terms initial, intermediate and advanced coding that link to low, medium and high-level conceptual analysis and development. The coding terms devised by Birks and Mills 6 were used for Figure 1 ; however, these can be altered to reflect the coding terminology used in the respective GT genres selected by the researcher.

Initial coding

Initial coding of data is the preliminary step in GT data analysis. 6 , 9 The purpose of initial coding is to start the process of fracturing the data to compare incident to incident and to look for similarities and differences in beginning patterns in the data. In initial coding, the researcher inductively generates as many codes as possible from early data. 16 Important words or groups of words are identified and labelled. In GT, codes identify social and psychological processes and actions as opposed to themes. Charmaz 16 emphasises keeping codes as similar to the data as possible and advocates embedding actions in the codes in an iterative coding process. Saldaña 33 agrees that codes that denote action, which he calls process codes, can be used interchangeably with gerunds (verbs ending in ing ). In vivo codes are often verbatim quotes from the participants’ words and are often used as the labels to capture the participant’s words as representative of a broader concept or process in the data. 6 Table 1 reflects variation in the terminology of codes used by grounded theorists.

Initial coding categorises and assigns meaning to the data, comparing incident-to-incident, labelling beginning patterns and beginning to look for comparisons between the codes. During initial coding, it is important to ask ‘what is this data a study of’. 18 What does the data assume, ‘suggest’ or ‘pronounce’ and ‘from whose point of view’ does this data come, whom does it represent or whose thoughts are they?. 16 What collectively might it represent? The process of documenting reactions, emotions and related actions enables researchers to explore, challenge and intensify their sensitivity to the data. 34 Early coding assists the researcher to identify the direction for further data gathering. After initial analysis, theoretical sampling is employed to direct collection of additional data that will inform the ‘developing theory’. 9 Initial coding advances into intermediate coding once categories begin to develop.

Theoretical sampling

The purpose of theoretical sampling is to allow the researcher to follow leads in the data by sampling new participants or material that provides relevant information. As depicted in Figure 1 , theoretical sampling is central to GT design, aids the evolving theory 5 , 7 , 16 and ensures the final developed theory is grounded in the data. 9 Theoretical sampling in GT is for the development of a theoretical category, as opposed to sampling for population representation. 10 Novice researchers need to acknowledge this difference if they are to achieve congruence within the methodology. Birks and Mills 6 define theoretical sampling as ‘the process of identifying and pursuing clues that arise during analysis in a grounded theory study’ (p. 68). During this process, additional information is sought to saturate categories under development. The analysis identifies relationships, highlights gaps in the existing data set and may reveal insight into what is not yet known. The exemplars in Box 1 highlight how theoretical sampling led to the inclusion of further data.

Examples of theoretical sampling.

Thus, theoretical sampling is used to focus and generate data to feed the iterative process of continual comparative analysis of the data. 6

Intermediate coding

Intermediate coding, identifying a core category, theoretical data saturation, constant comparative analysis, theoretical sensitivity and memoing occur in the next phase of the GT process. 6 Intermediate coding builds on the initial coding phase. Where initial coding fractures the data, intermediate coding begins to transform basic data into more abstract concepts allowing the theory to emerge from the data. During this analytic stage, a process of reviewing categories and identifying which ones, if any, can be subsumed beneath other categories occurs and the properties or dimension of the developed categories are refined. Properties refer to the characteristics that are common to all the concepts in the category and dimensions are the variations of a property. 37

At this stage, a core category starts to become evident as developed categories form around a core concept; relationships are identified between categories and the analysis is refined. Birks and Mills 6 affirm that diagramming can aid analysis in the intermediate coding phase. Grounded theorists interact closely with the data during this phase, continually reassessing meaning to ascertain ‘what is really going on’ in the data. 30 Theoretical saturation ensues when new data analysis does not provide additional material to existing theoretical categories, and the categories are sufficiently explained. 6

Advanced coding

Birks and Mills 6 described advanced coding as the ‘techniques used to facilitate integration of the final grounded theory’ (p. 177). These authors promote storyline technique (described in the following section) and theoretical coding as strategies for advancing analysis and theoretical integration. Advanced coding is essential to produce a theory that is grounded in the data and has explanatory power. 6 During the advanced coding phase, concepts that reach the stage of categories will be abstract, representing stories of many, reduced into highly conceptual terms. The findings are presented as a set of interrelated concepts as opposed to presenting themes. 28 Explanatory statements detail the relationships between categories and the central core category. 28

Storyline is a tool that can be used for theoretical integration. Birks and Mills 6 define storyline as ‘a strategy for facilitating integration, construction, formulation, and presentation of research findings through the production of a coherent grounded theory’ (p. 180). Storyline technique is first proposed with limited attention in Basics of Qualitative Research by Strauss and Corbin 12 and further developed by Birks et al. 38 as a tool for theoretical integration. The storyline is the conceptualisation of the core category. 6 This procedure builds a story that connects the categories and produces a discursive set of theoretical propositions. 24 Birks and Mills 6 contend that storyline can be ‘used to produce a comprehensive rendering of your grounded theory’ (p. 118). Birks et al. 38 had earlier concluded, ‘storyline enhances the development, presentation and comprehension of the outcomes of grounded theory research’ (p. 405). Once the storyline is developed, the GT is finalised using theoretical codes that ‘provide a framework for enhancing the explanatory power of the storyline and its potential as theory’. 6 Thus, storyline is the explication of the theory.

Theoretical coding occurs as the final culminating stage towards achieving a GT. 39 , 40 The purpose of theoretical coding is to integrate the substantive theory. 41 Saldaña 40 states, ‘theoretical coding integrates and synthesises the categories derived from coding and analysis to now create a theory’ (p. 224). Initial coding fractures the data while theoretical codes ‘weave the fractured story back together again into an organized whole theory’. 18 Advanced coding that integrates extant theory adds further explanatory power to the findings. 6 The examples in Box 2 describe the use of storyline as a technique.

Writing the storyline.

Theoretical sensitivity

As presented in Figure 1 , theoretical sensitivity encompasses the entire research process. Glaser and Strauss 5 initially described the term theoretical sensitivity in The Discovery of Grounded Theory. Theoretical sensitivity is the ability to know when you identify a data segment that is important to your theory. While Strauss and Corbin 12 describe theoretical sensitivity as the insight into what is meaningful and of significance in the data for theory development, Birks and Mills 6 define theoretical sensitivity as ‘the ability to recognise and extract from the data elements that have relevance for the emerging theory’ (p. 181). Conducting GT research requires a balance between keeping an open mind and the ability to identify elements of theoretical significance during data generation and/or collection and data analysis. 6

Several analytic tools and techniques can be used to enhance theoretical sensitivity and increase the grounded theorist’s sensitivity to theoretical constructs in the data. 28 Birks and Mills 6 state, ‘as a grounded theorist becomes immersed in the data, their level of theoretical sensitivity to analytic possibilities will increase’ (p. 12). Developing sensitivity as a grounded theorist and the application of theoretical sensitivity throughout the research process allows the analytical focus to be directed towards theory development and ultimately result in an integrated and abstract GT. 6 The example in Box 3 highlights how analytic tools are employed to increase theoretical sensitivity.

Theoretical sensitivity.

The grounded theory

The meticulous application of essential GT methods refines the analysis resulting in the generation of an integrated, comprehensive GT that explains a process relating to a particular phenomenon. 6 The results of a GT study are communicated as a set of concepts, related to each other in an interrelated whole, and expressed in the production of a substantive theory. 5 , 7 , 16 A substantive theory is a theoretical interpretation or explanation of a studied phenomenon 6 , 17 Thus, the hallmark of grounded theory is the generation of theory ‘abstracted from, or grounded in, data generated and collected by the researcher’. 6 However, to ensure quality in research requires the application of rigour throughout the research process.

Quality and rigour

The quality of a grounded theory can be related to three distinct areas underpinned by (1) the researcher’s expertise, knowledge and research skills; (2) methodological congruence with the research question; and (3) procedural precision in the use of methods. 6 Methodological congruence is substantiated when the philosophical position of the researcher is congruent with the research question and the methodological approach selected. 6 Data collection or generation and analytical conceptualisation need to be rigorous throughout the research process to secure excellence in the final grounded theory. 44

Procedural precision requires careful attention to maintaining a detailed audit trail, data management strategies and demonstrable procedural logic recorded using memos. 6 Organisation and management of research data, memos and literature can be assisted using software programs such as NVivo. An audit trail of decision-making, changes in the direction of the research and the rationale for decisions made are essential to ensure rigour in the final grounded theory. 6

This article offers a framework to assist novice researchers visualise the iterative processes that underpin a GT study. The fundamental process and methods used to generate an integrated grounded theory have been described. Novice researchers can adapt the framework presented to inform and guide the design of a GT study. This framework provides a useful guide to visualise the interplay between the methods and processes inherent in conducting GT. Research conducted ethically and with meticulous attention to process will ensure quality research outcomes that have relevance at the practice level.

Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research Research Tools and Apps

Theoretical Research: Definition, Methods + Examples

Research is the careful study of a particular research problem or concern using the scientific method. A theory is essential for any research project because it gives it direction and helps prove or disprove something. Theoretical basis helps us figure out how things work and why we do certain things.

Theoretical research lets you examine and discuss a research object using philosophical ideas and abstract theoretical structures.

In theoretical research, you can’t look at the research object directly. With the help of research literature, your research aims to define and sketch out the chosen topic’s conceptual models, explanations, and structures.

LEARN ABOUT: Research Process Steps

This blog will cover theoretical research and why it is essential. In addition to that, we are going to go over some examples.

What is the theoretical research?

Theoretical research is the systematic examination of a set of beliefs and assumptions.

It aims to learn more about a subject and help us understand it better. The information gathered in this way is not used for anything in particular because this kind of research aims to learn more.

All professionals, like biologists, chemists, engineers, architects, philosophers, writers, sociologists, historians, etc., can do theoretical research. No matter what field you work in, theoretical research is the foundation for new ideas.

It tries to answer basic questions about people, which is why this kind of research is used in every field of knowledge.

For example , a researcher starts with the idea that we need to understand the world around us. To do this, he begins with a hypothesis and tests it through experiments that will help him develop new ideas. 

What is the theoretical framework?

A theoretical framework is a critical component in research that provides a structured foundation for investigating a specific topic or problem. It encompasses a set of interconnected theories, existing theories, and concepts that guide the entire research process. 

The theoretical framework introduces a comprehensive understanding of the subject matter. Also, the theoretical framework strengthens the research’s validity and specifies the key elements that will be explored. Furthermore, it connects different ideas and theories, forming a cohesive structure that underpins the research endeavor.

A complete theoretical framework consists of a network of theories, existing theories, and concepts that collectively shape the direction of a research study. 

The theoretical framework is the fundamental principle that will be explored, strengthens the research’s credibility by aligning it with established knowledge, specifies the variables under investigation, and connects different aspects of the research to create a unified approach.

Theoretical frameworks are the intellectual scaffolding upon which the research is constructed. It is the lens through which researchers view their subject, guiding their choice of methodologies, data collection, analysis, and interpretation. By incorporating existing theory, and established concepts, a theoretical framework not only grounds the research but also provides a coherent roadmap for exploring the intricacies of the chosen topic.

Benefits of theoretical research

Theoretical research yields a wealth of benefits across various fields, from social sciences to human resource development and political science. Here’s a breakdown of these benefits while incorporating the requested topics:

Predictive power

Theoretical models are the cornerstone of theoretical research. They grant us predictive power, enabling us to forecast intricate behaviors within complex systems, like societal interactions. In political science, for instance, a theoretical model helps anticipate potential outcomes of policy changes.

Understanding human behavior

Drawing from key social science theories, it assists us in deciphering human behavior and societal dynamics. For instance, in the context of human resource development, theories related to motivation and psychology provide insights into how to effectively manage a diverse workforce.

Optimizing workforce

In the realm of human resource development, insights gleaned from theoretical research, along with the research methods knowledge base, help create targeted training programs. By understanding various learning methodologies and psychological factors, organizations can optimize workforce training for better results.

Building on foundations

It doesn’t exist in isolation; it builds upon existing theories. For instance, within the human resource development handbook, theoretical research expands established concepts, refining their applicability to contemporary organizational challenges.

Ethical policy formulation

Within political science, theoretical research isn’t confined to governance structures. It extends to ethical considerations, aiding policymakers in creating policies that balance the collective good with individual rights, ensuring just and fair governance. 

Rigorous investigations

Theoretical research underscores the importance of research methods knowledge base. This knowledge equips researchers in theory-building research methods and other fields to design robust research methodologies, yielding accurate data and credible insights.

Long-term impact

Theoretical research leaves a lasting impact. The theoretical models and insights from key social science theories provide enduring frameworks for subsequent research, contributing to the cumulative growth of knowledge in these fields.

Innovation and practical applications

It doesn’t merely remain theoretical. It inspires innovation and practical applications. By merging insights from diverse theories and fields, practitioners in human resource development devise innovative strategies to foster employee growth and well-being.

Theoretical research method

Researchers follow so many methods when doing research. There are two types of theoretical research methods.

• Scientific methods
• Social science method 

Let’s explore them below:

Scientific method

Scientific methods have some important points that you should know. Let’s figure them out below:

• Observation: Any part you want to explain can be found through observation. It helps define the area of research.
• Hypothesis: The hypothesis is the idea put into words, which helps us figure out what we see.
• Experimentation: Hypotheses are tested through experiments to see if they are true. These experiments are different for each research.
• Theory: When we create a theory, we do it because we believe it will explain hypotheses of higher probability.
• Conclusions: Conclusions are the learnings we derive from our investigation.

Social science methods

There are different methods for social science theoretical research. It consists of polls, documentation, and statistical analysis.

• Polls: It is a process whereby the researcher uses a topic-specific questionnaire to gather data. No changes are made to the environment or the phenomenon where the polls are conducted to get the most accurate results. QuestionPro live polls are a great way to get live audiences involved and engaged.
• Documentation: Documentation is a helpful and valuable technique that helps the researcher learn more about the subject. It means visiting libraries or other specialized places, like documentation centers, to look at the existing bibliography. With the documentation, you can find out what came before the investigated topic and what other investigations have found. This step is important because it shows whether or not similar investigations have been done before and what the results were.
• Statistic analysis : Statistics is a branch of math that looks at random events and differences. It follows the rules that are established by probability. It’s used a lot in sociology and language research. 

Examples of theoretical research

We talked about theoretical study methods in the previous part. We’ll give you some examples to help you understand it better.

Example 1: Theoretical research into the health benefits of hemp

The plant’s active principles are extracted and evaluated, and by studying their components, it is possible to determine what they contain and whether they can potentially serve as a medication.

Example 2: Linguistics research

Investigate to determine how many people in the Basque Country speak Basque. Surveys can be used to determine the number of native Basque speakers and those who speak Basque as a second language.

Example 3: Philosophical research

Research politics and ethics as they are presented in the writings of Hanna Arendt from a theoretical perspective.

LEARN ABOUT: 12 Best Tools for Researchers

From our above discussion, we learned about theoretical research and its methods and gave some examples. It explains things and leads to more knowledge for the sake of knowledge. This kind of research tries to find out more about a thing or an idea, but the results may take time to be helpful in the real world. 

This research is sometimes called basic research. Theoretical research is an important process that gives researchers valuable data with insight.

QuestionPro is a strong platform for managing your data. You can conduct simple surveys to more complex research using QuestionPro survey software.

At QuestionPro, we give researchers tools for collecting data, such as our survey software and a library of insights for any long-term study. Contact our expert team to find out more about it.

FREE TRIAL         LEARN MORE

MORE LIKE THIS

20 Best Website Optimization Tools to Improve Your Website

Mar 22, 2024

15 Best Digital Customer Experience Software of 2024

15 Best Product Experience Software of 2024

15 Best Customer Analytics Software of 2024 | QuestionPro

Mar 21, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, June 22). Types of Research Designs Compared | Guide & Examples. Scribbr. Retrieved March 20, 2024, from https://www.scribbr.com/methodology/types-of-research/

Is this article helpful?

Shona McCombes

Other students also liked, what is a research design | types, guide & examples, qualitative vs. quantitative research | differences, examples & methods, what is a research methodology | steps & tips, unlimited academic ai-proofreading.

✔ Document error-free in 5minutes ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

• Privacy Policy

Buy Me a Coffee

Home » Research – Types, Methods and Examples

Research – Types, Methods and Examples

Table of Contents

Definition:

Research refers to the process of investigating a particular topic or question in order to discover new information , develop new insights, or confirm or refute existing knowledge. It involves a systematic and rigorous approach to collecting, analyzing, and interpreting data, and requires careful planning and attention to detail.

History of Research

The history of research can be traced back to ancient times when early humans observed and experimented with the natural world around them. Over time, research evolved and became more systematic as people sought to better understand the world and solve problems.

In ancient civilizations such as those in Greece, Egypt, and China, scholars pursued knowledge through observation, experimentation, and the development of theories. They explored various fields, including medicine, astronomy, and mathematics.

During the Middle Ages, research was often conducted by religious scholars who sought to reconcile scientific discoveries with their faith. The Renaissance brought about a renewed interest in science and the scientific method, and the Enlightenment period marked a major shift towards empirical observation and experimentation as the primary means of acquiring knowledge.

The 19th and 20th centuries saw significant advancements in research, with the development of new scientific disciplines and fields such as psychology, sociology, and computer science. Advances in technology and communication also greatly facilitated research efforts.

Today, research is conducted in a wide range of fields and is a critical component of many industries, including healthcare, technology, and academia. The process of research continues to evolve as new methods and technologies emerge, but the fundamental principles of observation, experimentation, and hypothesis testing remain at its core.

Types of Research

Types of Research are as follows:

• Applied Research : This type of research aims to solve practical problems or answer specific questions, often in a real-world context.
• Basic Research : This type of research aims to increase our understanding of a phenomenon or process, often without immediate practical applications.
• Experimental Research : This type of research involves manipulating one or more variables to determine their effects on another variable, while controlling all other variables.
• Descriptive Research : This type of research aims to describe and measure phenomena or characteristics, without attempting to manipulate or control any variables.
• Correlational Research: This type of research examines the relationships between two or more variables, without manipulating any variables.
• Qualitative Research : This type of research focuses on exploring and understanding the meaning and experience of individuals or groups, often through methods such as interviews, focus groups, and observation.
• Quantitative Research : This type of research uses numerical data and statistical analysis to draw conclusions about phenomena or populations.
• Action Research: This type of research is often used in education, healthcare, and other fields, and involves collaborating with practitioners or participants to identify and solve problems in real-world settings.
• Mixed Methods Research : This type of research combines both quantitative and qualitative research methods to gain a more comprehensive understanding of a phenomenon or problem.
• Case Study Research: This type of research involves in-depth examination of a specific individual, group, or situation, often using multiple data sources.
• Longitudinal Research: This type of research follows a group of individuals over an extended period of time, often to study changes in behavior, attitudes, or health outcomes.
• Cross-Sectional Research : This type of research examines a population at a single point in time, often to study differences or similarities among individuals or groups.
• Survey Research: This type of research uses questionnaires or interviews to gather information from a sample of individuals about their attitudes, beliefs, behaviors, or experiences.
• Ethnographic Research : This type of research involves immersion in a cultural group or community to understand their way of life, beliefs, values, and practices.
• Historical Research : This type of research investigates events or phenomena from the past using primary sources, such as archival records, newspapers, and diaries.
• Content Analysis Research : This type of research involves analyzing written, spoken, or visual material to identify patterns, themes, or messages.
• Participatory Research : This type of research involves collaboration between researchers and participants throughout the research process, often to promote empowerment, social justice, or community development.
• Comparative Research: This type of research compares two or more groups or phenomena to identify similarities and differences, often across different countries or cultures.
• Exploratory Research : This type of research is used to gain a preliminary understanding of a topic or phenomenon, often in the absence of prior research or theories.
• Explanatory Research: This type of research aims to identify the causes or reasons behind a particular phenomenon, often through the testing of theories or hypotheses.
• Evaluative Research: This type of research assesses the effectiveness or impact of an intervention, program, or policy, often through the use of outcome measures.
• Simulation Research : This type of research involves creating a model or simulation of a phenomenon or process, often to predict outcomes or test theories.

Data Collection Methods

• Surveys : Surveys are used to collect data from a sample of individuals using questionnaires or interviews. Surveys can be conducted face-to-face, by phone, mail, email, or online.
• Experiments : Experiments involve manipulating one or more variables to measure their effects on another variable, while controlling for other factors. Experiments can be conducted in a laboratory or in a natural setting.
• Case studies : Case studies involve in-depth analysis of a single case, such as an individual, group, organization, or event. Case studies can use a variety of data collection methods, including interviews, observation, and document analysis.
• Observational research : Observational research involves observing and recording the behavior of individuals or groups in a natural setting. Observational research can be conducted covertly or overtly.
• Content analysis : Content analysis involves analyzing written, spoken, or visual material to identify patterns, themes, or messages. Content analysis can be used to study media, social media, or other forms of communication.
• Ethnography : Ethnography involves immersion in a cultural group or community to understand their way of life, beliefs, values, and practices. Ethnographic research can use a range of data collection methods, including observation, interviews, and document analysis.
• Secondary data analysis : Secondary data analysis involves using existing data from sources such as government agencies, research institutions, or commercial organizations. Secondary data can be used to answer research questions, without collecting new data.
• Focus groups: Focus groups involve gathering a small group of people together to discuss a topic or issue. The discussions are usually guided by a moderator who asks questions and encourages discussion.
• Interviews : Interviews involve one-on-one conversations between a researcher and a participant. Interviews can be structured, semi-structured, or unstructured, and can be conducted in person, by phone, or online.
• Document analysis : Document analysis involves collecting and analyzing written documents, such as reports, memos, and emails. Document analysis can be used to study organizational communication, policy documents, and other forms of written material.

Data Analysis Methods

Data Analysis Methods in Research are as follows:

• Descriptive statistics : Descriptive statistics involve summarizing and describing the characteristics of a dataset, such as mean, median, mode, standard deviation, and frequency distributions.
• Inferential statistics: Inferential statistics involve making inferences or predictions about a population based on a sample of data, using methods such as hypothesis testing, confidence intervals, and regression analysis.
• Qualitative analysis: Qualitative analysis involves analyzing non-numerical data, such as text, images, or audio, to identify patterns, themes, or meanings. Qualitative analysis can be used to study subjective experiences, social norms, and cultural practices.
• Content analysis: Content analysis involves analyzing written, spoken, or visual material to identify patterns, themes, or messages. Content analysis can be used to study media, social media, or other forms of communication.
• Grounded theory: Grounded theory involves developing a theory or model based on empirical data, using methods such as constant comparison, memo writing, and theoretical sampling.
• Discourse analysis : Discourse analysis involves analyzing language use, including the structure, function, and meaning of words and phrases, to understand how language reflects and shapes social relationships and power dynamics.
• Network analysis: Network analysis involves analyzing the structure and dynamics of social networks, including the relationships between individuals and groups, to understand social processes and outcomes.

Research Methodology

Research methodology refers to the overall approach and strategy used to conduct a research study. It involves the systematic planning, design, and execution of research to answer specific research questions or test hypotheses. The main components of research methodology include:

• Research design : Research design refers to the overall plan and structure of the study, including the type of study (e.g., observational, experimental), the sampling strategy, and the data collection and analysis methods.
• Sampling strategy: Sampling strategy refers to the method used to select a representative sample of participants or units from the population of interest. The choice of sampling strategy will depend on the research question and the nature of the population being studied.
• Data collection methods : Data collection methods refer to the techniques used to collect data from study participants or sources, such as surveys, interviews, observations, or secondary data sources.
• Data analysis methods: Data analysis methods refer to the techniques used to analyze and interpret the data collected in the study, such as descriptive statistics, inferential statistics, qualitative analysis, or content analysis.
• Ethical considerations: Ethical considerations refer to the principles and guidelines that govern the treatment of human participants or the use of sensitive data in the research study.
• Validity and reliability : Validity and reliability refer to the extent to which the study measures what it is intended to measure and the degree to which the study produces consistent and accurate results.

Applications of Research

Research has a wide range of applications across various fields and industries. Some of the key applications of research include:

• Advancing scientific knowledge : Research plays a critical role in advancing our understanding of the world around us. Through research, scientists are able to discover new knowledge, uncover patterns and relationships, and develop new theories and models.
• Improving healthcare: Research is instrumental in advancing medical knowledge and developing new treatments and therapies. Clinical trials and studies help to identify the effectiveness and safety of new drugs and medical devices, while basic research helps to uncover the underlying causes of diseases and conditions.
• Enhancing education: Research helps to improve the quality of education by identifying effective teaching methods, developing new educational tools and technologies, and assessing the impact of various educational interventions.
• Driving innovation: Research is a key driver of innovation, helping to develop new products, services, and technologies. By conducting research, businesses and organizations can identify new market opportunities, gain a competitive advantage, and improve their operations.
• Informing public policy : Research plays an important role in informing public policy decisions. Policy makers rely on research to develop evidence-based policies that address societal challenges, such as healthcare, education, and environmental issues.
• Understanding human behavior : Research helps us to better understand human behavior, including social, cognitive, and emotional processes. This understanding can be applied in a variety of settings, such as marketing, organizational management, and public policy.

Importance of Research

Research plays a crucial role in advancing human knowledge and understanding in various fields of study. It is the foundation upon which new discoveries, innovations, and technologies are built. Here are some of the key reasons why research is essential:

• Advancing knowledge: Research helps to expand our understanding of the world around us, including the natural world, social structures, and human behavior.
• Problem-solving: Research can help to identify problems, develop solutions, and assess the effectiveness of interventions in various fields, including medicine, engineering, and social sciences.
• Innovation : Research is the driving force behind the development of new technologies, products, and processes. It helps to identify new possibilities and opportunities for improvement.
• Evidence-based decision making: Research provides the evidence needed to make informed decisions in various fields, including policy making, business, and healthcare.
• Education and training : Research provides the foundation for education and training in various fields, helping to prepare individuals for careers and advancing their knowledge.
• Economic growth: Research can drive economic growth by facilitating the development of new technologies and innovations, creating new markets and job opportunities.

When to use Research

Research is typically used when seeking to answer questions or solve problems that require a systematic approach to gathering and analyzing information. Here are some examples of when research may be appropriate:

• To explore a new area of knowledge : Research can be used to investigate a new area of knowledge and gain a better understanding of a topic.
• To identify problems and find solutions: Research can be used to identify problems and develop solutions to address them.
• To evaluate the effectiveness of programs or interventions : Research can be used to evaluate the effectiveness of programs or interventions in various fields, such as healthcare, education, and social services.
• To inform policy decisions: Research can be used to provide evidence to inform policy decisions in areas such as economics, politics, and environmental issues.
• To develop new products or technologies : Research can be used to develop new products or technologies and improve existing ones.
• To understand human behavior : Research can be used to better understand human behavior and social structures, such as in psychology, sociology, and anthropology.

Characteristics of Research

The following are some of the characteristics of research:

• Purpose : Research is conducted to address a specific problem or question and to generate new knowledge or insights.
• Systematic : Research is conducted in a systematic and organized manner, following a set of procedures and guidelines.
• Empirical : Research is based on evidence and data, rather than personal opinion or intuition.
• Objective: Research is conducted with an objective and impartial perspective, avoiding biases and personal beliefs.
• Rigorous : Research involves a rigorous and critical examination of the evidence and data, using reliable and valid methods of data collection and analysis.
• Logical : Research is based on logical and rational thinking, following a well-defined and logical structure.
• Generalizable : Research findings are often generalized to broader populations or contexts, based on a representative sample of the population.
• Replicable : Research is conducted in a way that allows others to replicate the study and obtain similar results.
• Ethical : Research is conducted in an ethical manner, following established ethical guidelines and principles, to ensure the protection of participants’ rights and well-being.
• Cumulative : Research builds on previous studies and contributes to the overall body of knowledge in a particular field.

Advantages of Research

Research has several advantages, including:

• Generates new knowledge: Research is conducted to generate new knowledge and understanding of a particular topic or phenomenon, which can be used to inform policy, practice, and decision-making.
• Provides evidence-based solutions : Research provides evidence-based solutions to problems and issues, which can be used to develop effective interventions and strategies.
• Improves quality : Research can improve the quality of products, services, and programs by identifying areas for improvement and developing solutions to address them.
• Enhances credibility : Research enhances the credibility of an organization or individual by providing evidence to support claims and assertions.
• Enables innovation: Research can lead to innovation by identifying new ideas, approaches, and technologies.
• Informs decision-making : Research provides information that can inform decision-making, helping individuals and organizations make more informed and effective choices.
• Facilitates progress: Research can facilitate progress by identifying challenges and opportunities and developing solutions to address them.
• Enhances understanding: Research can enhance understanding of complex issues and phenomena, helping individuals and organizations navigate challenges and opportunities more effectively.
• Promotes accountability : Research promotes accountability by providing a basis for evaluating the effectiveness of policies, programs, and interventions.
• Fosters collaboration: Research can foster collaboration by bringing together individuals and organizations with diverse perspectives and expertise to address complex issues and problems.

Limitations of Research

Some Limitations of Research are as follows:

• Cost : Research can be expensive, particularly when large-scale studies are required. This can limit the number of studies that can be conducted and the amount of data that can be collected.
• Time : Research can be time-consuming, particularly when longitudinal studies are required. This can limit the speed at which research findings can be generated and disseminated.
• Sample size: The size of the sample used in research can limit the generalizability of the findings to larger populations.
• Bias : Research can be affected by bias, both in the design and implementation of the study, as well as in the analysis and interpretation of the data.
• Ethics : Research can present ethical challenges, particularly when human or animal subjects are involved. This can limit the types of research that can be conducted and the methods that can be used.
• Data quality: The quality of the data collected in research can be affected by a range of factors, including the reliability and validity of the measures used, as well as the accuracy of the data entry and analysis.
• Subjectivity : Research can be subjective, particularly when qualitative methods are used. This can limit the objectivity and reliability of the findings.
• Accessibility : Research findings may not be accessible to all stakeholders, particularly those who are not part of the academic or research community.
• Interpretation : Research findings can be open to interpretation, particularly when the data is complex or contradictory. This can limit the ability of researchers to draw firm conclusions.
• Unforeseen events : Unexpected events, such as changes in the environment or the emergence of new technologies, can limit the relevance and applicability of research findings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Position Paper – Example, Format and Writing...

What is Literature – Definition, Types, Examples

What is Science – Definition, Methods, Types

Academic Paper – Format, Example and Writing...

Evolution – Definition, Types and Example

What is Art – Definition, Types, Examples

Community Blog

Keep up-to-date on postgraduate related issues with our quick reads written by students, postdocs, professors and industry leaders.

Types of Research – Explained with Examples

• By DiscoverPhDs
• October 2, 2020

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

• Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
• Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

Find out how you can use Scrivener for PhD Thesis & Dissertation writing to streamline your workflow and make academic writing fun again!

Impostor Syndrome is a common phenomenon amongst PhD students, leading to self-doubt and fear of being exposed as a “fraud”. How can we overcome these feelings?

The unit of analysis refers to the main parameter that you’re investigating in your research project or study.

Join thousands of other students and stay up to date with the latest PhD programmes, funding opportunities and advice.

Browse PhDs Now

In this post you’ll learn what the significance of the study means, why it’s important, where and how to write one in your paper or thesis with an example.

Multistage sampling is a more complex form of cluster sampling for obtaining sample populations. Learn their pros and cons and how to undertake them.

Dr Anwar gained her PhD in Biochemistry from the University of Helsinki in 2019. She is now pursuing a career within industry and becoming more active in science outreach.

Nina’s in the first year of her PhD in the Department of Psychology at the University of Bath. Her project is focused on furthering our understanding of fatigue within adolescent depression.

Join Thousands of Students

Types Of Research Theories

There are several types of research theories. The point when doing research, it is imperative to first have your thought, or subject you will be exploring. When you have picked the subject, then the examination starts. There are such a large number of spots to head off to use as sources. There are books, articles in magazines or daily papers, questioning sources, and the regularly developing web. Individual encounters are an alternate incredible method for getting data for your exploration. There are numerous approaches to acquire data for your subject, yet choosing the subject to research is the first stage. At that point, you have to choose how slender or wide-ran your subject is set to be. One case is Crime Scene Forensics. Will the paper be carried out on the whole subject of legal sciences, or will it be contracted down to one component of criminology, for example DNA testing, or perhaps ballistics? This is a choice that you will make before you begin exploring the subject. An exceptionally savvy teacher, said to pick something that hobbies you and the exploring will be a great deal all the more fascinating and enjoyable to do. I suppose this is an extremely exceptional notion to recollect about examination, and can additionally make the venture exceptionally satisfying.

When doing research, there are hypotheses that we use to get data. There are four principle types of research theories under utilization. The four speculations are deductive, inductive, crushed, and aphoristic. These four speculations are utilized for distinctive explanations. They are utilized independently and together.

Deductive Theory:

Deductive Theory is utilized while working from a bigger, more general set of thoughts, and works down to a more particular set of plans. This is known as the top – down methodology to research. We begin by utilizing a general thought, then working it down to a speculation that might be tried. We can even go further and thin this speculation down to a perception and test that into an affirmation of the definitive conclusion of our hypothesis.

Inductive Theory:

Inductive types of research theories work in the inverse way. We begin our examination from an exceptionally particular thought, and change it into an extremely wide and summed up hypothesis. This is known as a base – up methodology to research. We begin with extremely particular measures or perceptions, discover examples, then achieve a speculation that we investigate, winding up with summed up conclusions and hypotheses. The contrasts in these two hypotheses, is that deductive research begins extremely open finished and exploratory, while inductive research begins more particular. Generally investigate holds both techniques, in spite of the fact that it might appear that the data is one or the other sort.

Grounded theory:

Grounded theory is the point at which the hypothesis is created by utilizing the actualities. This is to a degree, an inductive system or approach. A few specialists say that crushed examination divides hypothesis and information, while others say it joins the two. This sort of hypothesis takes inquiries and correlations and utilizes express techniques to guide this sort of exploration. This system for examination furnishes the analyst thickness, immersion, and legitimization eventually information, by utilizing nitty gritty and efficient strategies. Pounded hypothesis has numerous preferences. Then again, on the grounds that the methodology requires a ton of finesse from the analyst, this technique is not proposed for beginner analysts and they might as well escape this strategy for examination.

Axiomatic theory:

Axiomatic types of research theories are applied when solid and foreseeable assessments could be got. Deductive focuses are utilized to make research foresee precisely what will happen. One case of this is Newton’s Theory of Gravity. Deductive strategies were utilized to confirm what might happen to the fruit when it fell. Deductively, this could dependably be anticipated to happen when scrutinizing gravity.

Related Posts

• Types Of Green Beans
• Types Of Circumcision
• Types Of Ear Infections
• Types Of Baseball Pitches
• Types Of Hernias in Men
• Types Of Doughnuts
• Types Of Dates
• Types Of Face Shapes

• Memberships

Univariate Analysis: basic theory and example

Univariate analysis: this article explains univariate analysis in a practical way. The article begins with a general explanation and an explanation of the reasons for applying this method in research, followed by the definition of the term and a graphical representation of the different ways of representing univariate statistics. Enjoy the read!

Introduction

Research is a dynamic process that carefully uses different techniques and methods to gain insights, validate hypotheses and make informed decisions.

Using a variety of analytical methods, researchers can gain a thorough understanding of their data, revealing patterns, trends, and relationships.

One of the main approaches or methods for research is the univariate analysis, which provides valuable insights into individual variables and their characteristics.

In this article, we dive into the world of univariate analysis, its definition, importance and applications in research.

Techniques and methods in research

Research methodologies encompass a wide variety of techniques and methods that help researchers extract meaningful information from their data. Some common approaches are:

Descriptive statistics

Summarizing data using measures such as mean, median, mode, variance, and standard deviation.

Inferential statistics

Drawing conclusions about a broader population based on a sample. Methods such as hypothesis testing and confidence intervals are used for this.

Multivariate analysis

Exploring relationships between multiple variables simultaneously, allowing researchers to explore complex interactions and dependencies. A bivariate analysis is when the relationship between two variables is explored.

Qualitative analysis

Discovering insights and trying to understand subjective type of data, such as interviews, observations and case studies.

Quantitative analysis

Analyzing numerical data using statistical methods to reveal patterns and trends.

What is univariate analysis?

Univariate analysis focuses on the study and interpretation of only one variable on its own, without considering possible relationships with other variables.

The method aims to understand the characteristics and behavior of that specific variable. Univariate analysis is the simplest form of analyzing data.

Definition of univariate

The term univariate consists of two elements: uni, which means one, and variate, which refers to a statistical variable. Therefore, univariate analysis focuses on exploring and summarizing the properties of one variable independently.

Importance of univariate analysis

Univariate analysis serves as an important first step in many research projects, as it provides essential insights and lays a foundation for further research. It offers researchers the following benefits:

Data exploration

Univariate analysis allows researchers to understand the distribution, central tendency, and variability of a variable.

Identification of outliers

By detecting anomalous values, univariate analysis helps identify outliers that require further investigation or treatment during the data analysis phase.

Data cleaning

Univariate analysis helps identify missing data, inconsistencies or errors within a variable, allowing researchers to refine and optimize their data set before moving on to more complex analyses.

Variable selection

Researchers can use the univariate analysis to determine which variables are most promising for further research. This enables efficient allocation of resources and hypothesis testing.

Reporting and visualization

Summarizing and visualizing univariate statistics facilitates clear and concise reporting of research results. This makes complex data more accessible to a wider audience.

Research Methods For Business Students Course A-Z guide to writing a rockstar Research Paper with a bulletproof Research Methodology!   More information

Applications of univariate analysis

Univariate analysis is used in various research areas and disciplines. It is often used in:

• Epidemiological studies to analyze risk factors
• Social science research to investigate attitudes, behaviors or socio-economic variables
• Market research to understand consumer preferences, buying patterns or market trends
• Environmental studies to investigate pollution, climate data or species distributions

By using univariate analysis, researchers can uncover valuable insights, detect trends, and lay the groundwork for more comprehensive statistical analysis.

Types of univariate analyses

The most common method of performing univariate analysis is summary statistics. The correct statistics are determined by the level of measurement or the nature of the information in the variabels. The following are the most common types of summary statistics:

• Measures of dispersion: these numbers describe how evenly the values are distributed in a dataset. The range, standard deviation, interquartile range, and variance are some examples.
• Range: the difference between the highest and lowest value in a data set.
• Standard deviation: an average measure of the spread.
• Interquartile range: the spread of the middle 50% of the values.
• Measures of central tendency: these numbers describe the location of the center point of a data set or the middle value of the data set. The mean, median and mode are the three main measures of central tendency.

Figure 1. Univariate Analysis – Types

Frequency table

Frequency indicates how often something occurs. The frequency of observation thus indicates the number of times an event occurs.

The frequency distribution table can display qualitative and numerical or quantitative variables. The distribution provides an overview of the data and allows you to spot patterns.

The bar chart is displayed in the form of rectangular bars. The chart compares different categories. The chart can be plotted vertically or horizontally.

In most cases, the bar is plotted vertically.

The horizontal or x-axis represents the category and the vertical y-axis represents the value of the category.

This diagram can be used, for example, to see which part of a budget is the largest.

A histogram is a graph that shows how often certain values occur in a data set. It consists of bars whose height indicates how often a certain value occurs.

Frequency polygon

The frequency polygon is very similar to the histogram. It is used to compare data sets or to display the cumulative frequency distribution.

The frequency polygon is displayed as a line graph.

The pie chart displays the data in a circular format. The diagram is divided into pieces where each piece is proportional to its part of the complete category. So each “pie slice” in the pie chart is a portion of the total. The total of the pieces should always be 100.

Example situation of an Univariate Analysis

An example of univariate analysis might be examining the age of employees in a company.

Data is collected on the age of all employees and then a univariate analysis is performed to understand the characteristics and distribution of this single variable.

We can calculate summary statistics, such as the mean, median, and standard deviation, to get an idea of the central tendency and range of ages.

Histograms can also be used to visualize the frequency of different age groups and to identify any patterns or outliers.

Now it’s your turn

What do you think? Do you recognize the explanation about the univariate analysis? Have you ever heard of univariate analysis? Have you applied it yourself during any of the studies you have conducted? Do you know of any other methods or techniques used in conjunction with univariate analysis? Are you familiar with the visual graphs used in univariate analysis?

Share your experience and knowledge in the comments box below.

More information about the Univariate Analysis

• Barick, R. (2021). Research Methods For Business Students . Retrieved 02/16/2024 from Udemy.
• Dowdy, S., Wearden, S., & Chilko, D. (2011). Statistics for research . John Wiley & Sons.
• Garfield, J., & Ben‐Zvi, D. (2007). How students learn statistics revisited: A current review of research on teaching and learning statistics . International statistical review, 75(3), 372-396.
• Ostle, B. (1963). Statistics in research . Statistics in research., (2nd Ed).
• Wagner III, W. E. (2019). Using IBM® SPSS® statistics for research methods and social science statistics . Sage Publications .

How to cite this article: Janse, B. (2024). Univariate Analysis . Retrieved [insert date] from Toolshero: https://www.toolshero.com/research/univariate-analysis/

Original publication date: 03/22/2024 | Last update: 03/22/2024

Add a link to this page on your website: <a href=”https://www.toolshero.com/research/univariate-analysis/”>Toolshero: Univariate Analysis</a>

Did you find this article interesting?

Your rating is more than welcome or share this article via Social media!

Average rating 4 / 5. Vote count: 4

No votes so far! Be the first to rate this post.

We are sorry that this post was not useful for you!

Let us improve this post!

Tell us how we can improve this post?

Ben Janse is a young professional working at ToolsHero as Content Manager. He is also an International Business student at Rotterdam Business School where he focusses on analyzing and developing management models. Thanks to his theoretical and practical knowledge, he knows how to distinguish main- and side issues and to make the essence of each article clearly visible.

Related ARTICLES

Bivariate Analysis in Research explained

Contingency Table: the Theory and an Example

Content Analysis explained plus example

Starting a Thesis: The Most Common How’s, Why’s, and Where’s Answered

Field Research explained

Observational Research Method explained

Also interesting.

Research Ethics explained

Research Proposal explained and guide

Research Cycle Process and Theory explained

Leave a reply cancel reply.

You must be logged in to post a comment.

BOOST YOUR SKILLS

Toolshero supports people worldwide ( 10+ million visitors from 100+ countries ) to empower themselves through an easily accessible and high-quality learning platform for personal and professional development.

By making access to scientific knowledge simple and affordable, self-development becomes attainable for everyone, including you! Join our learning platform and boost your skills with Toolshero.

POPULAR TOPICS

• Change Management
• Marketing Theories
• Problem Solving Theories
• Psychology Theories

ABOUT TOOLSHERO

• Free Toolshero e-book
• Memberships & Pricing

Chapter 4: Theory in Psychology

4.2 the variety of theories in psychology, learning objectives.

• Describe three dimensions along which theories in psychology vary.
• Give examples of several different types of theories in psychology.

Researchers in psychology have found that many different types of theories can help them to organize phenomena, predict what will happen in new situations, and generate new research. It is important for beginning researchers to be aware of the different types so that they recognize theories when they see them in the research literature. (They are not always clearly labeled as “theories.”) It is also important for them to see that some types of theories are well within their ability to understand, use, and even construct. In this section, we look at the variety of psychological theories in terms of three important dimensions: formality, scope, and theoretical approach.

Psychological theories vary widely in their formality —the extent to which the components of the theory and the relationships among them are specified clearly and in detail. At the informal end of this dimension are theories that consist of simple verbal descriptions of a few important components and relationships. The habituation theory of expressive-writing effects on health is relatively informal in this sense. So is the drive theory of social facilitation and inhibition. At the more precise, formal end of this dimension are theories that are expressed in terms of mathematical equations or computer programs.

Formal Theories in Psychology

People who are not familiar with scientific psychology are sometimes surprised to learn that psychological theories can take the form of mathematical equations and computer programs. The following formal theories are among the best known and most successful in the field.

• ACT-R. A comprehensive theory of human cognition that is akin to a programming language, within which more specific models can be created. See http://act-r.psy.cmu.edu .
• Prospect theory. A formal theory of decision making under uncertainty. Psychologist Daniel Kahneman won the Nobel Prize in economics based in part on prospect theory. Read about Kahneman’s Nobel Prize work at http://www.nobelprize.org/nobel_prizes/economics/laureates/2002/kahneman-autobio.html .
• Rescorla-Wagner model. A theory of classical conditioning that features an equation describing how the strength of the association between unconditioned and conditioned stimuli changes when the two are paired. For more on this formal theory—including an interactive version—see http://psych.hanover.edu/javatest/rescrolawagner .

Both informal and formal theories have their place in psychological research. Informal theories tend to be easier to create and to understand but less precise in their predictions, which can make them more difficult to test. They are especially appropriate, however, in the early stages of research when the phenomena of interest have not yet been described in detail. Formal theories tend to be more difficult to create and to understand—sometimes requiring a certain amount of mathematical or computer programming background—but they also tend to be more precise in their predictions and therefore easier to test. They are especially appropriate in the later stages of research when the phenomena of interest have been described in detail

Theories in psychology also vary widely in their scope —the number and diversity of the phenomena they explain or interpret. Many early psychological theories were extremely broad in that they attempted to interpret essentially all human behavior. Freud and his followers, for example, applied his theory not only to understanding psychological disorders but also to slips of the tongue and other everyday errors, dreaming, sexuality, art, politics, and even civilization itself (Fine, 1979). Such theories have fallen out of favor in scientific psychology, however, because they tend to be imprecise and difficult to test. In addition, they have not been particularly successful at organizing or predicting the range and complexity of human behavior at the level of detail that scientific researchers usually seek.

Still, contemporary theories in psychology can vary in their scope. At the broad end of this dimension are theories that apply to many diverse phenomena. Cognitive dissonance theory, for example, assumes that when people hold inconsistent beliefs, this creates mental discomfort that they are motivated to reduce by changing one or both of the beliefs. This theory has been applied to a wide variety of phenomena, including the persistence of irrational beliefs and behaviors (e.g., smoking), the effectiveness of certain persuasion and sales techniques (e.g., asking for a small favor before asking for a big one), and even placebo effects. At the narrow end of this dimension are theories that apply to a small number of closely related phenomena. Consider, for example, a very specific quantitative ability called subitizing. This refers to people’s ability to quickly and accurately perceive the number of objects in a scene without counting them—as long as the number is four or fewer. Several theories have been proposed to explain subitizing. Among them is the idea that small numbers of objects are associated with easily recognizable patterns. For example, people know immediately that there are three objects in a scene because the three objects tend to form a “triangle” and it is this pattern that is quickly perceived (Logan & Sbrodoff, 2003).

As with informal and formal theories, both broad and narrow theories have their place in psychological research. Broad theories organize more phenomena but tend to be less formal and less precise in their predictions. Narrow theories organize fewer phenomena but tend to be more formal and more precise in their predictions.

Theoretical Approach

In addition to varying in formality and scope, theories in psychology vary widely in the kinds of theoretical ideas they are constructed from. We will refer to this as the theoretical approach .

Functional theories explain psychological phenomena in terms of their function or purpose. For example, one prominent theory of repeated self-injury (e.g., cutting) is that people do it because it produces a short-term reduction in the intensity of negative emotions that they are feeling (Tantam & Huband, 2009). Note that this theory does not focus on how this happens, but on the function of self-injury for the people who engage in it. Theories from the perspective of evolutionary psychology also tend to be functional—assuming that human behavior has evolved to solve specific adaptive problems faced by our distant ancestors. Consider the phenomenon of sex differences in human mating strategies (Buss & Schmitt, 1993). Men are somewhat more likely than women to seek short-term partners and to value physical attractiveness over material resources in a mate. Women are somewhat more likely than men to seek long-term partners and to value material resources over physical attractiveness in a mate. But why? The standard evolutionary theory holds that because the male investment in becoming a parent is relatively small, men reproduce more successfully by seeking several short-term partners who are young and healthy (which is signaled by physical attractiveness). But because the female investment in becoming a parent is quite large, women reproduce more successfully by seeking a long-term partner who has resources to contribute to raising the child.

Mechanistic theories , on the other hand, focus on specific variables, structures, and processes, and how they interact to produce the phenomena. The drive theory of social facilitation and inhibition and the multistore model of human memory are mechanistic theories in this sense. Figure 4.4 “Simplified Representation of One Contemporary Theory of Hypochondriasis” represents another example—a contemporary cognitive theory of hypochondriasis—an extreme form of health anxiety in which people misinterpret ordinary bodily symptoms (e.g., headaches) as signs of a serious illness (e.g., a brain tumor; Williams, 2004). This theory specifies several key variables and the relationships among them. Specifically, people who are high in the personality trait of neuroticism (also called negative emotionality) start to pay excessive attention to negative health information—especially if they have had a significant illness experience as a child (e.g., a seriously ill parent). This attention to negative health information then leads to health anxiety and hypochondriasis, especially among people who are low in effortful control, which is the ability to shift attention away from negative thoughts and feelings.

Figure 4.4 Simplified Representation of One Contemporary Theory of Hypochondriasis

This theory focuses on key variables and the relationships among them.

Mechanistic theories can also be expressed in terms of biological structures and processes. With advances in genetics and neuroscience, such theories are becoming increasingly common in psychology. For example, researchers are currently constructing and testing theories that specify the brain structures associated with the storage and rehearsal of information in the short-term store, the transfer of information to the long-term store, and so on. Theories of psychological disorders are also increasingly likely to focus on biological mechanisms. Schizophrenia, for example, has been explained in terms of several biological theories, including theories that focus on genetics, neurotransmitters, brain structures, and even prenatal exposure to infections.

Finally, there are also theoretical approaches that provide organization without necessarily providing a functional or mechanistic explanation. These include stage theories , which specify a series of stages that people pass through as they develop or adapt to their environment. Famous stage theories include Abraham Maslow’s hierarchy of needs and Jean Piaget’s theory of cognitive development. Typologies provide organization by categorizing people or behavior into distinct types. These include theories that identify several basic emotions (e.g., happiness, sadness, fear, surprise, anger, and disgust), several distinct types of intelligence (e.g., spatial, linguistic, mathematical, kinesthetic, musical, interpersonal, and intrapersonal), and distinct types of personalities (e.g., Type A vs. Type B).

Researchers in psychology have found that there is a place for all these theoretical approaches. In fact, multiple approaches are probably necessary to provide a complete understanding of any set of phenomena. A complete understanding of emotions, for example, is likely to require identifying the basic emotions that people experience, explaining why we have those emotions, and describing how those emotions work in terms of underlying psychological and biological variables, structures, and processes.

Key Takeaway

• Theories in psychology vary widely in terms of their formality, scope, and theoretical approach. The different types of theories all play important roles in psychological research.
• Practice: Find an empirical research report in a professional journal, identify a theory that the researchers present, and then describe the theory in terms of its formality (informal vs. formal), scope (broad vs. narrow), and theoretical approach (functional, mechanistic, etc.).
• Discussion: Do you think there will ever be a single theory that explains all psychological disorders? Why or why not?

Buss, D. M., & Schmitt, D. P. (1993). Sexual strategies theory: A contextual evolutionary analysis of human mating. Psychological Review, 100 , 204–232.

Fine, R. (1979). A history of psychoanalysis . New York, NY: Columbia University Press.

Logan, G. D., & Sbrodoff, N. J. (2003). Subitizing and similarity: Toward a pattern-matching theory of enumeration. Psychonomic Bulletin & Review, 10 , 676–682.

Tantam, D., & Huband, N. (2009). Understanding repeated self-injury: A multidisciplinary approach . New York, NY: Palgrave Macmillan.

Williams, P. G. (2004). The psychopathology of self-assessed health: A cognitive approach to health anxiety and hypochondriasis. Cognitive Therapy and Research, 28 , 629–644.

• Research Methods in Psychology. Provided by : University of Minnesota Libraries Publishing. Located at : http://open.lib.umn.edu/psychologyresearchmethods/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

Privacy Policy

You are using an outdated browser. Please upgrade your browser to improve your experience.

Issues We Care About

• Digital Divide
• Affordability
• College Readiness
• Environmental Barriers

Measuring Impact

• WGU's Success Metrics
• Policy Priorities
• Annual Report
• WGU's Story
• Careers at WGU
• Impact Blog

Types of Psychology: The 4 Branches

• See More Tags

The mind is a powerful and complex wonder—and psychology is the key to unlocking and understanding it. Psychology is a broad field that delves into human thought processes, emotions, motivations, and actions. As a result, psychologists employ diverse research methods and theories to understand human cognition and behavior.

• There are four main branches of psychology, each offering distinct insights into the complexities of  human behavior and mental processes. 
• Clinical psychology. This branch focuses on diagnosing and treating mental illnesses and disorders through therapeutic methods.
• Cognitive psychology. By investigating mental processes such as perception, memory, and problem-solving, researchers better understand how individuals acquire, process, and apply information. 
• Developmental psychology. Understanding how people grow and change across their lifespan, from infancy to old age, reveals factors that influence human development and behavior. 
• Social psychology. This discipline examines how social interactions, relationships, and group dynamics influence behavior and cognition. 
• Gaining knowledge of each area can help individuals interested in psychology decide where to focus their academic and career pursuits. This article will explore the applications and benefits of the four main branches of psychology.

Clinical Psychology 

Clinical psychology focuses on the diagnosis and treatment of mental disorders through various therapeutic methods. According to the American Psychological Association, professionals within this specialty provide comprehensive and continuing mental and behavioral health care for a broad spectrum of mental health issues. Their goal is to work toward the psychological well-being of individuals, couples, families, and communities. Key interventions include cognitive-behavioral therapy (CBT) and psychoanalysis, which help patients navigate challenges to improve their quality of life. 

Clinical psychologists play a crucial role in identifying and addressing mental disorders and stressors. They work in a variety of settings, including hospitals, private practice, community mental health centers, and academic institutions. Working closely with clients and healthcare teams, clinical psychologists conduct assessments and intake interviews, and administer psychological tests to gain insights into cognitive, emotional, and behavioral patterns. Clinical psychologists also contribute significantly to research, training, supervision, and consultation.

Cognitive Psychology 

Cognitive psychology focuses on mental processes such as perception, learning, thinking, memory, language, and problem-solving. It enhances our understanding of human intellectual abilities and how people acquire, process, store, and retrieve information. In other words, cognitive psychology helps researchers understand the human brain.

Cognitive psychology has broad applications in education, healthcare, technology, and beyond. Through various research methods such as experiments, observation, and computer simulations, cognitive psychologists examine how individuals interpret and make sense of information from their environment. They explore sensory interpretation, memory encoding, storage and retrieval, and how these processes contribute to improving memory, problem-solving skills, and decision-making abilities. Studying this branch of psychology allows researchers to develop strategies that address memory deficits and cognitive disorders. Most cognitive psychologists work in academic settings, where they teach or conduct research. They may also work directly with patients in private practice, treatment facilities, or other clinical settings.  

Developmental Psychology 

Developmental psychologists study how people grow, develop, and change at different life stages. In addition to physical development, psychologists who specialize in this field study the social, emotional, and cognitive development that occurs throughout life. This may include language acquisition, moral reasoning, motor skill development, self-awareness, social and cultural influence, and personality development. Two key theories that developmental psychologists study are Piaget's stages of cognitive development, which examines how children's thinking evolves, and Erikson's stages of psychosocial development over the course of a lifetime. 

Piaget’s theory of cognitive development elaborates upon how children's intelligence develops and changes as they grow. According to Piaget’s theory, children progress through four distinct stages:

• Sensorimotor (0 to 2 years) 
• Preoperational (2 to 7 years) 
• Concrete operational (7 to 11 years) 
• Formal operational (12+ years)

Each stage is defined by distinct parameters regarding cognitive abilities and limitations, such as abstract reasoning and object permanence, which help shape a child's understanding of the world. By describing these stages, Piaget illustrates how cognitive growth intertwines with psychological development for a holistic view of human development.

Erikson's stages of psychosocial development emerge as individuals progress through life, from childhood through adulthood. Each stage is characterized by two contrasting psychological tendencies, one positive/syntactic and one negative/dystonic (for example, trust versus mistrust or intimacy versus isolation). These tensions shape an individual’s psychological growth and interpersonal dynamics, influencing their sense of self and their relationships throughout life. Accordingly, Erikson notes that resolving any conflicts that arise is essential for achieving a healthy psychosocial balance and successful development.

Social Psychology 

Social psychology explores how individuals think, feel, and behave in social contexts. By studying group behavior, social perception, and interpersonal relationships, social psychologists uncover the underlying processes that govern human interaction and influence behavior.

From conformity and obedience to prejudice and interpersonal attraction, social psychologists examine a wide range of phenomena that shape our social world. Their research findings have practical implications in areas such as marketing, politics, and organizational behavior, highlighting the profound impact of social psychology on various aspects of society.

Understanding the four main branches of psychology—clinical, cognitive, developmental, and social—provides valuable insights into human behavior and mental processes. The foundational knowledge gained from a bachelor's degree program in psychology lays the groundwork for a career in psychology or other industries. 

WGU's B.S. Psychology degree is an online program that allows you to study and work at your own pace. Virtual simulations enhance the experience, teaching students how to apply psychologically sound approaches to interpersonal communication, collaboration, and conflict resolution. About 49% of graduates with a bachelor’s degree in psychology go on to earn an advanced degree. Whether you plan to pursue further education to become a therapist or utilize the knowledge in another field , a bachelor's degree in psychology equips you with vital research, analysis, and communication skills that will benefit you in any career you choose. 

Ready to Start Your Journey?

HEALTH & NURSING

Recommended Articles

Take a look at other articles from WGU. Our articles feature information on a wide variety of subjects, written with the help of subject matter experts and researchers who are well-versed in their industries. This allows us to provide articles with interesting, relevant, and accurate information. 

Back-End Developer vs. Back-End Engineer

10 Careers to Pursue with an Associate's Degree in Healthcare

7 Back-end Web Development Programming Languages

Employee Spotlight: Amy Antonucci

10 Best Mental Health Careers and Why You Should Pursue One

The university.

• Accessibility
• Accreditation

For Students

• Student Portal
• Alumni Services

Most Visited Links

• Business Programs
• Bachelor's Degrees
• Student Experience
• Online Degrees
• Scholarships
• Financial Aid
• Diversity, Equity, and Inclusion
• Testimonials
• Student Communities

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Perspective
• Published: 06 March 2024

Artificial intelligence and illusions of understanding in scientific research

• Lisa Messeri   ORCID: orcid.org/0000-0002-0964-123X 1   na1 &
• M. J. Crockett   ORCID: orcid.org/0000-0001-8800-410X 2 , 3   na1  

Nature volume  627 ,  pages 49–58 ( 2024 ) Cite this article

20k Accesses

3 Citations

706 Altmetric

Metrics details

• Human behaviour
• Interdisciplinary studies
• Research management
• Social anthropology

Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists’ visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 51 print issues and online access

185,98 € per year

only 3,65 € per issue

Rent or buy this article

Prices vary by article type

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

Nobel Turing Challenge: creating the engine for scientific discovery

Hiroaki Kitano

Accelerating science with human-aware artificial intelligence

Jamshid Sourati & James A. Evans

On scientific understanding with artificial intelligence

Mario Krenn, Robert Pollice, … Alán Aspuru-Guzik

Crabtree, G. Self-driving laboratories coming of age. Joule 4 , 2538–2541 (2020).

Article   CAS   Google Scholar  

Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620 , 47–60 (2023). This review explores how AI can be incorporated across the research pipeline, drawing from a wide range of scientific disciplines .

Article   CAS   PubMed   ADS   Google Scholar  

Dillion, D., Tandon, N., Gu, Y. & Gray, K. Can AI language models replace human participants? Trends Cogn. Sci. 27 , 597–600 (2023).

Article   PubMed   Google Scholar  

Grossmann, I. et al. AI and the transformation of social science research. Science 380 , 1108–1109 (2023). This forward-looking article proposes a variety of ways to incorporate generative AI into social-sciences research .

Gil, Y. Will AI write scientific papers in the future? AI Mag. 42 , 3–15 (2022).

Google Scholar  

Kitano, H. Nobel Turing Challenge: creating the engine for scientific discovery. npj Syst. Biol. Appl. 7 , 29 (2021).

Article   PubMed   PubMed Central   Google Scholar  

Benjamin, R. Race After Technology: Abolitionist Tools for the New Jim Code (Oxford Univ. Press, 2020). This book examines how social norms about race become embedded in technologies, even those that are focused on providing good societal outcomes .

Broussard, M. More Than a Glitch: Confronting Race, Gender, and Ability Bias in Tech (MIT Press, 2023).

Noble, S. U. Algorithms of Oppression: How Search Engines Reinforce Racism (New York Univ. Press, 2018).

Bender, E. M., Gebru, T., McMillan-Major, A. & Shmitchell, S. On the dangers of stochastic parrots: can language models be too big? in Proc. 2021 ACM Conference on Fairness, Accountability, and Transparency 610–623 (Association for Computing Machinery, 2021). One of the first comprehensive critiques of large language models, this article draws attention to a host of issues that ought to be considered before taking up such tools .

Crawford, K. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence (Yale Univ. Press, 2021).

Johnson, D. G. & Verdicchio, M. Reframing AI discourse. Minds Mach. 27 , 575–590 (2017).

Article   Google Scholar  

Atanasoski, N. & Vora, K. Surrogate Humanity: Race, Robots, and the Politics of Technological Futures (Duke Univ. Press, 2019).

Mitchell, M. & Krakauer, D. C. The debate over understanding in AI’s large language models. Proc. Natl Acad. Sci. USA 120 , e2215907120 (2023).

Kidd, C. & Birhane, A. How AI can distort human beliefs. Science 380 , 1222–1223 (2023).

Birhane, A., Kasirzadeh, A., Leslie, D. & Wachter, S. Science in the age of large language models. Nat. Rev. Phys. 5 , 277–280 (2023).

Kapoor, S. & Narayanan, A. Leakage and the reproducibility crisis in machine-learning-based science. Patterns 4 , 100804 (2023).

Hullman, J., Kapoor, S., Nanayakkara, P., Gelman, A. & Narayanan, A. The worst of both worlds: a comparative analysis of errors in learning from data in psychology and machine learning. In Proc. 2022 AAAI/ACM Conference on AI, Ethics, and Society (eds Conitzer, V. et al.) 335–348 (Association for Computing Machinery, 2022).

Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1 , 206–215 (2019). This paper articulates the problems with attempting to explain AI systems that lack interpretability, and advocates for building interpretable models instead .

Crockett, M. J., Bai, X., Kapoor, S., Messeri, L. & Narayanan, A. The limitations of machine learning models for predicting scientific replicability. Proc. Natl Acad. Sci. USA 120 , e2307596120 (2023).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Lazar, S. & Nelson, A. AI safety on whose terms? Science 381 , 138 (2023).

Article   PubMed   ADS   Google Scholar  

Collingridge, D. The Social Control of Technology (St Martin’s Press, 1980).

Wagner, G., Lukyanenko, R. & Paré, G. Artificial intelligence and the conduct of literature reviews. J. Inf. Technol. 37 , 209–226 (2022).

Hutson, M. Artificial-intelligence tools aim to tame the coronavirus literature. Nature https://doi.org/10.1038/d41586-020-01733-7 (2020).

Haas, Q. et al. Utilizing artificial intelligence to manage COVID-19 scientific evidence torrent with Risklick AI: a critical tool for pharmacology and therapy development. Pharmacology 106 , 244–253 (2021).

Article   CAS   PubMed   Google Scholar  

Müller, H., Pachnanda, S., Pahl, F. & Rosenqvist, C. The application of artificial intelligence on different types of literature reviews – a comparative study. In 2022 International Conference on Applied Artificial Intelligence (ICAPAI) https://doi.org/10.1109/ICAPAI55158.2022.9801564 (Institute of Electrical and Electronics Engineers, 2022).

van Dinter, R., Tekinerdogan, B. & Catal, C. Automation of systematic literature reviews: a systematic literature review. Inf. Softw. Technol. 136 , 106589 (2021).

Aydın, Ö. & Karaarslan, E. OpenAI ChatGPT generated literature review: digital twin in healthcare. In Emerging Computer Technologies 2 (ed. Aydın, Ö.) 22–31 (İzmir Akademi Dernegi, 2022).

AlQuraishi, M. AlphaFold at CASP13. Bioinformatics 35 , 4862–4865 (2019).

Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596 , 583–589 (2021).

Article   CAS   PubMed   PubMed Central   ADS   Google Scholar  

Lee, J. S., Kim, J. & Kim, P. M. Score-based generative modeling for de novo protein design. Nat. Computat. Sci. 3 , 382–392 (2023).

Gómez-Bombarelli, R. et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat. Mater. 15 , 1120–1127 (2016).

Krenn, M. et al. On scientific understanding with artificial intelligence. Nat. Rev. Phys. 4 , 761–769 (2022).

Extance, A. How AI technology can tame the scientific literature. Nature 561 , 273–274 (2018).

Hastings, J. AI for Scientific Discovery (CRC Press, 2023). This book reviews current and future incorporation of AI into the scientific research pipeline .

Ahmed, A. et al. The future of academic publishing. Nat. Hum. Behav. 7 , 1021–1026 (2023).

Gray, K., Yam, K. C., Zhen’An, A. E., Wilbanks, D. & Waytz, A. The psychology of robots and artificial intelligence. In The Handbook of Social Psychology (eds Gilbert, D. et al.) (in the press).

Argyle, L. P. et al. Out of one, many: using language models to simulate human samples. Polit. Anal. 31 , 337–351 (2023).

Aher, G., Arriaga, R. I. & Kalai, A. T. Using large language models to simulate multiple humans and replicate human subject studies. In Proc. 40th International Conference on Machine Learning (eds Krause, A. et al.) 337–371 (JMLR.org, 2023).

Binz, M. & Schulz, E. Using cognitive psychology to understand GPT-3. Proc. Natl Acad. Sci. USA 120 , e2218523120 (2023).

Ornstein, J. T., Blasingame, E. N. & Truscott, J. S. How to train your stochastic parrot: large language models for political texts. Github , https://joeornstein.github.io/publications/ornstein-blasingame-truscott.pdf (2023).

He, S. et al. Learning to predict the cosmological structure formation. Proc. Natl Acad. Sci. USA 116 , 13825–13832 (2019).

Article   MathSciNet   CAS   PubMed   PubMed Central   ADS   Google Scholar  

Mahmood, F. et al. Deep adversarial training for multi-organ nuclei segmentation in histopathology images. IEEE Trans. Med. Imaging 39 , 3257–3267 (2020).

Teixeira, B. et al. Generating synthetic X-ray images of a person from the surface geometry. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 9059–9067 (Institute of Electrical and Electronics Engineers, 2018).

Marouf, M. et al. Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks. Nat. Commun. 11 , 166 (2020).

Watts, D. J. A twenty-first century science. Nature 445 , 489 (2007).

boyd, d. & Crawford, K. Critical questions for big data. Inf. Commun. Soc. 15 , 662–679 (2012). This article assesses the ethical and epistemic implications of scientific and societal moves towards big data and provides a parallel case study for thinking about the risks of artificial intelligence .

Jolly, E. & Chang, L. J. The Flatland fallacy: moving beyond low–dimensional thinking. Top. Cogn. Sci. 11 , 433–454 (2019).

Yarkoni, T. & Westfall, J. Choosing prediction over explanation in psychology: lessons from machine learning. Perspect. Psychol. Sci. 12 , 1100–1122 (2017).

Radivojac, P. et al. A large-scale evaluation of computational protein function prediction. Nat. Methods 10 , 221–227 (2013).

Bileschi, M. L. et al. Using deep learning to annotate the protein universe. Nat. Biotechnol. 40 , 932–937 (2022).

Barkas, N. et al. Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat. Methods 16 , 695–698 (2019).

Demszky, D. et al. Using large language models in psychology. Nat. Rev. Psychol. 2 , 688–701 (2023).

Karjus, A. Machine-assisted mixed methods: augmenting humanities and social sciences with artificial intelligence. Preprint at https://arxiv.org/abs/2309.14379 (2023).

Davies, A. et al. Advancing mathematics by guiding human intuition with AI. Nature 600 , 70–74 (2021).

Peterson, J. C., Bourgin, D. D., Agrawal, M., Reichman, D. & Griffiths, T. L. Using large-scale experiments and machine learning to discover theories of human decision-making. Science 372 , 1209–1214 (2021).

Ilyas, A. et al. Adversarial examples are not bugs, they are features. Preprint at https://doi.org/10.48550/arXiv.1905.02175 (2019)

Semel, B. M. Listening like a computer: attentional tensions and mechanized care in psychiatric digital phenotyping. Sci. Technol. Hum. Values 47 , 266–290 (2022).

Gil, Y. Thoughtful artificial intelligence: forging a new partnership for data science and scientific discovery. Data Sci. 1 , 119–129 (2017).

Checco, A., Bracciale, L., Loreti, P., Pinfield, S. & Bianchi, G. AI-assisted peer review. Humanit. Soc. Sci. Commun. 8 , 25 (2021).

Thelwall, M. Can the quality of published academic journal articles be assessed with machine learning? Quant. Sci. Stud. 3 , 208–226 (2022).

Dhar, P. Peer review of scholarly research gets an AI boost. IEEE Spectrum spectrum.ieee.org/peer-review-of-scholarly-research-gets-an-ai-boost (2020).

Heaven, D. AI peer reviewers unleashed to ease publishing grind. Nature 563 , 609–610 (2018).

Conroy, G. How ChatGPT and other AI tools could disrupt scientific publishing. Nature 622 , 234–236 (2023).

Nosek, B. A. et al. Replicability, robustness, and reproducibility in psychological science. Annu. Rev. Psychol. 73 , 719–748 (2022).

Altmejd, A. et al. Predicting the replicability of social science lab experiments. PLoS ONE 14 , e0225826 (2019).

Yang, Y., Youyou, W. & Uzzi, B. Estimating the deep replicability of scientific findings using human and artificial intelligence. Proc. Natl Acad. Sci. USA 117 , 10762–10768 (2020).

Youyou, W., Yang, Y. & Uzzi, B. A discipline-wide investigation of the replicability of psychology papers over the past two decades. Proc. Natl Acad. Sci. USA 120 , e2208863120 (2023).

Rabb, N., Fernbach, P. M. & Sloman, S. A. Individual representation in a community of knowledge. Trends Cogn. Sci. 23 , 891–902 (2019). This comprehensive review paper documents the empirical evidence for distributed cognition in communities of knowledge and the resultant vulnerabilities to illusions of understanding .

Rozenblit, L. & Keil, F. The misunderstood limits of folk science: an illusion of explanatory depth. Cogn. Sci. 26 , 521–562 (2002). This paper provided an empirical demonstration of the illusion of explanatory depth, and inspired a programme of research in cognitive science on communities of knowledge .

Hutchins, E. Cognition in the Wild (MIT Press, 1995).

Lave, J. & Wenger, E. Situated Learning: Legitimate Peripheral Participation (Cambridge Univ. Press, 1991).

Kitcher, P. The division of cognitive labor. J. Philos. 87 , 5–22 (1990).

Hardwig, J. Epistemic dependence. J. Philos. 82 , 335–349 (1985).

Keil, F. in Oxford Studies In Epistemology (eds Gendler, T. S. & Hawthorne, J.) 143–166 (Oxford Academic, 2005).

Weisberg, M. & Muldoon, R. Epistemic landscapes and the division of cognitive labor. Philos. Sci. 76 , 225–252 (2009).

Sloman, S. A. & Rabb, N. Your understanding is my understanding: evidence for a community of knowledge. Psychol. Sci. 27 , 1451–1460 (2016).

Wilson, R. A. & Keil, F. The shadows and shallows of explanation. Minds Mach. 8 , 137–159 (1998).

Keil, F. C., Stein, C., Webb, L., Billings, V. D. & Rozenblit, L. Discerning the division of cognitive labor: an emerging understanding of how knowledge is clustered in other minds. Cogn. Sci. 32 , 259–300 (2008).

Sperber, D. et al. Epistemic vigilance. Mind Lang. 25 , 359–393 (2010).

Wilkenfeld, D. A., Plunkett, D. & Lombrozo, T. Depth and deference: when and why we attribute understanding. Philos. Stud. 173 , 373–393 (2016).

Sparrow, B., Liu, J. & Wegner, D. M. Google effects on memory: cognitive consequences of having information at our fingertips. Science 333 , 776–778 (2011).

Fisher, M., Goddu, M. K. & Keil, F. C. Searching for explanations: how the internet inflates estimates of internal knowledge. J. Exp. Psychol. Gen. 144 , 674–687 (2015).

De Freitas, J., Agarwal, S., Schmitt, B. & Haslam, N. Psychological factors underlying attitudes toward AI tools. Nat. Hum. Behav. 7 , 1845–1854 (2023).

Castelo, N., Bos, M. W. & Lehmann, D. R. Task-dependent algorithm aversion. J. Mark. Res. 56 , 809–825 (2019).

Cadario, R., Longoni, C. & Morewedge, C. K. Understanding, explaining, and utilizing medical artificial intelligence. Nat. Hum. Behav. 5 , 1636–1642 (2021).

Oktar, K. & Lombrozo, T. Deciding to be authentic: intuition is favored over deliberation when authenticity matters. Cognition 223 , 105021 (2022).

Bigman, Y. E., Yam, K. C., Marciano, D., Reynolds, S. J. & Gray, K. Threat of racial and economic inequality increases preference for algorithm decision-making. Comput. Hum. Behav. 122 , 106859 (2021).

Claudy, M. C., Aquino, K. & Graso, M. Artificial intelligence can’t be charmed: the effects of impartiality on laypeople’s algorithmic preferences. Front. Psychol. 13 , 898027 (2022).

Snyder, C., Keppler, S. & Leider, S. Algorithm reliance under pressure: the effect of customer load on service workers. Preprint at SSRN https://doi.org/10.2139/ssrn.4066823 (2022).

Bogert, E., Schecter, A. & Watson, R. T. Humans rely more on algorithms than social influence as a task becomes more difficult. Sci Rep. 11 , 8028 (2021).

Raviv, A., Bar‐Tal, D., Raviv, A. & Abin, R. Measuring epistemic authority: studies of politicians and professors. Eur. J. Personal. 7 , 119–138 (1993).

Cummings, L. The “trust” heuristic: arguments from authority in public health. Health Commun. 29 , 1043–1056 (2014).

Lee, M. K. Understanding perception of algorithmic decisions: fairness, trust, and emotion in response to algorithmic management. Big Data Soc. 5 , https://doi.org/10.1177/2053951718756684 (2018).

Kissinger, H. A., Schmidt, E. & Huttenlocher, D. The Age of A.I. And Our Human Future (Little, Brown, 2021).

Lombrozo, T. Explanatory preferences shape learning and inference. Trends Cogn. Sci. 20 , 748–759 (2016). This paper provides an overview of philosophical theories of explanatory virtues and reviews empirical evidence on the sorts of explanations people find satisfying .

Vrantsidis, T. H. & Lombrozo, T. Simplicity as a cue to probability: multiple roles for simplicity in evaluating explanations. Cogn. Sci. 46 , e13169 (2022).

Johnson, S. G. B., Johnston, A. M., Toig, A. E. & Keil, F. C. Explanatory scope informs causal strength inferences. In Proc. 36th Annual Meeting of the Cognitive Science Society 2453–2458 (Cognitive Science Society, 2014).

Khemlani, S. S., Sussman, A. B. & Oppenheimer, D. M. Harry Potter and the sorcerer’s scope: latent scope biases in explanatory reasoning. Mem. Cognit. 39 , 527–535 (2011).

Liquin, E. G. & Lombrozo, T. Motivated to learn: an account of explanatory satisfaction. Cogn. Psychol. 132 , 101453 (2022).

Hopkins, E. J., Weisberg, D. S. & Taylor, J. C. V. The seductive allure is a reductive allure: people prefer scientific explanations that contain logically irrelevant reductive information. Cognition 155 , 67–76 (2016).

Weisberg, D. S., Hopkins, E. J. & Taylor, J. C. V. People’s explanatory preferences for scientific phenomena. Cogn. Res. Princ. Implic. 3 , 44 (2018).

Jerez-Fernandez, A., Angulo, A. N. & Oppenheimer, D. M. Show me the numbers: precision as a cue to others’ confidence. Psychol. Sci. 25 , 633–635 (2014).

Kim, J., Giroux, M. & Lee, J. C. When do you trust AI? The effect of number presentation detail on consumer trust and acceptance of AI recommendations. Psychol. Mark. 38 , 1140–1155 (2021).

Nguyen, C. T. The seductions of clarity. R. Inst. Philos. Suppl. 89 , 227–255 (2021). This article describes how reductive and quantitative explanations can generate a sense of understanding that is not necessarily correlated with actual understanding .

Fisher, M., Smiley, A. H. & Grillo, T. L. H. Information without knowledge: the effects of internet search on learning. Memory 30 , 375–387 (2022).

Eliseev, E. D. & Marsh, E. J. Understanding why searching the internet inflates confidence in explanatory ability. Appl. Cogn. Psychol. 37 , 711–720 (2023).

Fisher, M. & Oppenheimer, D. M. Who knows what? Knowledge misattribution in the division of cognitive labor. J. Exp. Psychol. Appl. 27 , 292–306 (2021).

Chromik, M., Eiband, M., Buchner, F., Krüger, A. & Butz, A. I think I get your point, AI! The illusion of explanatory depth in explainable AI. In 26th International Conference on Intelligent User Interfaces (eds Hammond, T. et al.) 307–317 (Association for Computing Machinery, 2021).

Strevens, M. No understanding without explanation. Stud. Hist. Philos. Sci. A 44 , 510–515 (2013).

Ylikoski, P. in Scientific Understanding: Philosophical Perspectives (eds De Regt, H. et al.) 100–119 (Univ. Pittsburgh Press, 2009).

Giudice, M. D. The prediction–explanation fallacy: a pervasive problem in scientific applications of machine learning. Preprint at PsyArXiv https://doi.org/10.31234/osf.io/4vq8f (2021).

Hofman, J. M. et al. Integrating explanation and prediction in computational social science. Nature 595 , 181–188 (2021). This paper highlights the advantages and disadvantages of explanatory versus predictive approaches to modelling, with a focus on applications to computational social science .

Shmueli, G. To explain or to predict? Stat. Sci. 25 , 289–310 (2010).

Article   MathSciNet   Google Scholar  

Hofman, J. M., Sharma, A. & Watts, D. J. Prediction and explanation in social systems. Science 355 , 486–488 (2017).

Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: people prefer algorithmic to human judgment. Organ. Behav. Hum. Decis. Process. 151 , 90–103 (2019).

Nguyen, C. T. Cognitive islands and runaway echo chambers: problems for epistemic dependence on experts. Synthese 197 , 2803–2821 (2020).

Breiman, L. Statistical modeling: the two cultures. Stat. Sci. 16 , 199–215 (2001).

Gao, J. & Wang, D. Quantifying the benefit of artificial intelligence for scientific research. Preprint at arxiv.org/abs/2304.10578 (2023).

Hanson, B. et al. Garbage in, garbage out: mitigating risks and maximizing benefits of AI in research. Nature 623 , 28–31 (2023).

Kleinberg, J. & Raghavan, M. Algorithmic monoculture and social welfare. Proc. Natl Acad. Sci. USA 118 , e2018340118 (2021). This paper uses formal modelling methods to demonstrate that when companies all rely on the same algorithm to make decisions (an algorithmic monoculture), the overall quality of those decisions is reduced because valuable options can slip through the cracks, even when the algorithm performs accurately for individual companies .

Article   MathSciNet   CAS   PubMed   PubMed Central   Google Scholar  

Hofstra, B. et al. The diversity–innovation paradox in science. Proc. Natl Acad. Sci. USA 117 , 9284–9291 (2020).

Hong, L. & Page, S. E. Groups of diverse problem solvers can outperform groups of high-ability problem solvers. Proc. Natl Acad. Sci. USA 101 , 16385–16389 (2004).

Page, S. E. Where diversity comes from and why it matters? Eur. J. Soc. Psychol. 44 , 267–279 (2014). This article reviews research demonstrating the benefits of cognitive diversity and diversity in methodological approaches for problem solving and innovation .

Clarke, A. E. & Fujimura, J. H. (eds) The Right Tools for the Job: At Work in Twentieth-Century Life Sciences (Princeton Univ. Press, 2014).

Silva, V. J., Bonacelli, M. B. M. & Pacheco, C. A. Framing the effects of machine learning on science. AI Soc. https://doi.org/10.1007/s00146-022-01515-x (2022).

Sassenberg, K. & Ditrich, L. Research in social psychology changed between 2011 and 2016: larger sample sizes, more self-report measures, and more online studies. Adv. Methods Pract. Psychol. Sci. 2 , 107–114 (2019).

Simon, A. F. & Wilder, D. Methods and measures in social and personality psychology: a comparison of JPSP publications in 1982 and 2016. J. Soc. Psychol. https://doi.org/10.1080/00224545.2022.2135088 (2022).

Anderson, C. A. et al. The MTurkification of social and personality psychology. Pers. Soc. Psychol. Bull. 45 , 842–850 (2019).

Latour, B. in The Social After Gabriel Tarde: Debates and Assessments (ed. Candea, M.) 145–162 (Routledge, 2010).

Porter, T. M. Trust in Numbers: The Pursuit of Objectivity in Science and Public Life (Princeton Univ. Press, 1996).

Lazer, D. et al. Meaningful measures of human society in the twenty-first century. Nature 595 , 189–196 (2021).

Knox, D., Lucas, C. & Cho, W. K. T. Testing causal theories with learned proxies. Annu. Rev. Polit. Sci. 25 , 419–441 (2022).

Barberá, P. Birds of the same feather tweet together: Bayesian ideal point estimation using Twitter data. Polit. Anal. 23 , 76–91 (2015).

Brady, W. J., McLoughlin, K., Doan, T. N. & Crockett, M. J. How social learning amplifies moral outrage expression in online social networks. Sci. Adv. 7 , eabe5641 (2021).

Article   PubMed   PubMed Central   ADS   Google Scholar  

Barnes, J., Klinger, R. & im Walde, S. S. Assessing state-of-the-art sentiment models on state-of-the-art sentiment datasets. In Proc. 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (eds Balahur, A. et al.) 2–12 (Association for Computational Linguistics, 2017).

Gitelman, L. (ed.) “Raw Data” is an Oxymoron (MIT Press, 2013).

Breznau, N. et al. Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty. Proc. Natl Acad. Sci. USA 119 , e2203150119 (2022). This study demonstrates how 73 research teams analysing the same dataset reached different conclusions about the relationship between immigration and public support for social policies, highlighting the subjectivity and uncertainty involved in analysing complex datasets .

Gillespie, T. in Media Technologies: Essays on Communication, Materiality, and Society (eds Gillespie, T. et al.) 167–194 (MIT Press, 2014).

Leonelli, S. Data-Centric Biology: A Philosophical Study (Univ. Chicago Press, 2016).

Wang, A., Kapoor, S., Barocas, S. & Narayanan, A. Against predictive optimization: on the legitimacy of decision-making algorithms that optimize predictive accuracy. ACM J. Responsib. Comput. , https://doi.org/10.1145/3636509 (2023).

Athey, S. Beyond prediction: using big data for policy problems. Science 355 , 483–485 (2017).

del Rosario Martínez-Ordaz, R. Scientific understanding through big data: from ignorance to insights to understanding. Possibility Stud. Soc. 1 , 279–299 (2023).

Nussberger, A.-M., Luo, L., Celis, L. E. & Crockett, M. J. Public attitudes value interpretability but prioritize accuracy in artificial intelligence. Nat. Commun. 13 , 5821 (2022).

Zittrain, J. in The Cambridge Handbook of Responsible Artificial Intelligence: Interdisciplinary Perspectives (eds. Voeneky, S. et al.) 176–184 (Cambridge Univ. Press, 2022). This article articulates the epistemic risks of prioritizing predictive accuracy over explanatory understanding when AI tools are interacting in complex systems.

Shumailov, I. et al. The curse of recursion: training on generated data makes models forget. Preprint at arxiv.org/abs/2305.17493 (2023).

Latour, B. Science In Action: How to Follow Scientists and Engineers Through Society (Harvard Univ. Press, 1987). This book provides strategies and approaches for thinking about science as a social endeavour .

Franklin, S. Science as culture, cultures of science. Annu. Rev. Anthropol. 24 , 163–184 (1995).

Haraway, D. Situated knowledges: the science question in feminism and the privilege of partial perspective. Fem. Stud. 14 , 575–599 (1988). This article acknowledges that the objective ‘view from nowhere’ is unobtainable: knowledge, it argues, is always situated .

Harding, S. Objectivity and Diversity: Another Logic of Scientific Research (Univ. Chicago Press, 2015).

Longino, H. E. Science as Social Knowledge: Values and Objectivity in Scientific Inquiry (Princeton Univ. Press, 1990).

Daston, L. & Galison, P. Objectivity (Princeton Univ. Press, 2007). This book is a historical analysis of the shifting modes of ‘objectivity’ that scientists have pursued, arguing that objectivity is not a universal concept but that it shifts alongside scientific techniques and ambitions .

Prescod-Weinstein, C. Making Black women scientists under white empiricism: the racialization of epistemology in physics. Signs J. Women Cult. Soc. 45 , 421–447 (2020).

Mavhunga, C. What Do Science, Technology, and Innovation Mean From Africa? (MIT Press, 2017).

Schiebinger, L. The Mind Has No Sex? Women in the Origins of Modern Science (Harvard Univ. Press, 1991).

Martin, E. The egg and the sperm: how science has constructed a romance based on stereotypical male–female roles. Signs J. Women Cult. Soc. 16 , 485–501 (1991). This case study shows how assumptions about gender affect scientific theories, sometimes delaying the articulation of what might be considered to be more accurate descriptions of scientific phenomena .

Harding, S. Rethinking standpoint epistemology: What is “strong objectivity”? Centen. Rev. 36 , 437–470 (1992). In this article, Harding outlines her position on ‘strong objectivity’, by which clearly articulating one’s standpoint can lead to more robust knowledge claims .

Oreskes, N. Why Trust Science? (Princeton Univ. Press, 2019). This book introduces the reader to 20 years of scholarship in science and technology studies, arguing that the tools the discipline has for understanding science can help to reinstate public trust in the institution .

Rolin, K., Koskinen, I., Kuorikoski, J. & Reijula, S. Social and cognitive diversity in science: introduction. Synthese 202 , 36 (2023).

Hong, L. & Page, S. E. Problem solving by heterogeneous agents. J. Econ. Theory 97 , 123–163 (2001).

Sulik, J., Bahrami, B. & Deroy, O. The diversity gap: when diversity matters for knowledge. Perspect. Psychol. Sci. 17 , 752–767 (2022).

Lungeanu, A., Whalen, R., Wu, Y. J., DeChurch, L. A. & Contractor, N. S. Diversity, networks, and innovation: a text analytic approach to measuring expertise diversity. Netw. Sci. 11 , 36–64 (2023).

AlShebli, B. K., Rahwan, T. & Woon, W. L. The preeminence of ethnic diversity in scientific collaboration. Nat. Commun. 9 , 5163 (2018).

Campbell, L. G., Mehtani, S., Dozier, M. E. & Rinehart, J. Gender-heterogeneous working groups produce higher quality science. PLoS ONE 8 , e79147 (2013).

Nielsen, M. W., Bloch, C. W. & Schiebinger, L. Making gender diversity work for scientific discovery and innovation. Nat. Hum. Behav. 2 , 726–734 (2018).

Yang, Y., Tian, T. Y., Woodruff, T. K., Jones, B. F. & Uzzi, B. Gender-diverse teams produce more novel and higher-impact scientific ideas. Proc. Natl Acad. Sci. USA 119 , e2200841119 (2022).

Kozlowski, D., Larivière, V., Sugimoto, C. R. & Monroe-White, T. Intersectional inequalities in science. Proc. Natl Acad. Sci. USA 119 , e2113067119 (2022).

Fehr, C. & Jones, J. M. Culture, exploitation, and epistemic approaches to diversity. Synthese 200 , 465 (2022).

Nakadai, R., Nakawake, Y. & Shibasaki, S. AI language tools risk scientific diversity and innovation. Nat. Hum. Behav. 7 , 1804–1805 (2023).

National Academies of Sciences, Engineering, and Medicine et al. Advancing Antiracism, Diversity, Equity, and Inclusion in STEMM Organizations: Beyond Broadening Participation (National Academies Press, 2023).

Winner, L. Do artifacts have politics? Daedalus 109 , 121–136 (1980).

Eubanks, V. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor (St. Martin’s Press, 2018).

Littmann, M. et al. Validity of machine learning in biology and medicine increased through collaborations across fields of expertise. Nat. Mach. Intell. 2 , 18–24 (2020).

Carusi, A. et al. Medical artificial intelligence is as much social as it is technological. Nat. Mach. Intell. 5 , 98–100 (2023).

Raghu, M. & Schmidt, E. A survey of deep learning for scientific discovery. Preprint at arxiv.org/abs/2003.11755 (2020).

Bishop, C. AI4Science to empower the fifth paradigm of scientific discovery. Microsoft Research Blog www.microsoft.com/en-us/research/blog/ai4science-to-empower-the-fifth-paradigm-of-scientific-discovery/ (2022).

Whittaker, M. The steep cost of capture. Interactions 28 , 50–55 (2021).

Liesenfeld, A., Lopez, A. & Dingemanse, M. Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators. In Proc. 5th International Conference on Conversational User Interfaces 1–6 (Association for Computing Machinery, 2023).

Chu, J. S. G. & Evans, J. A. Slowed canonical progress in large fields of science. Proc. Natl Acad. Sci. USA 118 , e2021636118 (2021).

Park, M., Leahey, E. & Funk, R. J. Papers and patents are becoming less disruptive over time. Nature 613 , 138–144 (2023).

Frith, U. Fast lane to slow science. Trends Cogn. Sci. 24 , 1–2 (2020). This article explains the epistemic risks of a hyperfocus on scientific productivity and explores possible avenues for incentivizing the production of higher-quality science on a slower timescale .

Stengers, I. Another Science is Possible: A Manifesto for Slow Science (Wiley, 2018).

Lake, B. M. & Baroni, M. Human-like systematic generalization through a meta-learning neural network. Nature 623 , 115–121 (2023).

Feinman, R. & Lake, B. M. Learning task-general representations with generative neuro-symbolic modeling. Preprint at arxiv.org/abs/2006.14448 (2021).

Schölkopf, B. et al. Toward causal representation learning. Proc. IEEE 109 , 612–634 (2021).

Mitchell, M. AI’s challenge of understanding the world. Science 382 , eadm8175 (2023).

Sartori, L. & Bocca, G. Minding the gap(s): public perceptions of AI and socio-technical imaginaries. AI Soc. 38 , 443–458 (2023).

Download references

Acknowledgements

We thank D. S. Bassett, W. J. Brady, S. Helmreich, S. Kapoor, T. Lombrozo, A. Narayanan, M. Salganik and A. J. te Velthuis for comments. We also thank C. Buckner and P. Winter for their feedback and suggestions.

Author information

These authors contributed equally: Lisa Messeri, M. J. Crockett

Authors and Affiliations

Department of Anthropology, Yale University, New Haven, CT, USA

Lisa Messeri

Department of Psychology, Princeton University, Princeton, NJ, USA

M. J. Crockett

University Center for Human Values, Princeton University, Princeton, NJ, USA

You can also search for this author in PubMed   Google Scholar

Contributions

The authors contributed equally to the research and writing of the paper.

Corresponding authors

Correspondence to Lisa Messeri or M. J. Crockett .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature thanks Cameron Buckner, Peter Winter and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

Messeri, L., Crockett, M.J. Artificial intelligence and illusions of understanding in scientific research. Nature 627 , 49–58 (2024). https://doi.org/10.1038/s41586-024-07146-0

Download citation

Received : 31 July 2023

Accepted : 31 January 2024

Published : 06 March 2024

Issue Date : 07 March 2024

DOI : https://doi.org/10.1038/s41586-024-07146-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Ai is no substitute for having something to say.

Nature Reviews Physics (2024)

Perché gli scienziati si fidano troppo dell'intelligenza artificiale - e come rimediare

Nature Italy (2024)

Why scientists trust AI too much — and what to do about it

Nature (2024)

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Empowering education development through AIGC: A systematic literature review

• Published: 29 February 2024

Cite this article

• Xiaojiao Chen 1 ,
• Zhebing Hu 2 &
• Chengliang Wang   ORCID: orcid.org/0000-0003-2208-3508 3  

485 Accesses

Explore all metrics

As an exemplary representative of AIGC products, ChatGPT has ushered in new possibilities for the field of education. Leveraging its robust text generation and comprehension capabilities, it has had a revolutionary impact on pedagogy, learning experiences, personalized education and other aspects. However, to date, there has been no comprehensive review of AIGC technology’s application in education. In light of this gap, this study employs a systematic literature review and selects 134 relevant publications on AIGC’s educational application from 4 databases: EBSCO, EI Compendex, Scopus, and Web of Science. The study aims to explore the macro development status and future trends in AIGC’s educational application. The following findings emerge: 1) In the AIGC’s educational application field, the United States is the most active country. Theoretical research dominates the research types in this domain; 2) Research on AIGC’s educational application is primarily published in journals and academic conferences in the fields of educational technology and medicine; 3) Research topics primarily focus on five themes: AIGC technology performance assessment, AIGC technology instructional application, AIGC technology enhancing learning outcomes, AIGC technology educational application’s Advantages and Disadvantages analysis, and AIGC technology educational application prospects. 4) Through Grounded Theory, the study delves into the core advantages and potential risks of AIGC’s educational application, deconstructing the scenarios and logic of AIGC’s educational application. 5) Based on a review of existing literature, the study provides valuable future agendas from both theoretical and practical application perspectives. Discussing the future research agenda contributes to clarifying key issues related to the integration of AI and education, promoting more intelligent, effective, and sustainable educational methods and tools, which is of great significance for advancing innovation and development in the field of education.

Similar content being viewed by others

Students’ voices on generative AI: perceptions, benefits, and challenges in higher education

Cecilia Ka Yuk Chan & Wenjie Hu

The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers?

Cecilia Ka Yuk Chan & Katherine K. W. Lee

How should we change teaching and assessment in response to increasingly powerful generative Artificial Intelligence? Outcomes of the ChatGPT teacher survey

Matt Bower, Jodie Torrington, … Mark Alfano

Avoid common mistakes on your manuscript.

1 Introduction

Artificial Intelligence Generated Content (AIGC) refers to the technology that generates relevant content through the use of artificial intelligence techniques such as generative adversarial networks and large pre-trained models, which have the capacity for appropriate generalization through learning and training with existing data (Lee et al., 2020 ). As a concept that has long existed, constrained by algorithmic, data, and hardware and software technological conditions, AIGC was seldom mentioned by the general public in the past. This was because, before the introduction of the Transformer algorithm, traditional neural network algorithms such as Long Short-Term Memory (LSTM) networks and Recurrent Neural Networks (RNN) were the mainstream in natural language processing. However, these algorithms process sequences through recursion, which can lead to issues like gradient vanishing or explosion when dealing with long sequences (Ghorbani et al., 2020 ). This resulted in difficulties for past natural language algorithm models in understanding and outputting long texts, making AIGC not a mainstream focus in artificial intelligence research. However, in November 2022, OpenAI released Chat Generative Pre-trained Transformer (ChatGPT) based on Transformer Algorithmic. ChatGPT made significant breakthroughs in algorithms, data, and computing power, showcasing the potential of the latest advancements in AI for natural language processing. Thanks to this, AIGC quickly garnered global attention (Grant & Metz, 2022 ).

As a typical representative and pioneer of AIGC, ChatGPT has received mixed evaluations from the public and academia since its release (Rasul et al., 2023 ). On one hand, its outstanding natural language processing and code generation capabilities have garnered significant praise, making it a powerful tool for content creation (Zhu et al., 2023 ; Smith et al., 2023 ), interactive learning (Tsang, 2023 ), and knowledge dissemination (Escotet, 2023 ). On the other hand, it can effortlessly generate seemingly authentic information (Esplugas, 2023 ), leading to deeper concerns about its potential for misuse (Currie, 2023 ), the spread of misinformation (Heng et al., 2023 ; Hsu & Ching, 2023 ), and ethical implications (Ahmed, 2023 ; Busch et al., 2023 ).

The breakthrough and development of the new technology have brought profound transformations to many fields, with the field of education being one of the most deeply affected (Alqahtani et al., 2023 ). Many scholars have focused on the role of AIGC in empowering education, exploring its contributions in providing educational resources, improving learning experiences, and enhancing educational effectiveness, while also analyzing potential academic misconduct and integrity issues (Kasneci et al., 2023 ; Jalil et al., 2023 ). It is evident that, through ChatGPT as a starting point, the development and deployment of AIGC’s educational application will be a critical point where technology intersects with society. Its continued improvement may determine whether it serves as a positive force for innovation or a cautionary tale about unchecked technological progress.

The empowerment of education through AIGC technology has led to more personalized and adaptive teaching and learning, and many researchers believe that AIGC’s educational application has vast potential. However, constrained by subjective biases, technical reliability and security, adaptability of educational content, as well as cultural and ethical concerns, AIGC encounters obstacles in its application within the field of education. Currently, the question of “how to effectively incorporate AI technology, represented by ChatGPT, into the field of education” is still in its early exploration stage (Alnaqbi & Fouda, 2023 ). Therefore, it is essential to provide a reasonable assessment and rational judgment of current research in the AIGC’s educational application domain. Based on a comprehensive understanding of the state of research in this field, the level of methodological systematization, and the organization of content structures, this study employs a systematic literature review to organize, summarize, and analyze existing research on AIGC’s empowerment of education. It extracts the core advantages and potential risks of applying AIGC products in education, further indicating promising directions for development and insightful constructive recommendations. Specifically, this study addresses the following research questions:

RQ1: What are the current overall trends in the development in the field of AIGC empowerment in education?

RQ2: What are the core research topics in the field of AIGC empowerment in education?

RQ3: What are the core advantages and potential risks of AIGC empowerment in education?

RQ4: Based on the review of existing literature, what valuable future research agendas exist in the field of AIGC’s educational application?

2 Literature review

2.1 overview of conversational chatbot empowering education.

Conversational chatbot, as the precursor to generative chatbots, has long garnered attention from numerous scholars due to its role of empowering education. Before the emergence of ChatGPT, typical representatives of Conversational chatbot included Google Assistant, Cortana, Siri, among others. These Conversational chatbot systems have exhibited substantial potential in the field of education. Specifically, they play a significant role in assisting students in learning (Deng & Yu, 2023 ) and enhancing the learning experience (Sandu & Gide, 2019 ). Furthermore, Conversational chatbot can take on roles, actively interact with students, provide instant answers and guidance, thereby enhancing the interactivity and engagement in the learning process (Zhang et al., 2023a , b ; Hwang & Chang, 2021 ).

Thanks to the continuous evolution and rapid development of technology, the current landscape of Conversational chatbot in education is continually evolving (Deng & Yu, 2023 ). Given that human-computer interaction has always been a crucial component of technology-empowered education, scholars in the field of educational technology have been actively incorporating these technologies into the realm of education (Hadi & Junor, 2022 ; Kepuska & Bohouta, 2018 ). Through ongoing experimentation, the application of Conversational chatbot has become increasingly diverse, expanding into various domains (Kılıçkaya, 2020 ; Lee & Yeo, 2022 ; Kohnke, 2022 ; Zhang et al., 2023a , b ). Currently, scholars are implementing Conversational chatbot in real educational settings to assess its concrete educational value and discuss how to maximize its potential while addressing potential adverse effects (Mokmin & Ibrahim, 2021 ).

In summary, over the past five years, Conversational chatbot has remained dynamic in the realm of empowering education, providing extensive prospects for innovation in the field of education. However, it is noteworthy that more researchers are recognizing the limitations of Conversational chatbot (Tam et al., 2023 ). While technological advancements have lowered the costs and increased the convenience of human-computer interaction, Conversational chatbot has certainly demonstrated its capabilities in addressing simple problems and constructing interactive and adaptive learning modes (Ahmed, 2023 ). Nevertheless, when confronted with the complexities of solving intricate problems and generating long-form text, past Conversational chatbot systems have consistently fallen short. This represents an insurmountable gap in the development of Conversational chatbot, perpetually constrained by technological limitations.

2.2 Development history and educational application of generative Chatbots

As a representative of generative artificial intelligence, with its outstanding text comprehension ability, long-form writing ability, and programming ability, ChatGPT has sparked a craze in the field of education since its release by OpenAI in November 2022 (Chang et al., 2022 ; Deng & Yu, 2023 ). Various scholars have explored the application value of AIGC in the field of education from different perspectives. The application includes enhancing creativity and critical thinking using ChatGPT (Seetharaman, 2023 ), improving second-language learning efficiency (Yan, 2023 ), and enhancing the quality of engineering education (Sánchez-Ruiz et al., 2023 ). Beyond its advantages, researchers have also raised concerns about the potential risks of AIGC’s educational application. These concerns encompass ethical and moral issues (Esplugas, 2023 ), transparency and legal matters (Currie, 2023 ), bias risks (Mohammad et al., 2023 ), plagiarism (Cross et al., 2023 ), and the lack of originality (Sallam, 2023a , b ). It is imperative to recognize that any cutting-edge technology is a double-edged sword. The assessment of potential risks and core advantages of AIGC requires ongoing exploration by researchers. Only by viewing the impact of AIGC on education from a developmental perspective, and actively mitigating potential risks while maximizing its potential in empowering education, can AIGC truly empower education (Lodge et al., 2023 ).

From the overall assessment of current research, it is evident that most studies centralizing on AIGC’s educational application primarily consist of theoretical discussions. They focus on dissecting the basic functionalities and underlying mechanisms of ChatGPT (Rahimzadeh et al., 2023 ) and analyzing the educational application scenarios and future challenges of ChatGPT (Li et al., 2023 ; Banić et al., 2023 ). Moreover, they also make predictions and take preventive measures regarding the ethical concerns and potential risks associated with ChatGPT (Jeon & Lee, 2023 ; Jalil et al., 2023 ; Choi et al., 2023 ; Siegle, 2023 ). These observations highlight two prevailing issues in the current research landscape. Firstly, there is limited attention from scholars towards AIGC technology other than ChatGPT, such as Google Assistant and Microsoft Cortana. Secondly, there is a scarcity of empirical research focusing on teaching practices and the learning process. There has been limited exploration of the value of AIGC technology application from a practical perspective.

The introduction and application of new technology should not be limited to the theoretical realm; they should undergo validation and practical implementation in real educational environments. To better comprehend and address key issues in current research, and to pave the way for the deep integration of AIGC and education in the future, this study employs a systematic literature review (SLR) methodology. The aim is to comprehensively present and deeply analyze the current state, overview, and key points of AIGC-assisted education-related research. This will help establish a clear research framework in the field of artificial intelligence-assisted education, summarize the experience and lessons learned from existing research, and provide valuable insights for future exploration in a broader educational application domain. This approach not only contributes to a better understanding of the role of new technology in education but also offers a specific and clear path for the organic integration of educational practice and technological innovation.

3 Methodology and materials

3.1 methodology.

This study aims to systematically explore the current state of research in the field of AIGC’s educational application, striving to understand the research trends in this area. By analyzing the core advantages and potential risks of AIGC’s educational application, the study seeks to predict valuable future research topics. To accomplish this, the research employs the Systematic Literature Review (SLR) method, which accurately defines research questions, comprehensively retrieves literature, establishes clear criteria, and employs high-quality assessment methods, enabling the broadest and most effective integration of existing research (Diekemper et al., 2015 ; Paul et al., 2021 ).

SLR overcomes the uncontrollable problems such as subjectivity and bias issues present in traditional review methods (Gough et al., 2017 ) and provides a standardized screening analytical paradigm for understanding the current state and evolutionary trends of a research field or topic (Jing et al., 2023 ). Moreover, SLR’s practical logic aligns well with the practical value of social science research, particularly as an evaluation tool that includes qualitative research literature evaluation indicators (Shlonsky et al., 2011 ; Deacon et al., 2023 ). The field of AIGC empowering education empowers a substantial amount of research in the social sciences, making SLR suitable for this study’s needs.

In the practical process, this study first conducted literature selection through the standardized PRISMA selection process (Moher et al., 2009 ; Lin & Lan, 2015 ). It then established a coding system to collect structured coding information (such as publication statistics, journals, research types, etc.) for structured information and textual coding information (such as discussions on the core advantages and potential risks of AIGC technology) for non-structured information. This study statistically and visually presented structured information and used qualitative text analysis for non-structured text coding. The specific practical logic is shown in Fig.  1 .

Overview practical logic of research

3.2 Initial literature search

According to the PRISMA guidelines, SLR generally requires three or more databases as sources of literature (Moher et al., 2009 ). To comprehensively obtain the required literature data for this study, we drew inspiration from existing SLR studies in the field of educational technology (Radianti et al., 2020 ; Luo et al., 2021 ) and selected four English literature databases: EBSCO, Ei Compendex, Scopus, and Web of Science. These databases widely cover leading journals in the field of education, and their inclusion criteria ensure that the literature within them largely meets the quality requirements of SLR.

Regarding search conditions, this study referenced several sets of keywords from existing literature in the field of AIGC and established appropriate keywords. The final thematic search query for Web of Science, for example, was: TS = ((“Artificial Intelligence Generated Content” OR “AIGC” OR “Large Language Model*” OR “ChatGPT”) AND (“education” OR “teach*” OR “learn*”)). Since ChatGPT was released by OpenAI in November 2022, the search timeframe was set from November 2022 to August 2023, with the search cutoff date being August 10, 2023. After the initial search, 2243 papers were retrieved from the 4 databases with deduplicating (the amount of literature after the input search strategies of the 4 databases is visible in Fig.  2 .

PRISMA flowchart for including studies to review

3.3 Manual screening

The literature retrieved in the initial search often includes articles that appear relevant but are not. Therefore, to ensure that the selected articles are relevant to the research topic, a manual screening process is essential. To efficiently and systematically conduct manual screening and ensure the quality of the selected articles, the research team established the following selection criteria (see Table  1 ).

The manual screening process consisted of two steps. The first step involved excluding completely irrelevant literature based on titles and abstracts. Two members of the research team participated in this step, which took about three weeks, screening the 4 databases and deduplicating, resulting in 234 retained papers. The second step involved reading the full text to precisely identify the literature required for this study. Three members of the research team participated in this step, which took about two weeks, ultimately retaining 134 papers from the 4 databases (the complete literature screening process is shown in Fig. 2 ).

3.4 Analytical coding

After completing the manual screening and finalizing the selected articles, we conducted a full-text reading of the chosen papers. Since literature is categorized into Article & Review types (see Appendix Table  8 ) and Other types (including Editorial, Perspective, Letter, and Note, see Appendix Table  9 ), we established coding standards for these two types of literature.

First, for Article & Review type papers, while coding we collected detailed information in four categories: metadata, methodology information, potential risks of AIGC, and core advantages of AIGC. For coding other types of literature (including Editorial, Perspective, Letter and Note), we collected information in three categories to code: metadata, concerns about AIGC’s risks, and core advantages of AIGC. Table  2 presents this coding scheme and provides a brief description of what is coded for each category.

Upon designing the coding system, two researchers conducted the coding independently to ensure the study’s reliability (Gaur & Kumar, 2018 ). In cases of disputes during the coding process, the remaining team members participated in discussions to resolve inconsistencies. For more contentious coding issues, consultations with authoritative scholars in the field were sought. The coding process took two weeks.

4 Performance analysis (RQ1)

Performance analysis is essential for obtaining foundational information on a specific topic from a macro perspective, aiming to objectively present the development trends and directions in the discipline. This study will explore quantitative data related to the year distribution, the research type, the research method, the affiliated journal, and core productivity at the national level for the selected analysis articles, with the goal of presenting the research development trends in this field from a quantitative perspective.

4.1 Year distribution, research type and research method

This study initially conducted a statistical analysis of the publication years of the selected articles, the result of which is shown in Fig.  3 . From the figure, it is evident that after the release of ChatGPT, research on AIGC’s educational application rapidly became a hot topic, experiencing a surge in publications. Some journals, such as ECNU (East China Normal University) Review of Education (This is an educational journal published by the East China Normal University Press and Sage Publications, which has now been included in databases such as the Scopus database and the Web of Science’s Emerging Sources Citation Index (ESCI).) and TechTrends , issued calls for papers on the subject of AIGC’s educational application.

Publication year distribution of selected articles

A statistical analysis was performed on the literature types. Among the 134 papers included in this study, there were 106 articles of the Article & Review type, including 101 Original Research articles and 5 Review Articles. Additionally, there were 28 papers of other types, such as Editorial, Perspective, Letter and Note.

Further analysis was conducted on the 101 Original Research papers to classify research types. Based on the definitions used during coding, research types were divided into five categories: quantitative research, qualitative research, mixed research, case studies, and theoretical research. The results are shown in Fig.  4 . From Fig. 4 , it is apparent that in the existing research in this field, theoretical research papers are the most common, totaling 65. Quantitative research comes second with 28 papers. This indicates that research on AIGC in the field of education is primarily theoretical and quantitative. In addition, there are a few qualitative studies (5 papers), case studies (2 papers), and mixed research (1 paper). While classifying literature, because quantitative, qualitative, and mixed research methods share some common elements, this study followed Johnson and Onwuegbuzie’s (Johnson & Onwuegbuzie, 2004 ) definition of mixed research. Only when a study has a similar balance of quantitative and qualitative research components is it considered mixed research.

Research types of article-type literature

The research also performed a detailed statistical analysis of specific research methods used in the literature of quantitative research, qualitative research, and mixed research (a total of 34 papers), as shown in Fig.  5 .

Research methods of article-type literature

Note: The “Survey method” option refers only to Questionnaire. Methods based on questionnaires, such as Structural Equation Model, are not included in the “Survey method” option.

According to the statistical results, the literature assessing AIGC technology’s performance is the most numerous, totaling 17 papers. For instance, some researchers conducted experimental studies on ChatGPT’s performance in the United States Medical Licensing Examination (USMLE) (Kung et al., 2023 ). In terms of quantitative research methods, many existing studies employed experimental research. For example, scholars used ChatGPT as a subject and conducted quantitative research by comparing its writing abilities with other learners in the experiment. In the study, ChatGPT and learner subjects were given same writing-related tasks from university courses to assess ChatGPT’s writing abilities. The results showed that while ChatGPT initially performed well, it still fell short of human learner subjects. As the complexity of the questions increased, ChatGPT’s answers gradually lacked depth, breadth, and insight, leading to an increasing gap with learner subjects.

In qualitative research, interview-based research methods were also utilized. Through interview analysis, one can gain a more objective understanding of learners’ or teachers’ adoption intention towards AIGC as a new educational method. It can also be used to assess the risks associated with AIGC, allowing for better mitigation of its drawbacks. Furthermore, in mixed research, experimental and questionnaire methods were used to determine the skills required for teachers to prepare course content using chatbots (Kerneža, 2023 ). In the context of the widespread application of AIGC technology, research methods that combine Structural Equation Model and Fuzzy-Set Qualitative Comparative Analysis have emerged to explore the adoption intention towards AIGC (Strzelecki, 2023 ; Foroughi et al., 2023 ). These different research methods collectively explored the value, content, and significance of the research topic on AIGC’s educational application from various cognitive perspectives and practical dimensions.

4.2 Source and country

In the development of any research field, academic journals and conferences play an indispensable role. Serving as the primary vehicles for academic research, academic journals and conferences carry the crucial mission of facilitating scholarly exchange and advancing the frontiers of the subject. This study conducted a statistical analysis of the journals to which the selected articles belong and found that out of 134 articles, they originated from a total of 98 sources. Figure  6 presents the top 20 journals and conferences in terms of publication volume. From the figure, it is evident that these journals primarily pertain to the fields of medical education and educational technology. This indicates that the fields of medical education and educational technology are particularly attentive to this research area. In fact, even before the emergence of AI technology represented by ChatGPT, these fields had already shown a substantial interest in the integration of Artificial Intelligence into education.

Journals affiliated with selected articles

To further analyze the distribution of productivity at the national level in this research field, we used the country of affiliation of the first author as our statistical basis. We found that out of the 106 articles, they originated from 32 different countries. The United States emerges as the primary contributor to current research on AIGC’s impact on education, with 27 publications. Additionally, countries like Australia (11 publications), China (10 publications), Canada (6 publications), and Germany (5 publications) have made significant contributions. Given that OpenAI is a U.S.-based company and ChatGPT is a prominent native product of the United States, the unquestionable leadership of the United States in this research topic is evident.

5 Thematic analysis (RQ2)

The exploration of research topics holds significant value in the development of academic disciplines. Accurately identifying the pivotal topics under investigation within a research domain helps researchers gain a better understanding of the current landscape and make reasonable predictions about the field’s future trajectory. This study conducted a thematic categorization of the 106 selected Articles & Reviews, resulting in five primary themes, as presented in Table  3 .

Based on the results presented in Table 3 , it is evident that five research topics have been broadly covered. These topics encompass various aspects, including a technological performance perspective (Topic 1), the viewpoint of technology users (Topics 2 and 3), and the potential impacts of technology in educational application (Topics 4 and 5). From these topics, it becomes apparent that researchers are actively exploring the influence, prospects, and challenges associated with AIGC technology in the realm of education, particularly concerning the significant interaction between AIGC technology and primary participants in education -- educators and learners, which also becomes a prominent focal point in current research.

5.1 Evaluation of AIGC technology’s performance

The assessment of an emerging technology inherently involves testing its performance across various scenarios. Therefore, measuring the performance of AIGC technology is a crucial issue in this field. When ChatGPT technology first emerged, scholars from various domains attempted to assess AIGC technology by posing specialized questions in their respective research areas. Consequently, these issues span a broad spectrum, encompassing problem-solving abilities in educational practice (Tsang, 2023 ), as well as practical performance in clinical settings (Allen et al., 2021 ).

The evaluation of AIGC technology’s performance is not a one-dimensional and straightforward matter, as this process often involves assessing the logicality of responses, and completeness and relevance of information. For instance, in the realm of education, scholars conducted experiments to analyze AIGC’s capability in addressing microbiological questions (Das et al., 2023 ). Furthermore, research delved into how AIGC products respond to questions related to scientific education (Cooper, 2023 ), effectively examining AIGC technology’s role in enhancing educational instruction. Additionally, by assessing the accuracy and relevance of AIGC-generated content, it also aids in standardizing the use of artificially generated content in medical education (Karabacak et al., 2023 ).

5.2 AIGC technology in educational instruction

As crucial participants in the educational process, teachers have become a central focus of research concerning how to effectively harness AIGC technology. Scholars in this research topic have concentrated on examining teachers’ perspectives on AIGC as a pedagogical tool and how educators can make the most of AIGC technology in scientific education (Cooper, 2023 ). For example, one study surveyed ten teachers’ perceptions of AIGC technology’s effectiveness in supporting students’ English language learning, highlighting AIGC as a valuable supplement and enhancement to traditional language teaching methods (Mohamed, 2023 ).

In addition to investigating teachers’ perceptions of AIGC technology, a vital focus in this field of study is how to coordinate the relationship between teachers and AIGC technology. Some studies delve into the dynamics between AIGC technology and teachers, emphasizing their complementary roles in the educational domain (Jeon & Lee, 2023 ). Given AIGC technology’s status as an emerging intervention, teachers must acquire new skills and adopt innovative teaching methods. Therefore, certain studies aim to identify the skills teachers need for incorporating chatbots into course content (Kerneža, 2023 ) and how medical educators can develop skills and curricula to better utilize this innovative technology (Heng et al., 2023 ).

5.3 Enhancing learning performance with AIGC technology

From a learner’s perspective, the paramount value of technological innovation lies in its potential to improve learning performance. Hence, the focal point of this research topic predominantly centers on the interaction between learners and AIGC technology, aiming to explore the mechanisms through which AIGC technology enhances learning outcomes, thus introducing innovative disruptions to traditional modes of learning. In this field, scholars investigate not only how AIGC assists learners in enhancing various skills, such as strengthening programming abilities (Rahman & Watanobe, 2023 ) and acquiring domain knowledge (Seetharaman, 2023 ), among others.

Another key concern within this research topic is how to maximize the advantages of AIGC technology to innovate teaching methods, allowing students to better achieve their learning goals and adapt learning strategies for improved learning outcomes. For instance, some research demonstrates how AIGC simplifies interactive content creation in virtual immersive learning environments like Roblox and assesses the effectiveness of these experiences in engaging students and enhancing their understanding of e-learning (Ho & Lee, 2023 ). Furthermore, there is research utilizing ChatGPT as a real-time feedback tool in teaching (Alnaqbi & Fouda, 2023 ) to enable personalized teaching and learning.

5.4 Analysis of pros and cons in AIGC technology’s educational application

This research topic primarily focuses on the analysis of the strengths and weaknesses of AIGC technology in educational teaching activities. The perspective on the advantages of AIGC technology is highly diverse. From the learner’s viewpoint, it enables personalized learning, while from the perspective of educational administrators and leaders, it facilitates resource management in higher education. Some scholars also point out the outstanding advantages of AIGC technology in the tangible field of medical practice (Heng et al., 2023 ). However, the realization of these advantages depends on how teachers and learners harness this innovative force. It should be noted that the advantages of AIGC technology are not fixed, and further technological development will continue to amplify most of these advantages. Therefore, comprehending the logic and overall trends in AIGC technology development is as valuable as understanding its inherent advantages (Abdelghani et al., 2023 ).

Another key focus in this topic is the contemplation of potential risks and limitations of AIGC technology. AIGC technology exhibits inherent technological disadvantages in areas such as ethics and privacy issues (Mohamed, 2023 ), plagiarism and academic integrity (Teel et al., 2023 ), and information accuracy (Heng et al., 2023 ). For users, recognizing the pros and cons of AIGC technology is a crucial prerequisite for its correct use. For educational leaders and government bodies, analyzing the pros and cons comprehensively offers guidance for a more thorough evaluation of the prospects of AIGC technology in education, guiding its effective integration and optimization.

5.5 Prospects of AIGC technology’s educational application

This research topic places a strong emphasis on studying the future development prospects and challenges of AIGC technology in the field of education, encompassing a wide range of domains and topics. Differing from the previous research topic that analyzed the advantages and disadvantages of AIGC technology based on practical application, this topic primarily stands on the foundation of existing practices and forecasts and anticipates the value of AIGC’s deep integration into education in the future. For example, some studies conduct comprehensive analyses of the application patterns of AIGC technology in higher education and the potential benefits that widespread adoption could bring (Pinto et al., 2023 ; Killian et al., 2023 ). Other studies focus on the potential value of AIGC technology in specific domains, such as mathematics tutoring, and explore the possibilities of developing hybrid human-AI tutoring and teaching systems (Patel et al., 2023 ; Holmes & Kay, 2023 ).

Furthermore, scholars employ a forward-looking perspective to envision the potential transformative impact of AIGC technology on the future of the education sector. Some studies take a theoretical approach to engage in profound speculation, with the aim of laying the groundwork for the coexistence of AIGC as a revolutionary educational resource in the future (Lim et al., 2023 ). These studies illustrate the high expectations that scholars hold for the potential empowerment of AIGC technology in the field of education.

6 Grounded analysis for advantages and risks (RQ3)

Based on the review and analysis of the selected articles, the discussion and analysis of the core advantages and potential risks of AIGC’s educational application have consistently been a focal point for researchers. Many studies have deconstructed the inherent features and attributes of AIGC technology from various perspectives, evaluating its diverse application contexts in education, and further analyzing potential advantages and risks. These discussions concerning the core advantages and potential risks of AIGC exhibit intersections, yet currently, there is no systematic integration of these analytical discussions.

The discourse on the core advantages and potential risks of AIGC technology is primarily qualitative text data, presented in diverse ways with a variety of perspectives. Quantitative statistical methods are challenging to apply in this context. Therefore, this study employed a Grounded Theory approach for analysis. Grounded Theory is a method of theory generation that involves inductive analysis of qualitative data to find the subject or type and has found widespread application in the social sciences. Its main analytical methods include concept extraction and coding analysis for extracting key concepts and conducting in-depth data analysis from textual materials (Charmaz & Thornberg, 2021 ; Glaser & Strauss, 2017 ). This study followed the steps of the Grounded Theory approach and, with the assistance of Nvivo 11.0 software, conducted inductive coding for perceptions of risks and core advantages of AIGC in educational practices based on the selected articles.

6.1 Open coding

Open coding is a primary level of coding in the Grounded Theory. During the process of open coding, it is essential to maintain an open attitude, analyzing and coding materials word by word and sentence by sentence. Researchers need to extensively explore the data to extract as many concepts as possible (Glaser & Strauss, 2017 ). Furthermore, it is advisable to use verbatim language for naming initial concepts to maintain accuracy and authenticity. Additionally, during the coding process, similar or repetitive concepts should be compared and merged until the coding reaches a saturation point, where no new concepts emerge. This approach ensures that we extract as much information as possible from the interview materials and effectively organize and analyze this information.

In terms of core advantages, based on coding analysis, we ultimately extracted 29 initial concepts from the articles, including virtual teaching assistants, overall learning experiences, outstanding comprehension abilities, and the provision of specific topic information and resources. Building upon these initial concepts, we further synthesized 15 initial categories covering elements such as teaching assistants, learning experiences, feedback and guidance, personalized learning, enhancing comprehension, learning tools, processing capabilities, broad application, knowledge and skill provision, human-computer interaction, resource provision, educational enhancement, and assessment capabilities. The partial results of open coding are displayed in Table  4 , while the complete open coding results for core advantages can be obtained in Appendix Table  10 , and these categories collectively constitute the core advantages discussed in existing research regarding AIGC’s educational application.

Discussion of the core advantages of AIGC inevitably leads scholars to consider its application risks. After refining and initial coding of potential risk dimensions extracted from the selected articles, 31 initial concepts were ultimately synthesized, including concepts such as academic dishonesty, exam and assignment cheating, risks of bias and discrimination, data leakage, and others. Building upon these initial concepts, this study further distilled 15 initial categories, namely unethical behavior, misinformation, integrity concerns, limited knowledge, accuracy and reliability, overreliance, ethical and moral issues, plagiarism, cybersecurity concerns, information dissemination risks, privacy issues, misuse concerns, constraining creativity, technological monopolies, and emotional interactions. The partial open coding of potential risks is shown in Table  5 , while the complete open coding of potential risks is presented in Appendix Table  11 These 15 categories represent potential risks associated with AIGC’s educational application.

6.2 Axial coding

In the second-level coding stage, known as axial coding, a relationship analysis of the initial concepts synthesized during the first-level coding is conducted. This involves classifying and understanding the inherent connections and logical sequences between coding statements in the original materials to establish relationships between core categories and initial categories. Through axial analysis, researchers can delve deeper into the connections among various codes and better grasp the overall research framework (Glaser & Strauss, 2017 ). This study analyzed, summarized, and presented two core categories tables for the 15 initial categories associated with both core advantages and potential risks acquired in open coding.

The core categories for core advantages and their conceptual connotation are partly presented in Table  6 , while the complete axial coding of core advantages is presented in Appendix Table  12 . After further summarizing the initial categories, five core categories were derived: teaching assistance, learning facilitation, personalized feedback, exceptional performance, and convenient interaction. The relationship implications of these core categories capture the core advantages of AIGC from various angles, laying the foundation for a better understanding of the existing research directions pertaining to AIGC’s empowerment of education.

As for the initial categories of potential risks, Table  7 presents the results of axial coding for the partial aspects of potential risks (complete coding results can be obtained from Appendix Table  13 ). After summarization, five core categories were derived: product risks, ethical challenges, privacy risks, technological monopolies, and difficulties in emotional interaction. These core categories not only represent independent concepts but also capture the relationships and implications from various angles, providing a comprehensive synthesis of the potential risks associated with AIGC’s educational application. This level of analysis can assist in exploring the potential risks of AIGC in the field of education more deeply, offering robust support and guidance for mitigating risks in future AIGC application.

6.3 Selective coding: AIGC’s educational application scenarios and logic

Building upon the results of encoding core advantages and potential risks using the Grounded Theory approach, this study further delineates the underlying mechanism and logical framework of AIGC’s educational application, as illustrated in Fig.  7 .

The Underlying Mechanism and Logical Framework of AIGC’s Educational Application

This framework model consists of two pathways and four levels. The two pathways are the Core Advantages Pathway and the Potential Risks Pathway, while the four levels include the Technological Mechanism Level, the Attribute Level, the Teaching Model Level, and the Scenario Application Level. At the Technological Mechanism Level, given that any technology is a double-edged sword, AIGC technology is no exception, and its core advantages and potential risks originate from the underlying logic of the technology itself. At the Attribute Level, the core advantages and potential risks of AIGC are associated with the characteristic features of AIGC, providing a unique perspective on the logic behind their generation. At the Teaching Model Level, the framework model, based on a review of existing literature, provides a series of model methods that can harness AIGC technology’s core advantages and mitigate its potential risks. For instance, the thoughtful use of teaching models such as “personalized learning” and “adaptive learning” can effectively leverage the advantages of AIGC technology in teaching and overcome its drawbacks. At the top level is the Scenario Application Level, where theoretical frameworks must ultimately translate into practical application. Based on the results of a review of existing literature, it is evident that providing concrete examples and channels for educational technology application is crucial to harnessing technological advantages and mitigating technological risks. Recognizing the fundamental value of education is a key aspect of promoting the integration of AIGC technology into the current education system.

Considering the logic across these four levels, starting from the inherent attributes of the technology, leveraging the core advantages and potential risks in the educational context, and culminating in real learning scenarios, it is clear that empowering education with AIGC technology requires researchers to both emphasize the value of AIGC application, focusing on the essence of education, and avoid approaching AIGC application risks rationally. This includes acknowledging the inherent drawbacks of technology. In the process of building an educational system and ecosystem that integrates AIGC, this approach can help educators and learners better utilize AIGC technology, ultimately propelling education towards a direction that is more in line with the demands of the new era and technological advancements.

7 Future agenda (RQ4)

Presenting a valuable future agenda is a crucial mission undertaken by systematic literature reviews, and its importance has been demonstrated in previous practice of systematic literature reviews (Prikshat et al., 2023 ; Pentina et al., 2023 ; Ancillai et al., 2023 ; Mariani et al., 2023 ). Based on the review of existing literature, this study identifies research topics that are worth further exploration in the field and highlights the weaknesses and shortcomings in the existing research, which will significantly benefit the future development of this research area.

This study will provide a systematic presentation of the future agenda from both theoretical research and practical application perspectives. Firstly, on the theoretical research level, through a deep analysis of existing theoretical and empirical studies on AIGC, this research offers several future research topics related to leveraging AIGC’s core advantages in educational contexts and mitigating its potential risks (Section 7.1.1 & 7.1.2 ). Secondly, on the practical application level, this study points out two valuable future agendas in AIGC technology educational application: the development of specialized AIGC educational products and the challenges and solutions that AIGC technology adoption might face. The overall logic and interrelationships of the future agenda are depicted in Fig.  8 .

The Macro Logic of the Future Agenda

7.1 Research agenda at the theoretical level

7.1.1 harness aigc core advantages.

Fully exploring the core advantages of AIGC technology is a critical pathway for future research. This pathway not only drives innovation and progress in the field of education but also holds the promise of broader benefits for students, teachers, and educational systems. Therefore, in-depth research and development of the application potential of AIGC in education and its related multidisciplinary areas will play a crucial role in future research. Within this research direction, there are several future agendas worth further exploring.

Firstly, to build a diverse communication AIGC-assisted teaching system. Based on the multi-modal communication of human-machine-human interactions that has emerged with AIGC in education, a new communication mode has been established, offering a fresh logic for educational advancement. This mode presents new challenges and requirements for teachers’ professionalism, particularly in generating content in real-time during multi-modal dialogues and guiding students in conversing with machines. In this process of multi-modal communication, knowledge exchange occurs in both directions. Teachers can cleverly impart their rich teaching experience to AI, and conversely, AI assistants, through interactions with teachers and students, stimulate continuous improvement in the abilities of both teachers and students, realizing their self-improvement.

Secondly, to enhance the educational assessment and feedback mechanism based on AIGC technology. The key to successful educational assessment is using formative feedback (Schmulian & Coetzee, 2019 ), and technology in educational assessment can enhance learning during the assessment and feedback process (Deeley, 2018 ). Therefore, designing appropriate assessment and feedback mechanisms, analyzing students’ expressions and feedback in interactions, providing insights into students’ emotions and understanding for teachers, and helping educators gain a more comprehensive understanding of students’ learning status are crucial. Additionally, technology can also be creative in designing innovative assessment methods, such as language interactive tests, to gain a more accurate understanding of students’ knowledge levels and abilities.

Thirdly, to establish AIGC-driven personalized interactive education. Education approaches driven by the concept of personalized learning are understood as a set of different organizational, curricular, and teaching strategies aimed at promoting and enhancing the meaning students attach to their school learning (Engel & Coll, 2022 ). AIGC can simulate the role of a teacher, engaging in real-time interactions with students, addressing doubts, sparking critical thinking, and promoting deep learning. In the realm of personalized learning, AIGC can also tailor learning materials and methods based on students’ interests, learning styles, and progress, providing a personalized learning experience to better meet students’ needs. Interactive teaching is a significant advantage of AIGC. Future research can further explore the potential of AI in dialogue generation and emotion recognition, allowing AIGC to engage in more profound thought exchange, discussion, and guidance in educational teaching, going beyond mere question answering and assistance.

7.1.2 Mitigate potential AIGC risks

In addition to harnessing the advantages of AIGC technology, mitigating potential risks should also be a significant focus of future research. In this research direction, there are numerous agendas that can contribute to ensuring the safe and effective use of AIGC technology in the field of education. It is essential to delve into the potential challenges and risks that AIGC technology might pose to ensure the interests of students, teachers, and the entire education system are not compromised. In this crucial area, several research agendas worth further exploration can help minimize the negative impacts of AIGC technology and maximize its potential utilization.

Firstly, to standardize and strengthen the regulation of AIGC technology educational application. As AIGC becomes integrated into our educational activities, the capacity for regulation and standardization becomes vital (Shoja et al., 2023 ). To regulate the use of AI effectively, government, academia, and industry should collaborate to develop relevant laws and industry standards. These regulations should cover the development, deployment, and operation of AI systems, specifying requirements related to privacy protection, data security, algorithm fairness, and more. Furthermore, effective regulatory mechanisms should be established to ensure that the application of AI technology does not harm individual rights and public interests. Future research can also focus on developing more intelligent methods of regulation. Traditional regulations are often based on static rules and processes, but the complexity and dynamics of AI systems render conventional regulatory methods inadequate. Therefore, the adoption of data-driven regulatory approaches can be considered, involving real-time monitoring and analysis of AI system operation data to identify issues promptly and take appropriate measures. Additionally, exploring the introduction of autonomous regulatory mechanisms can empower AI systems to self-adjust and optimize their behavior to conform to preset norms and standards.

Secondly, to cultivate learners’ AI literacy and ethical values of AI application. As AI application in various fields becomes increasingly pervasive, the ethical issues involved also become more complex and sensitive. If people gradually lose their ability to think critically and become entirely reliant on AIGC recommendations and guidance, they risk becoming “slaves” to AI. This behavior has already been evident in the early stages of AIGC integration into education, particularly in students’ “plagiarism behavior” (Sallam, 2023a , b ). Education should guide students in cultivating AI literacy and instilling the correct AI ethical values, teaching them how to use AI responsibly while avoiding misuse and ensuring the ethical and legal standards of AI development align with societal norms. Future research can explore how to use AIGC to guide students in thinking deeply and discussing ethical topics related to AI through various means such as case studies, discussion classes, and ethics training, ultimately enhancing their AI literacy.

Thirdly, to construct AIGC with multi-modal data interaction. Human-machine interaction is a critical aspect of ensuring AIGC-style education and teaching, but existing interaction interfaces often suffer from stiffness and emotional understanding difficulties. Cultivating precise cognitive and emotional recognition capabilities in AIGC is particularly important. By gaining a more accurate understanding of students’ learning status, emotional experiences, and cognitive needs, AIGC can better comprehend the students’ state, allowing educators to provide more targeted instructional guidance, ultimately improving learning outcomes and meeting diverse students’ needs. Future research can enhance AIGC’s deep learning, natural language processing, and computer vision capabilities to harness the potential of AI for better collaboration, creating more significant value through more effective multi-modal data interaction.

7.2 Future agenda at the application level

7.2.1 developing specialized aigc educational application products.

Academic research can provide essential theoretical guidance for practical product development, especially when it comes to creating specialized AIGC (Artificial Intelligence Generated Content) educational application products. This step is considered the first move towards an intelligent education system, with tremendous potential to drive innovation and improvement in the field of education. In the following sections, we will explore the future research directions for developing specialized AIGC educational application products to provide effective guidelines for research topics at the application level.

Firstly, to build trustworthy AIGC educational application products. Data breaches are a significant cybersecurity issue that can result in substantial economic losses and compromise personal privacy (Albeshri & Thayananthan, 2018 ). The risk of data breaches in AIGC educational application products has generated distrust among many users. When educational application products face situations with reduced user resources and decreased credibility, their future prospects are undoubtedly bleak. Therefore, the development of trustworthy AIGC educational application products is a critical research agenda. Future research can emphasize transparency and interpretability in technical design and algorithm development, enabling educators and students to understand how AIGC products make recommendations and decisions, thereby establishing trust in their functioning. Additionally, research can explore ways to integrate ethics and values into the design of AIGC products to ensure their impact is positive and ethical. Furthermore, data privacy and security are essential elements in constructing trustworthy AIGC educational application products. Future research can focus on developing privacy protection technology to prevent the misuse or leakage of personal data of students and educators. Establishing stringent security measures to mitigate potential risks and threats can enhance the credibility of AIGC products.

Secondly, to promote the development of multi-modal data fusion in AIGC educational application products. Current AIGC educational application products primarily rely on limited data sources and types, usually confined to text input. However, in real educational teaching scenarios, various data types come into play, such as text, images, speech, and facial expressions, among others. Therefore, one of the future research directions should be how to effectively collect and utilize multi-modal data. By focusing on the development of technology that can integrate multiple data sources, we can gain a more comprehensive understanding of students’ learning status and needs, resulting in more accurate feedback and improved educational teaching effectiveness.

Thirdly, to develop AIGC products for specific educational scenarios. Current large language models are general-purpose and although they have broad application potential, to better cater to the needs of different educational fields, we need to emphasize the development of AIGC educational products for specific scenarios. These specific scenarios can include different subject areas, various age groups of students, and different teaching methods.

For instance, in the case of STEM (Science, Technology, Engineering, and Mathematics) education, specialized STEM education AIGC products can be developed to support students’ learning and exploration in this crucial field. These products may include interactive experiment simulations, problem-solving guidance, and personalized learning paths, specifically tailored to meet the needs of STEM education. Furthermore, for students of different age groups, AIGC products suitable for elementary, middle, and high school levels can be developed to ensure that the content and interaction methods match students’ cognitive levels and interests. In terms of teaching methods, we can develop AIGC products for different teaching modes, such as traditional classroom teaching, online learning, and blended education, to meet the needs of different teaching environments. Developing AIGC products for specific scenarios will help to customize educational solutions better, provide more targeted and effective teaching support, and ultimately enhance students’ learning outcomes. By developing AIGC products tailored to different educational scenarios, we can better meet the diverse needs of the education field, driving innovation and development in education.

7.2.2 Issues and measures for promoting the application of AIGC technology

The challenges that may arise during the process of technology promotion will inform the future research directions. The journey from the inception of a kind of technology to its widespread adoption is a lengthy one, and each stage involves its own set of issues. Many of these issues are domain-specific and not necessarily prescriptive, making it essential to pay attention to them in future application. To address these challenges effectively, future research can focus on expanding the audience, enhancing research methods, and promoting interdisciplinary studies.

Firstly, to expand the audience for AIGC technology in the educational ecosystem. Through the analysis of the selected articles, it’s evident that existing research has a narrow focus, mainly centered on discussions about AIGC products among teachers and learners. In reality, the promotion of a kind of technology will target a broader audience, including school administrators (educational leaders), parents, educational policymakers, and other stakeholders within the entire educational ecosystem, all of whom we should pay attention to. During the promotion of AIGC technology’s application, it is essential to take into consideration the attitudes and perspectives of various groups to ensure its broad acceptability and applicability. This approach can lead to a better understanding of the diverse needs, expectations, and concerns of different stakeholders and, in turn, ensure that the promotion of AIGC technology caters to the requirements of various stakeholders.

Secondly, to diversify research methods for investigating adoption intention towards AIGC technology. Through the analysis of existing literature, it’s clear that research on adoption intention towards AIGC technology primarily relies on quantitative analysis methods based on surveys (Foroughi et al., 2023 ; Strzelecki, 2023 ). However, it’s important to note that survey-based research is an analytical method with pre-existing researcher knowledge, and it may limit in-depth exploration of causal relationships. To gain a more comprehensive understanding of the current educational ecosystem’s adoption intention towards AIGC technology, it is advisable to actively incorporate qualitative research methods, such as in-depth interviews and real-world application case studies, and combine them with quantitative analytical methods. Quantitative analysis serves to collect extensive statistical data on external behaviors and discover the relationships between these behaviors and certain group variables. Qualitative analysis, on the other hand, can provide an understanding of the social and historical significance of these behaviors and deconstruct possible causal logic. By combining these two methods, a more profound and comprehensive insight can be provided, aiding in better guidance of adoption intention towards AIGC technology and addressing potential issues during its promotion. This integrated research approach offers deeper and broader insights, which are crucial for better guiding the application and promotion of AIGC technology in the educational field.

Thirdly, to promote interdisciplinary research as a vital measure for AIGC technology promotion. Presently, research on the promotion of AIGC technology is very narrow in terms of subject topics. However, to achieve the widespread promotion of emerging technology, it is essential to consider broader social and cultural factors, addressing issues like ease of human-computer interaction and users’ psychological acceptance. This emphasizes the need for analysis from a multidisciplinary perspective, including fields such as communication, technology science, and psychology. Interdisciplinary research is vital because it encourages cooperation between experts from various fields to collaboratively address issues in technology application, laying the foundation for comprehensive solutions. This collaborative effort can help in forming more diverse viewpoints and providing practical strategies and policy recommendations for technology promotion, thereby facilitating the sustainable development of technology and societal benefits. Future research should actively encourage and support interdisciplinary studies to further drive the widespread application of AIGC technology. Such cross-disciplinary collaboration can aid in gaining a more comprehensive understanding of the impact of AIGC technology in different fields and social contexts, providing more resources and wisdom for its successful application. The complete depiction of the future agenda is illustrated in Fig.  9 .

Comprehensive Diagram of the Future Agenda for AIGC’s Educational Application

8 Discussion, conclusion and prospect

8.1 discussion and conclusions.

In this section, we delve deeper into the implications and findings of our study, focusing on the key questions posed (RQ1-RQ4) and the insights we have gained from the systematic review of 134 academic research articles related to AIGC’s educational application.

8.1.1 Discussion on productivity (RQ1)

Through an analysis of 134 literature sources, this study attempts to provide a macroscopic overview of the discipline’s development. Firstly, we conducted an analysis of the state of productivity in this field, and from the overall development trend, it is evident that this research area has garnered significant attention from numerous scholars, resulting in a substantial increase in the number of publications. Secondly, the core productivity at the national level within this field has become well-established. Additionally, there is an ongoing diversification in the types and methods of research. As an interdisciplinary research topic, such trends undoubtedly inject more vitality and dynamism into this research field.

8.1.2 Discussion on research topics (RQ2)

In exploring the research topics on the empowerment of education by AIGC technology, this study observe the emergence of an interdisciplinary and multilevel field of study. The five key topics - performance assessment of AIGC technology, its application in teaching assistance, enhancement of learning performance, analysis of its advantages and disadvantages, and its future application prospects - collectively form a comprehensive framework for understanding the application of AIGC in education. From the assessment of technological performance to practical application, and further to in-depth analysis and exploration of future developments, these topics not only showcase the potential and challenges of AIGC technology in the field of education but also reflect the educational community’s cautious attitude towards emerging technologies and their continuous spirit of exploration.

The exploration of these topics is not isolated but interconnected and mutually influential. For instance, the performance assessment of the technology lays the foundation for its application in education (Tsang, 2023 ); how teachers and learners maximize the use of AIGC technology depends not only on the technology itself but also on teaching methods and learning strategies. Moreover, the analysis of the advantages and disadvantages of the technology not only reveals its current potential and limitations but also provides guidance for its future development (Tlili et al., 2023 ). Thus, this study delves deeply into the core strengths and potential risks of AIGC technology in education (RQ3) and comprehensively and systematically outlines the future agenda of this research field (RQ4).

8.1.3 Discussion on core advantages and potential risks (RQ3)

Clarifying the pros and cons of new technology applications in the field of education is undoubtedly a critical research topic. For this, our study constructs a multi-level and multi-dimensional analysis model (as shown in Fig. 7 ) while analyzing the core advantages and potential risks of AIGC technology in empowering education. This model starts from the mechanism of AIGC technology itself, analyzes the attributes represented in its application, and further connects to concrete scenarios, profoundly dissecting its core advantages and potential risks.

At the technological mechanism level, AIGC, as a double-edged sword, has its advantages and risks stemming from the inherent logic of the technology itself. This aspect highlights the complexity of technological development, demanding caution in leveraging its advantages while being vigilant about its potential side effects. At the attribute level, characteristics of AIGC such as instant feedback and personalized learning not only offer new possibilities for education but also raise concerns about privacy and bias (Abdulai & Hung, 2023 ; Extance, 2023 ). Additionally, discussions at the teaching model level and scenario application level point out how to balance these advantages and risks in the real educational environment. For instance, the thoughtful application of teaching concepts like “personalized learning” and “adaptive learning” can effectively harness the advantages of AIGC technology while overcoming its drawbacks (Sharma & Sharma, 2023 ; Sallam, 2023a , b ). This methodology is not just an application of the technology itself but a deep reflection and innovation in educational practice. At the scenario application level, theoretical frameworks must translate into practical applications, highlighting the importance of concrete examples and channels in the application of educational technology. This practice-oriented approach helps to better understand and utilize AIGC technology while ensuring the essence of education, driving it towards a direction more in line with the demands of the new era and technological progress.

Overall, this cross-level analytical framework not only emphasizes the value of applying AIGC technology in education but also points out the necessity of rationally dealing with the risks associated with technology use, including acknowledging the inherent drawbacks of the technology. In building an educational system and ecosystem that integrates AIGC, this approach can help educators and learners better utilize AIGC technology, ultimately propelling education towards a direction that aligns with the requirements of the new era and technological advancements.

8.1.4 Discussion on future research directions (RQ4)

Based on our extensive review of existing literature, we propose several valuable research topics for future investigation to further advance the integration of AIGC and education.

At the theoretical level, future research should explore the adaptability of existing educational theories to new technological learning contexts. For instance, examining how constructivist or socio-cultural learning theories can be applied in AI-enhanced classrooms can provide valuable insights into pedagogical design. Moreover, investigating the compatibility of these theories with AI-driven methodologies can guide the harnessing of advantages and mitigation of drawbacks, as demonstrated in the work of Zhang et al. ( 2023a ).

On the practical application front, future research should prioritize the development of proprietary large models specifically tailored for the educational domain. These models should be designed to understand and address educational nuances, ensuring that AI aligns seamlessly with the educational objectives (Extance, 2023 ).

In conclusion, discussing these future research agendas is instrumental in clarifying key issues in the integration of AI and education. It is crucial for promoting the development of more intelligent, effective, and sustainable educational methods and tools, ultimately benefiting learners, educators, and educational institutions worldwide.

8.1.5 Conclusions

Through a systematic review and analysis of 134 pieces of literature on AIGC applications in education, this study answered the four initial research questions posed. Firstly, regarding productivity ( RQ1 ), the field is experiencing a growth trend in publication volume, indicating a period of rapid development. Statistical analysis of the types of studies included shows that theoretical research is the most common, reflecting the academic community’s emphasis on and attention to theoretical frameworks and fundamental knowledge to deepen understanding and exploration of related topics. At the national productivity level, the United States is the leading country in publications in this field. Secondly, in terms of research themes ( RQ2 ), the study topics in AIGC in education include performance assessment of AIGC technology, AIGC-assisted teaching, AIGC’s enhancement of learning outcomes, potential impacts of AIGC technology, and development trends of AIGC technology. These five themes profoundly reveal the transformative impact of AIGC technology in education. Additionally, regarding the core advantages and potential risks of AIGC in educational applications ( RQ3 ), the study utilizes Grounded Theory to deeply deconstruct the mechanisms behind the advantages and disadvantages of AIGC technologies like ChatGPT, pointing out technological application models for promoting advantages and avoiding disadvantages. Finally, based on a review of existing literature, the study identifies valuable topics for future analysis from both theoretical and practical application perspectives ( RQ4 ). Theoretically, future research should consider whether existing educational theories are compatible with new technology-created learning contexts and if they can guide us in harnessing the advantages and avoiding the pitfalls of new technologies. In terms of practical application, future research should develop proprietary large models specific to the field of education, enabling AIGC to better serve current educational practices, and also pay attention to potential issues and countermeasures during the dissemination process. Discussing future research agendas will help clarify key issues in the integration of AI and education, promoting the development of more intelligent, effective, and sustainable educational methods and tools.

8.2 Limitations and future research

The study has certain limitations due to objective factors. Firstly, the study focused on AIGC technology’s educational application. However, due to the dominance of ChatGPT and related products in the scope of the review, it was challenging to conduct in-depth comparative analyses of different AIGC products. In reality, several AIGC products have emerged in the market beyond ChatGPT, such as Google’s Google Assistant (Pereira et al., 2022 ) and Microsoft’s Cortana (Kepuska & Bohouta, 2018 ). Given the time constraints, research on other AIGC products has yet to be published. Therefore, future research could consider conducting comparative analyses of AIGC products based on different technological foundations to provide more precise guiding recommendations for their educational application.

Furthermore, the primary purpose of this study is to employ a systematic literature review approach to consolidate research related to AIGC in the educational application domain, analyze comprehensive insights into the empowerment of education by AIGC, and thoroughly trace the development of research in the AIGC’s educational application. Despite the selection of articles from 4 databases as research objects, this study conducted in-depth scrutiny and analysis of only 134 selected articles, intending to portray the current state of research on the application of ChatGPT in the education sector. This approach, to a certain extent, is selective. Moreover, the subjective perception of the researchers inevitably affects the categorization of research topics and other aspects. Given these constraints, future research can improve in several ways. Firstly, by expanding the sample size and incorporating more relevant literature, a more comprehensive understanding of the current state of research on AIGC’s educational application can be achieved.

Data availability

The datasets (Coding results) generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Abdelghani, R., Wang, Y. H., Yuan, X., Wang, T., Lucas, P., Sauzéon, H., & Oudeyer, P. Y. (2023). Gpt-3-driven pedagogical agents to train children’s curious question-asking skills. International Journal of Artificial Intelligence in Education , 1–36. https://doi.org/10.1007/s40593-023-00340-7

Abdulai, A. F., & Hung, L. (2023). Will ChatGPT undermine ethical values in nursing education, research, and practice. Nursing Inquiry . e12556–e12556. https://doi.org/10.1111/nin.12556

Ahmed, S. K. (2023). The Impact of ChatGPT on the Nursing Profession: Revolutionizing Patient Care and Education. Annals of Biomedical Engineering , 1–2. https://doi.org/10.1007/s10439-023-03262-6

Albeshri, A., & Thayananthan, V. (2018). Analytical techniques for decision making on information security for big data breaches. International Journal of Information Technology & Decision Making, 17 (02), 527–545. https://doi.org/10.1142/S0219622017500432

Article   Google Scholar  

Allen, B., Dreyer, K., Stibolt Jr, R., Agarwal, S., Coombs, L., Treml, C., ... & Wald, C. (2021). Evaluation and real-world performance monitoring of artificial intelligence models in clinical practice: Try it, buy it, check it. Journal of the American College of Radiology , 18 (11), 1489–1496. https://doi.org/10.1016/j.jacr.2021.08.022

Alnaqbi, N. M., & Fouda, W. (2023). Exploring the role of ChatGPT and social media in enhancing student evaluation of teaching styles in higher education using neutrosophic sets. International Journal of Neutrosophic Science, 20 (4), 181–190. https://doi.org/10.1111/nin.12556

Alqahtani, T., Badreldin, H. A., Alrashed, M., Alshaya, A. I., Alghamdi, S. S., bin Saleh, K., ... & Albekairy, A. M. (2023). The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy. https://doi.org/10.1016/j.sapharm.2023.05.016

Ancillai, C., Sabatini, A., Gatti, M., & Perna, A. (2023). Digital technology and business model innovation: A systematic literature review and future research agenda. Technological Forecasting and Social Change, 188 , 122307. https://doi.org/10.1016/j.techfore.2022.122307

Banić, B., Konecki, M., & Konecki, M. (2023, May). Pair Programming Education Aided by ChatGPT. In 2023 46th MIPRO ICT and Electronics Convention (MIPRO) (pp. 911–915). IEEE.

Busch, F., Adams, L. C., & Bressem, K. K. (2023). Biomedical ethical aspects towards the implementation of artificial intelligence in medical education. Medical Science Educator., 33 , 1007–1012. https://doi.org/10.1007/s40670-023-01815-x

Article   PubMed   PubMed Central   Google Scholar  

Chang, C.-Y., Kuo, S.-Y., & Hwang, G.-H. (2022). Chatbot-facilitated nursing education: Incorporating a knowledge-based Chatbot system into a nursing training program. Educational Technology & Society , 25 (1), 15–27. Retrieved December 19, 2023, from https://www.jstor.org/stable/48647027

Charmaz, K., & Thornberg, R. (2021). The pursuit of quality in grounded theory. Qualitative Research in Psychology, 18 (3), 305–327. https://doi.org/10.1080/14780887.2020.1780357

Choi, E. P. H., Lee, J. J., Ho, M. H., Kwok, J. Y. Y., & Lok, K. Y. W. (2023). Chatting or cheating? The impacts of ChatGPT and other artificial intelligence language models on nurse education. Nurse Education Today, 125 , 105796–105796. https://doi.org/10.1016/j.nedt.2023.105796

Article   PubMed   Google Scholar  

Cooper, G. (2023). Examining science education in chatgpt: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32 (3), 444–452. https://doi.org/10.1007/s10956-023-10039-y

Article   ADS   Google Scholar  

Cross, J., Robinson, R., Devaraju, S., Vaughans, A., Hood, R., Kayalackakom, T., ... & Robinson, R. E. (2023). Transforming medical education: Assessing the integration of ChatGPT into faculty workflows at a Caribbean medical school. Cureus , 15 (7). https://doi.org/10.7759/cureus.41399

Currie, G. M. (2023, May). Academic integrity and artificial intelligence: Is ChatGPT hype, hero or heresy? In Seminars in Nuclear Medicine. WB Saunders. https://doi.org/10.1053/j.semnuclmed.2023.04.008

Das, D., Kumar, N., Longjam, L. A., Sinha, R., Roy, A. D., Mondal, H., & Gupta, P. (2023). Assessing the capability of ChatGPT in answering first-and second-order knowledge questions on microbiology as per competency-based medical education curriculum. Cureus , 15 (3). https://doi.org/10.7759/cureus.36034

Deacon, B., Laufer, M., & Schäfer, L. O. (2023). Infusing educational technologies in the heart of the university-a systematic literature review from an organisational perspective. British Journal of Educational Technology, 54 (2), 441–466. https://doi.org/10.1111/bjet.13277

Deeley, S. J. (2018). Using technology to facilitate effective assessment for learning and feedback in higher education. Assessment & Evaluation in Higher Education, 43 (3), 439–448. https://doi.org/10.1080/02602938.2017.1356906

Deng, X., & Yu, Z. (2023). A meta-analysis and systematic review of the effect of chatbot technology use in sustainable education. Sustainability, 15 (4), 2940. https://doi.org/10.3390/su15042940

Diekemper, R. L., Ireland, B. K., & Merz, L. R. (2015). Development of the documentation and appraisal review tool for systematic reviews. World Journal of Meta-Analysis, 3 (3), 142–150. https://doi.org/10.13105/wjma.v3.i3.142

Engel, A., & Coll, C. (2022). Hybrid teaching and learning environments to promote personalized learning. RIED-Revista Iberoamericana de Educacion a Distancia , 225–242. https://doi.org/10.5944/ried.25.1.31489

Escotet, M. Á. (2023). The optimistic future of Artificial Intelligence in higher education. Prospects, 1–10. https://doi.org/10.1007/s11125-023-09642-z

Esplugas, M. (2023). The use of artificial intelligence (AI) to enhance academic communication, education and research: A balanced approach. Journal of Hand Surgery (European Volume) , 48 (8), 819–822.  https://doi.org/10.1177/17531934231185746

Extance, A. (2023). ChatGPT has entered the classroom: How LLMs could transform education. Nature, 623 , 474–477. https://doi.org/10.1038/d41586-023-03507-3

Article   CAS   PubMed   ADS   Google Scholar  

Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A., Ghobakhloo, M., Annamalai, N., & Naghmeh-Abbaspour, B. (2023). Determinants of Intention to Use ChatGPT for Educational Purposes: Findings from PLS-SEM and fsQCA. International Journal of Human-Computer Interaction , 1–20. https://doi.org/10.1080/10447318.2023.2226495

Gaur, A., & Kumar, M. (2018). A systematic approach to conducting review studies: An assessment of content analysis in 25 years of IB research. Journal of World Business, 53 (2), 280–289. https://doi.org/10.1016/j.jwb.2017.11.003

Ghorbani, M., Bahaghighat, M., Xin, Q., & Özen, F. (2020). ConvLSTMConv network: A deep learning approach for sentiment analysis in cloud computing. Journal of Cloud Computing, 9 (1), 1–12. https://doi.org/10.1186/s13677-020-00162-1

Glaser, B., & Strauss, A. (2017). Discovery of grounded theory: Strategies for qualitative research . Routledge https://doi.org/10.1016/j.jwb.2017.11.003

Gough, D., Oliver, S., & Thomas, J. (Eds.). (2017). An introduction to systematic reviews . Sage https://doi.org/10.5124/jkma.2014.57.1.49

Grant, N., & Metz, C. (2022). A new chat bot is a ‘code red’ for Google's search business, The New York Times. Available at: https://www.nytimes.com/2022/12/21/technology/ai-chatgpt-google-search.html . Accessed 19 Dec 2023

Hadi, M. S., & Junor, R. S. (2022). Speaking to devices: Can we use Google assistant to Foster Students' speaking skills? Journal of Languages and Language Teaching, 10 (4), 570–578. https://doi.org/10.33394/jollt.v10i4.5808

Heng, J. J., Teo, D. B., & Tan, L. F. (2023). The impact of Chat Generative Pre-trained Transformer (ChatGPT) on medical education. Postgraduate Medical Journal , qgad058. https://doi.org/10.1093/postmj/qgad058

Ho, W., & Lee, D. (2023). Enhancing engineering education in the roblox metaverse: Utilizing chatgpt for game development for electrical machine course. International Journal on Advanced Science, Engineering & Information Technology , 13 (3). https://doi.org/10.18517/ijaseit.13.3.18458

Holmes, W., & Kay, J. (2023, June). AI in education. Coming of age? The community voice. In International conference on artificial intelligence in education (pp. 85–90). Springer Nature Switzerland.

Google Scholar  

Hsu, Y. C., & Ching, Y. H. (2023). Generative Artificial Intelligence in Education, Part One: the Dynamic Frontier. TechTrends , 1–5. https://doi.org/10.1007/s11528-023-00863-9

Hwang, G. J., & Chang, C. Y. (2021). A review of opportunities and challenges of chatbots in education. Interactive Learning Environments , 1–14. https://doi.org/10.1080/10494820.2021.1952615

Jalil, S., Rafi, S., LaToza, T. D., Moran, K., & Lam, W. (2023, April). Chatgpt and software testing education: Promises & perils. In 2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) (pp. 4130–4137). IEEE.

Jeon, J., & Lee, S. (2023). Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT. Education and Information Technologies , 1–20. https://doi.org/10.1007/s10639-023-11834-1

Jing, Y., Wang, C., Chen, Y., Wang, H., Yu, T., & Shadiev, R. (2023). Bibliometric mapping techniques in educational technology research: A systematic literature review. Education and Information Technologies , 1–29. https://doi.org/10.1007/s10639-023-12178-6

Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33 (7), 14–26. https://doi.org/10.3102/0013189X033007014

Karabacak, M., Ozkara, B. B., Margetis, K., Wintermark, M., & Bisdas, S. (2023). The advent of generative language models in medical education. JMIR Medical Education, 9 , e48163. https://doi.org/10.2196/48163

Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103 , 102274. https://doi.org/10.1016/j.lindif.2023.102274

Kepuska, V., & Bohouta, G. (2018, January). Next-generation of virtual personal assistants (microsoft cortana, apple siri, amazon alexa and google home). In 2018 IEEE 8th annual computing and communication workshop and conference (CCWC) (pp. 99–103). IEEE.

Kerneža, M. (2023). Fundamental And Basic Cognitive Skills Required For Teachers To Effectively Use Chatbots In Education. In Science And Technology Education: New Developments And Innovations (pp. 99–110). Scientia Socialis, UAB.

Kılıçkaya, F. (2020). Using a chatbot, Replika, to practice writing through conversations in L2 English: A Case study. In New Technological applications for foreign and second language learning and teaching (pp. 221–238). IGI Global. https://doi.org/10.4018/978-1-7998-2591-3.ch011

Killian, C. M., Marttinen, R., Howley, D., Sargent, J., & Jones, E. M. (2023). “Knock, Knock... Who’s There?” ChatGPT and Artificial Intelligence-Powered Large Language Models: Reflections on Potential Impacts Within Health and Physical Education Teacher Education. Journal of Teaching in Physical Education , 1 (aop), 1–5. https://doi.org/10.1123/jtpe.2023-0058

Kohnke, L. (2022). A pedagogical Chatbot: A supplemental language learning Tool. RELC Journal , 00336882211067054. https://doi.org/10.1177/00336882211067054

Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., ... & Tseng, V. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLoS Digital Health, 2 (2), e0000198. https://doi.org/10.1371/journal.pdig.0000198

Lee, D., & Yeo, S. (2022). Developing an AI-based chatbot for practicing responsive teaching in mathematics. Computers & Education, 191 , 104646. https://doi.org/10.1016/j.compedu.2022.104646

Lee, L. W., Dabirian, A., McCarthy, I. P., & Kietzmann, J. (2020). Making sense of text: Artificial intelligence-enabled content analysis. European Journal of Marketing, 54 (3), 615–644. https://doi.org/10.1108/EJM-02-2019-0219

Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., & Hemphill, L. (2023). ChatGPT in education: A discourse analysis of worries and concerns on social media. arXiv preprint arXiv :2305.02201. https://doi.org/10.48550/arXiv.2305.02201

Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21 (2), 100790. https://doi.org/10.1016/j.ijme.2023.100790

Lin, T. J., & Lan, Y. J. (2015). Language learning in virtual reality environments: Past, present, and future. Journal of Educational Technology & Society, 18 (4), 486–497. Retrieved December 19, 2023, from http://www.jstor.org/stable/jeductechsoci.18.4.486

Lodge, J. M., Thompson, K., & Corrin, L. (2023). Mapping out a research agenda for generative artificial intelligence in tertiary education. Australasian Journal of Educational Technology, 39 (1), 1–8. https://doi.org/10.14742/ajet.8695

Luo, H., Li, G., Feng, Q., Yang, Y., & Zuo, M. (2021). Virtual reality in K-12 and higher education: A systematic review of the literature from 2000 to 2019. Journal of Computer Assisted Learning, 37 (3), 887–901. https://doi.org/10.1111/jcal.12538

Mariani, M. M., Hashemi, N., & Wirtz, J. (2023). Artificial intelligence empowered conversational agents: A systematic literature review and research agenda. Journal of Business Research, 161 , 113838. https://doi.org/10.1016/j.jbusres.2023.113838

Mohamed, A. M. (2023). Exploring the potential of an AI-based Chatbot (ChatGPT) in enhancing English as a Foreign Language (EFL) teaching: perceptions of EFL Faculty Members. Education and Information Technologies, 1–23. https://doi.org/10.1007/s10639-023-11917-z

Mohammad, B., Supti, T., Alzubaidi, M., Shah, H., Alam, T., Shah, Z., & Househ, M. (2023). The pros and cons of using ChatGPT in medical education: A scoping review. Student Health Technology Information, 305 , 644–647. https://doi.org/10.3233/SHTI230580

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Annals of Internal Medicine, 151 , 264–269. https://doi.org/10.7326/0003-4819-151-4-200908180-00135

Mokmin, N. A. M., & Ibrahim, N. A. (2021). The evaluation of chatbot as a tool for health literacy education among undergraduate students. Education and Information Technologies, 26 (5), 6033–6049. https://doi.org/10.1007/s10639-021-10542-y

Patel, N., Nagpal, P., Shah, T., Sharma, A., Malvi, S., & Lomas, D. (2023). Improving mathematics assessment readability: Do large language models help? Journal of Computer Assisted Learning, 39 (3), 804–822. https://doi.org/10.1111/jcal.12776

Paul, J., Lim, W. M., O’Cass, A., Hao, A. W., & Bresciani, S. (2021). Scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR). International Journal of Consumer Studies, 45 (4), O1–O16. https://doi.org/10.1111/ijcs.12695

Pentina, I., Xie, T., Hancock, T., & Bailey, A. (2023). Consumer–machine relationships in the age of artificial intelligence: Systematic literature review and research directions. Psychology & Marketing, 40 (8), 1593–1614. https://doi.org/10.1002/mar.21853

Pereira, R., Reis, A., Barroso, J., Sousa, J., & Pinto, T. (2022). Virtual assistants applications in education. In International conference on technology and innovation in learning, teaching and education (pp. 468–480). Springer Nature Switzerland.

Pinto, A. S., Abreu, A., Costa, E., & Paiva, J. (2023). How Machine Learning (ML) is transforming higher education: A systematic literature review. Journal of Information Systems Engineering and Management, 8 (2). https://doi.org/10.55267/iadt.07.13227

Prikshat, V., Islam, M., Patel, P., Malik, A., Budhwar, P., & Gupta, S. (2023). AI-augmented HRM: Literature review and a proposed multilevel framework for future research. Technological Forecasting and Social Change, 193 , 122645. https://doi.org/10.1016/j.techfore.2023.122645

Radianti, J., Majchrzak, T. A., Fromm, J., & Wohlgenannt, I. (2020). A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda. Computers & Education, 147 , 103778. https://doi.org/10.1016/j.compedu.2019.103778

Rahimzadeh, V., Kostick-Quenet, K., Blumenthal Barby, J., & McGuire, A. L. (2023). Ethics education for healthcare professionals in the era of chatGPT and other large language models: Do we still need it?. The American Journal of Bioethics , 1–11. https://doi.org/10.1080/15265161.2023.2233358

Rahman, M. M., & Watanobe, Y. (2023). ChatGPT for education and research: Opportunities, threats, and strategies. Applied Sciences, 13 (9), 5783. https://doi.org/10.3390/app13095783

Article   CAS   Google Scholar  

Rasul, T., Nair, S., Kalendra, D., Robin, M., de Oliveira Santini, F., Ladeira, W. J., ... & Heathcote, L. (2023). The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning and Teaching, 6 (1). https://doi.org/10.37074/jalt.2023.6.1

Sallam, M. (2023a). ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. In Healthcare (Vol. 11, No. 6, p. 887). MDPI. https://doi.org/10.3390/healthcare11060887

Sallam, M. (2023b). ChatGPT utility in healthcare education, research, and practice: Systematic review on the promising perspectives and valid concerns. Healthcare, 11 (6), 887. https://doi.org/10.3390/healthcare11060887

Sánchez-Ruiz, L. M., Moll-López, S., Nuñez-Pérez, A., Moraño-Fernández, J. A., & Vega-Fleitas, E. (2023). ChatGPT challenges blended learning methodologies in engineering education: A case study in mathematics. Applied Sciences, 13 (10), 6039. https://doi.org/10.3390/app13106039

Sandu, N., & Gide, E. (2019). Adoption of AI-Chatbots to enhance student learning experience in higher education in India. In 2019 18th International Conference on Information Technology Based Higher Education and Training (ITHET) (pp. 1–5). IEEE.

Schmulian, A., & Coetzee, S. A. (2019). Students’ experience of team assessment with immediate feedback in a large accounting class. Assessment & Evaluation in Higher Education, 44 (4), 516–532. https://doi.org/10.1080/02602938.2018.1522295

Seetharaman, R. (2023). Revolutionizing medical education: Can ChatGPT boost subjective learning and expression? Journal of Medical Systems, 47 (1), 1–4. https://doi.org/10.1007/s10916-023-01957-w

Sharma, M., & Sharma, S. (2023). A holistic approach to remote patient monitoring, fueled by ChatGPT and Metaverse technology: The future of nursing education. Nurse Education Today, 131 , 105972. https://doi.org/10.1016/j.nedt.2023.105972

Shlonsky, A., Noonan, E., Littell, J. H., & Montgomery, P. (2011). The role of systematic reviews and the Campbell collaboration in the realization of evidence-informed practice. Clinical Social Work Journal, 39 , 362–368. https://doi.org/10.1007/s10615-010-0307-0

Shoja, M. M., Van de Ridder, J. M., & Rajput, V. (2023). The emerging role of generative artificial intelligence in medical education, research, and practice. Cureus, 15 (6), e40883. https://doi.org/10.7759/cureus.40883

Siegle, D. (2023). A role for ChatGPT and AI in gifted education. Gifted Child Today, 46 (3), 211–219. https://doi.org/10.1177/10762175231168443

Smith, A., Hachen, S., Schleifer, R., Bhugra, D., Buadze, A., & Liebrenz, M. (2023). Old dog, new tricks? Exploring the potential functionalities of ChatGPT in supporting educational methods in social psychiatry. International Journal of Social Psychiatry . https://doi.org/10.1177/0020764023117845

Strzelecki, A. (2023). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments , 1–14. https://doi.org/10.1080/10494820.2023.2209881

Tam, W., Huynh, T., Tang, A., Luong, S., Khatri, Y., & Zhou, W. (2023). Nursing education in the age of artificial intelligence powered Chatbots (AI-Chatbots): Are we ready yet? Nurse Education Today, 129 , 105917. https://doi.org/10.1016/j.nedt.2023.105917

Teel, Z. A., Wang, T., & Lund, B. (2023). ChatGPT conundrums: Probing plagiarism and parroting problems in higher education practices. College & Research Libraries News, 84 (6), 205. https://doi.org/10.5860/crln.84.6.205

Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10 (1), 15. https://doi.org/10.1186/s40561-023-00237-x

Tsang, R. (2023). Practical applications of ChatGPT in undergraduate medical education. Journal of Medical Education and Curricular Development , 10 . https://doi.org/10.1177/23821205231178449

Yan, D. (2023). Impact of ChatGPT on learners in a L2 writing practicum: An exploratory investigation. Education and Information Technologies, 1–25. https://doi.org/10.1007/s10639-023-11742-4

Zhang, R., Zou, D., & Cheng, G. (2023a). A review of chatbot-assisted learning: pedagogical approaches, implementations, factors leading to effectiveness, theories, and future directions. Interactive Learning Environments , 1–29. https://doi.org/10.1080/10494820.2023.2202704

Zhang, S., Shan, C., Lee, J. S. Y., Che, S., & Kim, J. H. (2023b). Effect of chatbot-assisted language learning: A meta-analysis. Education and Information Technologies , 1–21. https://doi.org/10.1007/s10639-023-11805-6

Zhu, C., Sun, M., Luo, J., Li, T., & Wang, M. (2023). How to harness the potential of ChatGPT in education? Knowledge Management & E-Learning, 15 (2), 133. https://doi.org/10.34105/j.kmel.2023.15.008

Download references

Author information

Authors and affiliations.

College of Educational Science and Technology, Zhejiang University of Technology, Hangzhou, China

Xiaojiao Chen

College of Foreign Languages, Zhejiang University of Technology, Hangzhou, China

Department of Education Information Technology, Faculty of Education, East China Normal University, Shanghai, China

Chengliang Wang

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Chengliang Wang .

Ethics declarations

Conflict of interest.

During the research, the authors indicate that no commercial or financial ties that may be regarded a possible conflict of interest existed.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Chen, X., Hu, Z. & Wang, C. Empowering education development through AIGC: A systematic literature review. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12549-7

Download citation

Received : 19 October 2023

Accepted : 05 February 2024

Published : 29 February 2024

DOI : https://doi.org/10.1007/s10639-024-12549-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Artificial intelligence generated content
• Artificial intelligence
• Systematic literature review
• Educational technology
• Find a journal
• Publish with us
• Track your research

ALA User Menu

• ALA Websites

Breadcrumb navigation

• Submit News Items
• Contact Congress

New from ACRL - “Universal Design for Learning in Academic Libraries: Theory into Practice”

• Share This Page

Related Images

(Click for full-size)

For Immediate Release Thu, 03/21/2024

Erin Nevius

Content Strategist

[email protected]

CHICAGO – The Association of College and Research Libraries (ACRL) announces the publication of “ Universal Design for Learning in Academic Libraries: Theory into Practice ,” edited by Danielle Skaggs and Rachel M. McMullin. It includes lesson plans and strategies for the wide range of instructional activities that occur in academic libraries, including in-person, online, synchronous, asynchronous, and research help, as well as different types of academic library work such as access services and leadership. 

Universal Design for Learning (UDL) is an educational framework for improving and optimizing teaching and learning. It’s focused on intentionally designing for the needs and abilities of all learners—putting accessibility into the planning stages instead of as an accommodation after the fact—and providing flexibility in the ways students access and engage with materials and learning objectives. 

"Universal Design for Learning in Academic Libraries: Theory into Practice" explores UDL in four parts:

• Theory and Background
• In Instruction and Reference
• Behind the Scenes
• Beyond the Library

Chapters include looks at UDL and U.S. law and policy; working with student disability services to create accessible research services; UDL and the ACRL "Framework for Information Literacy for Higher Education" and the Reference and User Services Association’s “Guidelines for Behavioral Performance of Reference and Information Service Providers”; making open educational resources equitable and accessible; and much more. 

"Universal Design for Learning in Academic Libraries" can make learning about UDL and implementing it into your work quicker and easier, and provides ways to become an advocate for UDL inside your library and across campus. 

“Universal Design for Learning in Academic Libraries: Theory into Practice” is available for purchase in print and as an ebook through the ALA Online Store; in print through Amazon.com; and by telephone order at (866) 746-7252 in the U.S. or (770) 442-8633 for international customers. 

The Association of College & Research Libraries (ACRL) is the higher education association for academic libraries and library workers. Representing nearly 8,000 individuals and libraries, ACRL (a division of the American Library Association) develops programs, products, and services to help those working in academic and research libraries learn, innovate, and lead within the academic community. Founded in 1940, ACRL is committed to advancing learning, transforming scholarship, and creating diverse and inclusive communities. Find ACRL on the  web ,  Facebook ,  Twitter ,  Instagram ,  LinkedIn ,  Threads , and  YouTube . 

open I am a

• Future Student
• Current Student
• Newly Admitted Student
• Parent/Guardian
• Faculty / Staff Member

open Colleges

• Arts and Letters
• Fowler College of Business
• Engineering
• Graduate Studies
• Health and Human Services
• SDSU Library
• Professional Studies and Fine Arts
• Weber Honors College

open Other Locations

• SDSU Georgia
• SDSU Global Campus
• SDSU Imperial Valley
• SDSU Mission Valley
• March Madness Bracketology: A statistician’s guide for beating 1-in-147 quintillion odds of the perfect bracket

Fowler College of Business lecturer Chris O’Byrne, a college sports fanatic and former options trader on Wall Street, breaks down some of the math behind bracketology, and offers some tips for winning your bracket pool.

• Share on Facebook
• Share on Twitter
• Share on LinkedIn
• Share via Email

College basketball fans watch their favorite teams all season with the hope that come March, their squad will earn a berth in the NCAA Tournament.

Fans without a qualifying team, and plenty of others who don’t even follow the sport, might still become consumed by March Madness because of bragging rights at stake. Every year the tournament brings friends, families and colleagues together to compete in bracket pools, or challenges to see who can pick the most winners in a slate of 67 games.

Most know that picking a perfect bracket is next to impossible. Companies have long offered millions in sweepstakes cash to anyone who can accomplish the feat, but few know the simple math equation that explains why it’s so difficult.

San Diego State University NewsCenter asked Fowler College of Business lecturer Chris O’Byrne , a college sports fanatic and former options trader on Wall Street, to walk through that equation. O’Byrne offered a few tips that might help you beat the odds, and took a look at a few factors that could help or hurt the Aztecs’ chances of playing in the title game in back-to-back seasons.

Why is it so hard to pick a perfect bracket? The chances of picking a perfect bracket is: 1 in 2 to the 67th power, or 1 in 147,573,952,589,676,412,928, or about 147 quintillion. This assumes that each participant has a 50/50 chance of winning. Since the tournament is seeded, this changes the odds a little bit in the bracket filler’s favor, but the fact remains that is how many different or unique brackets can be created from 68 teams.

How improbable was it to see 16th-ranked Fairleigh Dickinson beat no. 1 Purdue last March?

It was very improbable. In the first 139 match ups between the 1 and 16 seeds, the 1 seed won. So the 16 seed was 0-139. Granted, there have been some very close, nail-biting, almost upsets, but in the end, the 1 seed always prevailed. From a strictly probabilistic standpoint - 0.714% (1 out of 140) - and that may be too high – but strictly using a relative frequency probability model – less than 1%. I do believe there are fatter tails for the 16 seed to knock off the 1 seed than indicated above due to competition and parity between teams closing.

How can success or failure in the final leg of the season impact the Aztecs’ chances of competing for the National Championship for the second year in a row?

A successful final leg of the season, including in the Mountain West conference tournament, can provide the Aztecs with momentum and confidence heading into the NCAA Tournament and helps their case for a better seed in the NCAA Tournament. Higher seeds generally have more favorable matchups in the early rounds, increasing the Aztecs’ chances of advancing deeper into the tournament.

An impressive run up to Selection Sunday could lead to increased national recognition and attention from basketball analysts. This can boost the team’s confidence and potentially influence tournament selection committees, leading to more favorable matchups and considerations.

Conversely, failure down the stretch or even key injuries, can hurt the Aztecs’ confidence and momentum and translate to poor play in the NCAA Tournament. Losses in critical games could result in a lower seed, leading to tough early-round matchup and possibly an early exit from the tournament.

Do you have any tips for winning office bracket pools? Do you recommend picking the lowest seed to win each matchup? Why or why not?

Winning an office bracket pool often requires a balance of safe picks (higher seeds) and calculated risks (lower seeds and upsets). Don’t solely pick the favorites to win each matchup. While upsets are a big part of March Madness, they are still relatively rare, especially in the early rounds. A good strategy is to mix in some lower-seeded teams for upsets, but also pick higher-seeded teams that are strong contenders.

Higher seeds are higher for a reason. They often have better overall records, stronger schedules, and more talent. It’s generally a good idea to pick higher seeds to advance in the early rounds, but also be strategic about where you pick your upsets. One common first-round upset to consider is the 12th seed defeating the 5th seed. This happens more frequently than other upsets and can be a good place to take a risk. It’s often better to take calculated risks as the tournament progresses and the stakes get higher. While upsets are exciting, being too risky too early can lead to a busted bracket quickly.

Teams that performed well in their conference tournaments often carry that momentum into March Madness. Look at how teams performed in their conference tournaments as an indicator of their current form.

What are some of the other key factors?

It could also help to analyze teams beyond their seeding. Consider factors like team styles (fast-paced vs. slow-paced), strengths and weaknesses (strong defense or poor three-point shooting), and recent performances. Some matchups favor certain teams even if they are seeded lower.

Unfortunately, injuries can significantly impact a team’s performance, especially in the tournament. Keep an eye on injury reports leading up to the games and adjust your picks accordingly.

Ultimately, March Madness is unpredictable, and even the most well-researched bracket can be busted by unexpected outcomes. Trust your instincts but also use data, statistics, and analysis to inform your picks.

Lastly, have fun. Remember, part of the enjoyment of March Madness is the unpredictability. Even if your bracket doesn’t win, the excitement of the games and the camaraderie of participating in an office pool can make it a fun experience.

• Men’s Hoops: Aztecs face UAB in NCAA first round

The Aztecs are the 5-seed for the second time in program history after reaching the National Championship game last year on that seed line.

More From Sports

• Track and Field announces 45th Annual Aztec Invitational
• Steve Fisher: Reflections of a 'teacher-slash-coach'

Campus News

• Recap: 16th annual San Diego Festival of Science and Engineering
• SDSU Associated Students wins sustainability leadership award

• Exhibit 'Rising Up: Depictions of Social Protest in Comics' opens March 12
• GreenFest: Celebrating sustainability, diversity and SDSU pride

• SDSU Women’s Fund champions equity in athletics, academics
• SDSU announces $5 million Black Resource Center naming gift