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The impact of artificial intelligence on learner–instructor interaction in online learning

• Kyoungwon Seo   ORCID: orcid.org/0000-0003-3435-0685 1 ,
• Joice Tang 2 ,
• Ido Roll 3 ,
• Sidney Fels 4 &
• Dongwook Yoon 2  

International Journal of Educational Technology in Higher Education volume  18 , Article number:  54 ( 2021 ) Cite this article

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Artificial intelligence (AI) systems offer effective support for online learning and teaching, including personalizing learning for students, automating instructors’ routine tasks, and powering adaptive assessments. However, while the opportunities for AI are promising, the impact of AI systems on the culture of, norms in, and expectations about interactions between students and instructors are still elusive. In online learning, learner–instructor interaction (inter alia, communication, support, and presence) has a profound impact on students’ satisfaction and learning outcomes. Thus, identifying how students and instructors perceive the impact of AI systems on their interaction is important to identify any gaps, challenges, or barriers preventing AI systems from achieving their intended potential and risking the safety of these interactions. To address this need for forward-looking decisions, we used Speed Dating with storyboards to analyze the authentic voices of 12 students and 11 instructors on diverse use cases of possible AI systems in online learning. Findings show that participants envision adopting AI systems in online learning can enable personalized learner–instructor interaction at scale but at the risk of violating social boundaries. Although AI systems have been positively recognized for improving the quantity and quality of communication, for providing just-in-time, personalized support for large-scale settings, and for improving the feeling of connection, there were concerns about responsibility, agency, and surveillance issues. These findings have implications for the design of AI systems to ensure explainability, human-in-the-loop, and careful data collection and presentation. Overall, contributions of this study include the design of AI system storyboards which are technically feasible and positively support learner–instructor interaction, capturing students’ and instructors’ concerns of AI systems through Speed Dating, and suggesting practical implications for maximizing the positive impact of AI systems while minimizing the negative ones.

Introduction

The opportunities for artificial intelligence (AI) in online learning and teaching are broad (Anderson et al., 1985 ; Baker, 2016 ; Roll et al., 2018 ; Seo et al., 2020b ; VanLehn, 2011 ), ranging from personalized learning for students and automation of instructors’ routine tasks to AI-powered assessments (Popenici & Kerr, 2017 ). For example, AI tutoring systems can provide personalized guidance, support, or feedback by tailoring learning content based on student-specific learning patterns or knowledge levels (Hwang et al., 2020 ). AI teaching assistants help instructors save time answering students’ simple, repetitive questions in online discussion forums, and instead instructors can dedicate their saved time to higher-value work (Goel & Polepeddi, 2016 ). AI analytics allows instructors to understand students’ performance, progress, and potential by decrypting their clickstream data (Roll & Winne, 2015 ; Fong et al., 2019 ; Seo et al., 2021 ; Holstein et al., 2018 ).

While the opportunities for AI are promising, students and instructors may perceive the impact of AI systems negatively. For instance, students may perceive indiscriminate collection and analysis of their data through AI systems as a privacy breach, as illustrated by the Facebook–Cambridge Analytica data scandal (Chan, 2019 ; Luckin, 2017 ). The behavior of AI agents that do not take into account the risk of data bias or algorithmic bias can be perceived by students as discriminatory (Crawford & Calo, 2016 ; Murphy, 2019 ). Instructors worry that relying too much on AI systems might compromise the student’s ability to learn independently, solve problems creatively, and think critically (Wogu et al., 2018 ). It is important to examine how students and instructors perceive the impact of AI systems in online learning environments (Cruz-Benito et al., 2019 ).

The AI in Education (AIEd) community is increasingly exploring the impact of AI systems in online education. For example, Roll and Wylie ( 2016 ) call for more involvement of AI systems in the communication between students and instructors, and in education applications outside school context. At the same time, Zawacki-Richter and his colleagues ( 2019 ) conducted a systematic review of AIEd publications from 2007 to 2018 and as a result found a lack of critical reflection of the ethical impact and risks of AI systems on learner–instructor interaction. Popenici and Kerr ( 2017 ) investigated the impact of AI systems on learning and teaching, and uncovered potential conflicts between students and instructors, such as privacy concerns, changes in power structures, and excessive control. All of these studies called for more research into the impact of AI systems on learner–instructor interaction, which will help us identify any gaps, issues, or barriers preventing AI systems from achieving their intended potential.

Indeed, learner–instructor interaction plays a crucial role in online learning. Kang and Im ( 2013 ) demonstrated that factors of learner–instructor interaction, such as communication, support, and presence, improve students’ satisfaction and learning outcomes. The learner–instructor interaction further affects students’ self-esteem, motivation to learn, and confidence in facing new challenges (Laura & Chapman, 2009 ). Less is known, however, about how introducing AI systems in online learning will affect learner–instructor interaction. Guilherme ( 2019 , p. 7) predicted that AI systems would have “a deep impact in the classroom, changing the relationship between teacher and student.” More work is needed to understand how and why various forms of AI systems affect learner–instructor interaction in online learning (Felix, 2020 ).

Considering the findings in the literature and the areas for further research, the present study aimed to identify how students and instructors perceive the impact of AI systems on learner–instructor interaction in online learning. To this end, we used Speed Dating, a design method that allows participants to quickly interact with and experience the concepts and contextual dimensions of multiple AI systems without any technical implementation (Davidoff et al., 2007 ). In Speed Dating, participants are presented with various hypothetical scenarios via storyboards while researchers conduct interviews to understand the participants’ immediate reactions (Zimmerman & Forlizzi, 2017 ). These interviews provided rich opportunities to understand the way students and instructors perceive the impact of AI systems on learner–instructor interaction and the boundaries beyond which AI systems are perceived as “invasive.”

The study offers several unique contributions. First, as part of the method, we designed storyboards that can be used to facilitate further research on AI implications for online learning. Second, the study documents the main promises and concerns of AI in online learning, as perceived by both students and instructors in higher education. Last, we identify practical implications for the design of AI-based systems in online learning. These include empahses on explainability, human-in-the-loop, and careful data collection and presentation.

This paper is organized as follows. The next section provides the theoretical framework and background behind this research paper by describing the main aspects of the learner–instructor interaction and AI systems in education. “ Materials and methods ” section is related to the methodological approach followed in this research and describes the storyboards used to collect data, the participants, the study procedure, and the performed qualitative analysis. “ Findings ” section shows the results obtained and the main findings related to the research question. Finally, “ Discussion and conclusion ” section provides an overview of the study’s conclusions, limitations, and future research.

This paper explores the impact of AI systems on learner–instructor interaction in online learning. We first proposed a theoretical framework based on studies on learner–instructor interaction in online learning. We then reviewed the AI systems currently in use in online learning environments.

Theoretical framework

Interaction is paramount for successful online learning (Banna et al., 2015 ; Nguyen et al., 2018 ). Students exchange information and knowledge through interaction and construct new knowledge from this process (Jou et al., 2016 ). Moore ( 1989 ) classified these interactions in online learning into three types: learner–content, learner–learner, and learner–instructor. These interactions help students become active and more engaged in their online courses (Seo et al., 2021 ; Martin et al., 2018 ), and by doing so strengthen their sense of community which is essential for the continuous usage of online learning platforms (Luo et al., 2017 ).

Martin and Bolliger ( 2018 ) found that the learner–instructor interaction is the most important among Moore’s three types of interactions. Instructors can improve student engagement and learning by providing a variety of communication channels, support, encouragement, and timely feedback (Martin et al., 2018 ). Instructors can also enhance students’ sense of community by engaging and guiding online discussions (Shackelford & Maxwell, 2012 ; Zhang et al., 2018 ). Collectively, learner–instructor interaction has a significant impact on students’ satisfaction and achievement in online learning (Andersen, 2013 ; Kang & Im, 2013 ; Walker, 2016 ).

The five-factor model of learner–instructor interaction offers a useful lens for interpreting interactions between students and the instructor in online learning (see Table 1 ; Kang, 2010 ). Robinson et al. ( 2017 ) found that communication and support are key factors of the learner–instructor interaction for designing meaningful online collaborative learning. Richardson et al. ( 2017 ) added that the perceived presence during learner–instructor interaction positively influences student motivation, satisfaction, learning, and retention in online courses. Kang and Im ( 2013 ) synthesized these findings by showing that communication, support, and presence are the three most important factors in improving students’ achievement and satisfaction over other factors. Thus, in this study, we focused on communication, support, and presence between students and instructors.

AI systems are likely to affect the way learner–instructor interaction occurs in online learning environments (Guilherme, 2019 ). If students and instructors have strong concerns about the impact of AI systems on their interactions, then they would not use such systems, in spite of perceived benefits (Felix, 2020 ). To the best of our knowledge, the impact of AI systems on learner–instructor interaction has limited empirical studies, and Misiejuk and Wasson ( 2017 ) have called for more work on this.

Artificial intelligence in online learning

There are a variety of AI systems that are expected to affect learner–instructor interaction in online learning. For example, Goel and Polepeddi ( 2016 ) developed an AI teaching assistant named Jill Watson to augment the instructor’s communication with students by autonomously responding to student introductions, posting weekly announcements, and answering routine, frequently asked questions. Perin and Lauterbach ( 2018 ) developed an AI scoring system that allows faster communication of grades between students and the instructor. Luckin ( 2017 ) showed AI systems that support both students and instructors by providing constant feedback on how students learn and the progress they are making towards their learning goals. Ross et al. ( 2018 ) developed online adaptive quizzes to support students by providing learning contents tailored to each student’s individual needs, which improved student motivation and engagement. Heidicker et al. ( 2017 ) showed that virtual avatars allow several physically separated users to collaborate in an immersive virtual environment by increasing sense of presence. Aslan and her colleagues ( 2019 ) developed AI facial analytics to improve instructors’ presence as a coach in technology-mediated learning environments. When looking at these AI systems, in-depth insight into how students and instructors perceive the AI’s impact is important (Zawacki-Richter et al., 2019 ).

The recent introduction of commercial AI systems for online learning has demonstrated the complex impact of AI on learner–instructor interaction. For instance, Proctorio (Proctorio Inc., USA), a system that aims to prevent cheating by monitoring students and their computer screens during an exam, seems like a fool-proof plan to monitor students in online learning, but students complain that it increases their test-taking anxiety (McArthur, 2020 ). The idea of being recorded by Proctorio distracts students and creates an uncomfortable test-taking atmosphere. In a similar vein, although Squirrel AI (Squirrel AI Learning Inc., China) aims to provide adaptive learning by adjusting itself automatically to the best method for an individual student, there is a risk that this might restrict students’ creative learning (Beard, 2020 ). These environments have one thing in common: Unlike educational technologies that merely mediate interactions between instructors and students, AI systems have more autonomy in the way in which it interprets data, infers learning, and at times, takes instructional decisions.

In what follows, we describe Speed Dating with storyboards, an exploratory research method that allows participants to quickly experience different forms of AI systems possible in the near future, to examine the impact of those systems on learner–instructor interaction (“ Materials and methods ”). Findings offer new insights on students’ and instructors’ boundaries, such as when AI systems are perceived as “invasive” (“ Findings ”). Lastly, we discuss how our findings provide implications for future AI systems in online learning ( Discussion and conclusion ).

Materials and methods

The goal of this study is to gain insight on students’ and instructors’ perception of the impact of AI systems on learner–instructor interaction (inter alia, communication, support, and presence; Kang & Im, 2013 ) in online learning. The study was conducted amid the COVID-19 pandemic, thus students and instructors have heightened awareness about the importance of online learning and fresh experiences from the recent online courses. Our aim was not to evaluate specific AI technologies, but instead, to explore areas where AI systems positively contribute to learner–instructor interaction and where more attention is required.

We used Speed Dating with storyboards, an exploratory research method that allows participants to experience a number of possible AI systems in the form of storyboards, to prompt participants to critically reflect on the implications of each AI area (Zimmerman & Forlizzi, 2017 ). Exposure to multiple potential AI areas that are likely to be available in the future helps participants to shape their own perspectives and to evaluate the AI systems in a more nuanced way (Luria et al., 2020 ). We first created a set of eleven four-cut storyboards for the comprehensive and diverse use cases of possible AI systems in online learning (see “ Creating storyboards ” section), and then used these storyboards to conduct Speed Dating with student and instructor participants (see “ Speed dating ” section). Overall, we address the following research question:

How do students and instructors perceive the impact of AI systems on learner–instructor interaction (inter alia, communication, support, and presence) in online learning?

Creating storyboards

To create AI system storyboards which are technically feasible and positively support learner–instructor interaction, we ran an online brainwriting activity (Linsey & Becker, 2011 ) in which we asked a team of designers to come up with scenarios about possible AI systems in online learning. We recruited six designers from our lab (four faculty members and two PhD candidates) with an average of 15.4 years (SD = 4.7 years) of design experience in human–computer interaction (HCI). Each team member wrote down scenarios using a Google Slides file and then passed it on to another team member. This process was repeated four times until all designers agreed that the scenarios of AI systems were technically feasible and supported learner–instructor interaction in online learning.

As initial scenarios were made by HCI designers, in order to validate their technical feasibility and positive impact on learner–instructor interaction, we enacted additional interviews with six AI experts with an average of 10.8 years (SD = 7.8 years) of research experience and 8 years (SD = 6.2 years) of teaching experience (see Appendix A , Table 7 , for details). The first two authors conducted semi-structured interviews with AI experts using a video conferencing platform (i.e., Zoom). We showed each scenario to AI experts and asked the following questions: “Can you improve this scenario to make it technically feasible?” and “Can you improve this scenario to have a positive impact on learner–instructor interaction based on your own online teaching experience?” After showing all the scenarios, the following question was asked: “Do you have any research ideas that can be used as a new scenario?” The scenario was modified to reflect the opinions of AI experts and AIEd literature. The interviews lasted around 41 min on average (SD = 7.3 min). Each AI expert was compensated 100 Canadian dollars for their time. The process was cleared by the Research Ethics Board.

As shown in Table 2 , we ended up with 11 scenarios which support learner–instructor interaction (i.e., communication, support, and presence) in online learning. Scenarios were categorized by the first two authors with reference to the learner–instructor interaction factors as defined in Table 1 (see “ Theoretical framework ” section). For example, although the AI Teaching or Grading Assistant scenarios could be considered systems of support for the instructor, “support” within the learner–instructor interaction framework refers to support for the student. Therefore, since the scenarios illustrate increased or expedited communication between students and instructors rather than direct support for students, AI Teaching and Grading Assistant scenarios are categorized as systems for communication. We note that these categories are not definitive, and scenarios may have interleaving aspects of several learner–instructor interaction factors. However, the final categories in Table 2 refer to the factors that best define the respective scenarios.

Seven scenarios (Scenarios 1, 3, 5, 6, 8, 9, and 11) have well reflected the state-of-the-art AI systems that were identified in “Artificial intelligence in online learning” section. The following four scenarios were created based on research ideas from AI experts: AI Companion (Scenario 2), AI Peer Review (Scenario 4), AI Group Project Organizer (Scenario 7), and AI Breakout Room Matching (Scenario 10). These 11 final scenarios were not to exhaust all AI systems in online learning or to systematically address all topics, but rather to probe a range of situations that shed light on the realities that present themselves with the utilization of AI systems in online learning.

We generated four-cut storyboards based on the scenarios in Table 2 . Figure  1 shows an illustrated example of a storyboard detailing the scenario through captions. We stylized the characters in a single visual style and as flat cartoon shades in order to reduce gender and ethnic clues and enable participants to put themselves in the shoes of the characters in each storyboard (Truong et al., 2006 ; Zimmerman & Forlizzi, 2017 ). The full set of storyboards can be viewed at https://osf.io/3aj5v/?view_only=bc5fa97e6f7d46fdb66872588ff1e22e .

A storyboard example of scenario 8, Adaptive Quiz in Table 2

• Speed dating

Participants

Next, we conducted a Speed Dating activity with storyboards. We recruited 12 students (see Table 3 ) and 11 instructors (see Table 4 ) for a Speed Dating activity. For diversity, we recruited students from 11 different majors and recruited instructors from nine different subjects. Students and instructors had a minimum of three months of online learning or teaching experience due to the COVID-19 pandemic. Overall, students had at least one year of university experience and instructors had at least three years of teaching experience. We required students and instructors to have online learning and teaching experience respectively so as to control the expected and experienced norms of student-instructor interaction within online university classes. Conversely, we did not require participants to have knowledge of AI systems as we wanted their perspective on the intended human–AI interactions and their potential effects as illustrated. Previous studies showed that Speed Dating works well without any prior knowledge or experience with AI systems, so no special knowledge or experience was required to participate in this study (Luria et al., 2020 ; Zimmerman & Forlizzi, 2017 ). Each participant was compensated with 25 Canadian dollars for their time.

We conducted semi-structured interviews with participants using a video conferencing platform (i.e., Zoom). We designed the interview questions to capture how the participants perceive the AI systems illustrated in the storyboards (see Appendix B). Participants read each of the storyboards aloud and then expressed their perceptions of AI in online learning. Specifically, we asked participants to critically reflect on how incorporating the AI system into an online course would affect learner–instructor interaction and whether they would like to experience its effect. We also asked them to choose AI systems that would work well and which would not work well, to capture their holistic point of view regarding perceived affordances and drawbacks. The entire interview lasted around 50.9 min (SD = 10.7 min), with 3–5 min spent to share each storyboard and probe participants on its specific implications.

Data analysis

Each interview was audio recorded and transcribed for analysis. We used a Reflexive Thematic Analysis approach (Braun & Clarke, 2006 ; Nowell et al., 2017 ). After a period of familiarization with the interview data, the first two authors began by generating inductive codes with an initial round of semantic codes related to intriguing statements or phrases in the data. The two authors then coded each transcript by highlighting and commenting on data items through Google Docs, independently identifying patterns that arose through extended examination of the dataset. Any conflicts regarding such themes were resolved through discussion between the two authors. Later, through a deductive approach guided by the learner–instructor interaction factors adapted from Kang and Im ( 2013 ), data were coded and collated into themes in a separate word document. An example of our codes can be viewed at https://osf.io/3aj5v/?view_only=bc5fa97e6f7d46fdb66872588ff1e22e . We then utilized three iterative discussions with all five authors present that yielded recurrent topics and themes by organizing the data around significant thematic units; the final six major themes were derived from twelve codes. The themes, which describe the impact of AI systems, were as follows: (1) Quantity and Quality, (2) Responsibility, (3) Just-in-time Support, (4) Agency, (5) Connection, and (6) Surveillance. The findings below are presented according to these themes.

The central theme of participants’ responses, which stood out repeatedly in our study, was that adopting AI systems in online learning can enable personalized learner–instructor interaction at scale but at the risk violating social boundaries. Participants were concerned that AI systems could create responsibility, agency, and surveillance issues in online learning if they violated social boundaries in each factor of learner–instructor interaction (i.e., communication, support, and presence). Table 5 summarizes the perceived benefits and concerns of students and instructors about the impact of AI systems on learner–instructor interaction, as noted with ( +) and ( −) respectively. Each quote outlines whether the response came from a student (“S”) or an instructor (“I”).

Communication

In online learning environments, communication refers to questions and answers between students and the instructor about topics directly related to learning contents, such as instructional materials, assignments, discussions, and exams (Kang & Im, 2013 ). Students and instructors expect AI systems will positively impact the quantity and quality of communication between them but bears the risk causing miscommunication and responsibility issues, as described below.

Quantity and quality

Students believe that the anonymity afforded by AI would make them less self-conscious and, as a result, allow them to ask more questions . In online learning environments, students are generally afraid to ask questions to their instructors during class, primarily because they “worry that someone already asked it” (S4) or “don't want to seem dumb by instructors or peers” (S10). Students perceive that the anonymity from both an AI Teaching Assistant (Scenario 1) and an AI Companion (Scenario 2) would make them “less afraid to ask questions” (S10), “wouldn't feel bad about wasting the professor's time” (S11), and would be “less distracting to class” (S12). Bluntly put, participant S11 stated: “If it’s a dumb question, I’ve got an AI to handle it for me. The AI won't judge me. The AI is not thinking like, wow, what an idiot.” S5 expanded on this idea, mentioning that asking questions to an AI removes self-consciousness that typically exists in instructional communications: “… you don’t feel like you’re bothering a person by asking the questions. You can’t really irritate an AI, so you can ask as many as you need to.” As a result, all 12 students answered that AI systems would nudge them to ask more questions in online learning.

Instructors believe that AI could help answer simple, repetitive questions, which would allow them to focus on more meaningful communication with students . Answering repetitive questions from students takes a huge amount of time (I11). Instructors reflected that the time saved from tedious tasks, such as answering administrative questions, could allow course teams to focus on more content-based questions (I10). Because an AI Teaching Assistant (Scenario 1) answers students’ repetitive questions and AI Grading Assistance (Scenario 3) and AI Peer Review (Scenario 4) enable fast feedback loops, instructors can communicate more meaningfully with students by helping to “focus more on new questions” (I6) or “use their time for more comprehensive or more irregular questions” (I4). As well-stated by I10: “I think it allows us time to have conversations that are more meaningful… in some ways you're choosing quality over quantity. The more time I have, the more time, I can do things like answer emails or answer things on Piazza, things that actually will communicate with the student.”

Responsibility

Although students believe AI systems would improve the quantity and quality of instructional communication, they worry that AI could give unreliable answers and negatively impact their grades . For example, S4 worried that “I just want to make sure it’s a really reliable source, because if the AI is answering questions from students, and then they’re going to apply that answer to the way they do their work in the future, and it might be marked wrong. Then it's hard to go to the instructor and say, oh, this answer was what was given to me, but you said it was wrong.” Most students (10 out of 12) feel like the lack of explainability of AI would make it hard to blame despite the fact that it may hold a position of responsibility in some situations, such as answering questions where its answers should be considered as truth. S9 said that “Whereas with AI and just intelligent systems that you don't fully understand the back end to in a sense, it’s harder to decipher the reasoning behind the answer or why they gave that answer.” In particular, students are concerned about how instructors would react if something went wrong because they trusted the AI. S11 expects that “I can see a lot of my fellow engineering students finding more room to argue for their marks. I can see people not being as willing to accept their fate with this kind of system.”

Instructors predicted conflicts between students and the instructor due to AI-based misunderstandings or misleadingness . For example, a conflict could arise from potential discrepancies between answers from AI, the instructor, and human TAs. As expressed by I4, “Students will argue that, oh AI is wrong. I demand a better assessment right? So, you can say that easily for the AI. But for the authoritative figure like TA and instructor, maybe it's hard to do that.” Similarly, I6 argued a conflict could stem from the opposite direction: “If an AI gives students a great suggestion, if the instructor and TA decided to regrade, it would just be a lot of trouble.” Several instructors (five out of 11) also worried about conflicts that could arise from the quality of response. I1 said that “The concern is the quality of the response, given that there can be ambiguity in the way the students post questions. My concern here is that the algorithm may respond incorrectly or obliquely.” I8 also cautioned AI-based misunderstandings or misleadingness: “If you have a conversation in person, you can always clarify misunderstandings or things like that. I don't think a machine can do that yet. So there's a bit of a potential for misunderstandings so misleading the students.”

In online learning environments, support refers to the instructor’s instructional management for students, such as providing feedback, explanations, or recommendations directly related to what is being taught (Kang & Im, 2013 ). Students and instructors expect a positive impact from AI systems in terms of enabling just-in-time personalized support for students at scale, but they expect a negative impact in that excessive support could reduce student agency and ownership of learning.

Just-in-time support

Students believe that AI would support personalized learning experiences, particularly with studying and group projects . Ultimately, all 12 students felt that AI could help them work to their strengths, mainly in scenarios regarding instructor-independent activities like studying (Scenario 5, 6, 8) and group projects (Scenario 7). Students like S2, S3, and S9 focused on how adaptive technologies could make studying more effective and efficient, as it would “allow [them] to fully understand the concept of what [they’re] learning,” and “allows for them to try and focus on where they might be weaker.” In some cases, the sense of personalization led students to describe the systems as if they could fulfill roles as members of the course team. For example, S1 referred to the Adaptive Quiz system (Scenario 8) as a potential source of guidance: “I think being able to have that quiz to help me, guide me, I’m assuming it would help me.” Likewise, S5 described the presence of an AI Group Project Organizer (Scenario 7) as “having a mentor with you, helping you do it” which would help students “focus more on maybe just researching things, writing their papers, whatever they need to do for the project.”

Instructors believe AI could be effectively leveraged to help students receive just-in-time personalized support . I1 said that “one of the best learning mechanisms is to be confronted right away with the correct answer or the correct way of finding the right answer” when doing quizzes and assignments. Many instructors (10 out of 11) expressed approval towards AI-based Intelligent Suggestions (Scenario 5) and an Adaptive Quiz system (Scenario 8). All 11 instructors appreciated how immediate feedback afforded by AI could help students study and effectively understand gaps in their knowledge, particularly at times when they would be unavailable. Similarly, I4 and I11 appreciated that AI could support students who would otherwise be learning asynchronously. For example, AI systems could be supportive of student engagement “because the students are getting real-time answers, particularly in an online world where they may not be in the same time zone, this is a synchronous type [of] learning event for them where they could be doing it when they're studying” (I11).

Despite the fact that students appreciated the support that they could potentially receive from AI, students perceived that canned and standardized support might have a negative influence on their ability to learn effectively . For example, S11 shared how he felt the usage of systems that collect engagement data would “over standardize” the learning process by prescribing how an engaged student would or should act. He likened some of the AI examples to “helicopter parenting,” expressing that guidance—whether from an AI or parent—can set an arbitrary pace for a student to follow, despite the fact that the learning experience should involve “learning about yourself and going at your own pace.” Several other students (four out of 12) were concerned with the potential effect of a system like the AI Group Project Organizer (Scenario 7), citing concerns that students “wouldn’t put that much effort” into their group projects because “it might just end up AI doing all the work for them” (S2). Similarly, S6 focused on how AI could detract from the fact that experiences with schoolwork can help students later in life: “… I think it’s like giving them a false sense of security in the sense that like, I’m so used to doing projects with this AI helper that when I go into the real world, I’m not going to be ready. I’m just not going to be prepared for it.”

Instructors are similarly wary of the fact that too much support from AI could take away students' opportunities for exploration and discovery . Many instructors (nine out of 11) were concerned that students could lose opportunities to learn new skills or learn from their mistakes. Responding about the AI Group Project Organizer (Scenario 7), I7 stressed that she wouldn’t want to standardize inconsistent group projects since part of an instructor’s job is “helping people understand how group work is conducted… [and] if you’re just laying on a simple answer, you miss that opportunity.” Similarly, other instructors (five out of 12)—primarily those in humanities-based fields—were concerned “it may take the creativity away from the students” since students’ projects “can be hugely different from each other, yet equally good,” and suggestions based on historical data could steer students towards certain directions (I6). I4 even expressed that he currently tries “not to share previous work because [he] thinks that restricts their creativity.” After experiencing all the storyboards related to AI-powered support, I11 presented a vital question: “At what stage is it students’ work and what stage is it the AI’s algorithm?”.

In online learning environments, presence refers to a factor that makes students and instructors perceive each other’s existence during the learning process (Kang & Im, 2013 ). Students and instructors expect the impact of AI systems to be positive in terms of giving them a feeling of improved connectivity, and to be negative in terms of increasing the risk of surveillance problems.

Students believe that AI can address privacy concerns and support learner–instructor connections by providing social interaction cues without personal camera information . Many students (10 out of 12) stated that they don’t want to turn on their camera in online courses, even though turning off the camera adversely affects their presence in class, because they have concerns like: “looking like a mess” (S1), “just in my pajamas” (S2), and “feeling too invasive” (S4). Specifically, S9 stated that turning on the camera “makes you more anxious and conscious of what you’re doing and as a result, it deters from you engaging with the content.” In this sense, most students (11 out of 12) liked the Virtual Avatar system (Scenario 9), where AI communicates student facial expressions and body language to the instructor via a virtual avatar. Students expect that this will make them “feel more comfortable going to lecture” (S2), “feel less intrusive for at home learning” (S4), and “showcase much more of their expression or confusion or understanding” (S10). Overall, many students (nine out of 12) appreciated the potential of AI systems as “it solves the problem of not needing to show your actual face, but you can still get your emotions across to the instructor” (S10).

Instructors believe that the addition of AI would help them become more aware of students’ needs . Many instructors (10 out of 11), particularly those that taught larger undergraduate courses, stated that students tend to turn off their cameras in online learning spaces, “so something that you really, really miss from teaching online is reading body language” (I10). Instructors generally expressed that AI systems like the Virtual Avatar (Scenario 9) and the AI Facial Analytics (Scenario 11) could be helpful, due to the fact that they would allow students to share their body language and facial expressions without directly sharing their video feed. I4 appreciated that the AI Facial Analytics could automate the process of looking at students’ faces “to see if they got it.” Similarly, I5 liked that a Virtual Avatar could give “any sign that someone is listening,” as “it’s sometimes very tough, especially if [she’s] making a joke.” Furthermore, I4 emphasized that turning on the camera can be helpful not just for the instructor but also for students’ own accountability since “if students don’t turn on the camera, it’s very likely that they are going to do something else.” Overall, instructors appreciated AI’s ability to provide critical information to understand how students are doing and how they feel in online courses.

Surveillance

Although AI can strengthen the connection between students and instructors, students are uncomfortable with the measurement of their unconscious behavior, such as eye tracking or facial expression analysis, because it feels like surveillance . All 12 students discussed how they would be anxious about being represented by unconscious eye-tracking data. S1 professed: “I don't really know what my eyes are doing. I think it might just make me a little nervous when it comes to taking quizzes or tests and all that. I might be scared that I might have accidentally cheated.” S12 additionally spoke on how that would make her more anxious when sending emails or asking questions due to concern that instructors would judge him based on his unconscious behavior before taking care of his questions. Note that most students (10 out of 12) feel uncomfortable with AI Facial Analytics (Scenario 11). For example, S6 was concerned that facial expression is “something that happens [that] might be outside of your control,” so AI might miss the nuance of authentic human emotion and flatten and simplify it in a way that might cause more confusion. In a similar vein, S11 said that “The nuances of social interaction is something that should be left up to humans and not guided because it’s innately something that, that’s what makes us human is the social interaction portion.” Overall, students complained that they didn’t want to use AI’s measures of unconscious behavior, such as eye tracking or facial expression analysis, even if there are positive aspects.

Instructors were negative about relying on AI interpretation to understand students’ social interaction cues . All instructors felt uncomfortable with collecting private data, such as eye movements and facial expressions of students through AI, because “not all the students feel comfortable sharing their private information with the instructor” (I2, I5). Additionally, I9 was concerned that AI Facial Analytics might force students to smile to get a good engagement score, which could adversely affect online learning itself. In this sense, many instructors (nine out of 11) declined to use AI systems that use eye tracking and facial expression analysis in their online courses. Furthermore, I6 would rather “choose to rely on my own kind of sense of the classroom dynamic instead of AI systems” because she believed that the social relationship between students and instructors should be authentic. Plus, other instructors stated they “don’t have time to check all of the interface[s]”, or would have trouble “knowing that that data is accurately reflecting, [that] the student is responding to [their] content” rather than extraneous stimulation in their personal environments (I3, I7). Overall, instructors were uncomfortable with AI giving detailed information about how students engage with their online courses, and they wanted to understand these social interaction cues for themselves.

In summary, students and instructors expect that AI systems will benefit learner–instructor interaction in online learning in terms of improving the quantity and quality of communication, enabling just-in-time personalized support for students at scale, and giving them a feeling of improved connectivity. However, at the same time, students and instructors were concerned that AI systems could create responsibility, agency, and surveillance issues in online learning if they violated social boundaries. These boundaries that make AI perceived to be negative will be discussed in the next section.

Discussion and conclusion

Our research question focused on examining how students and instructors perceive the impact of AI systems on learner–instructor interaction (inter alia, communication, support, and presence) in online learning. Although the growing body of AIEd research has been conducted to investigate the useful functionalities of AI systems (Seo et al., 2020b ; Popenici & Kerr, 2017 ; Zawacki-Richter et al., 2019 ), little has been done to understand students’ and instructors’ concerns on AI systems. Recent use of AI systems in online learning showed that careless application can cause surveillance and privacy issues (Lee, 2020 ), which makes students feel uncomfortable (Bajaj & Li, 2020 ). In this study, we found that students and instructors perceive the impact of AI systems as double-edged swords. Consequently, although AI systems have been positively recognized for improving the quantity and quality of communication, for providing just-in-time, personalized support for large-scale students, and for improving the feeling of connection, there were concerns about responsibility, agency, and surveillance issues. In fact, what students and instructors perceive negatively often stemmed from the positive aspects of AI systems. For example, students and instructors appreciated AI’s immediate communication, but at the same time they were concerned about AI-based misunderstandings or misleadingness. Although students and instructors valued the just-in-time, personalized support of AI, they feared that AI would limit their ability to learn independently. Students and instructors valued the social interaction cues provided by AI, but they are uncomfortable with the loss of privacy due to AI’s excessive data collection. As shown in Table 6 , this study provides rich opportunities to identify the boundaries beyond which AI systems are perceived as “invasive.”

First, although AI systems improve instructional communication due to the anonymity it can provide for students, students were concerned about responsibility issues that could arise when AI’s unreliable and unexplained answers lead to negative consequences. For instance, when communicating with an AI Teaching Assistant, the black-box nature of the AI system leaves no choices for students to check whether the answers from AI are right or wrong (Castelvecchi, 2016 ). Accordingly, students believe they would have a hard time deciphering the reasoning behind an AI’s answer. This can result in serious responsibility issues if students apply an AI’s answers to their tests but instructors mark them as wrong. As well, students would find more room to argue for their marks because of AI’s unreliability.

Acknowledging that AI systems cannot always provide the right answer, a potential solution to this problem is to ensure the system is explainable. Explainability refers to the ability to offer human-understandable justifications for the AI’s output or procedures (Gunning, 2017 ). Explainability gives students the opportunity to check whether an AI’s answer is right or wrong by themselves, and in doing so can make AI more reliable and responsible (Gunning, 2017 ). Explainability should be the boundary that determines students’ trust and acceptance of AI systems. How to ensure the explainability of AI systems in the online learning communication context will be an interesting research topic. For example, instead of providing unreliable answers that may mislead or confuse students, AI systems should connect students to relevant sources of information that students can navigate on their own.

Second, while AI systems enable some degree of personalized support, there is a risk of over-standardizing the learning process by prescribing how an engaged student would or should act. Despite the fact that students appreciate the support that they could potentially receive from AI systems, students also worry that canned and standardized support would have a negative influence on their agency over their own learning. Instructors are similarly wary of the fact that too much support from AI systems could take away students’ opportunities for exploration and discovery. Many instructors were concerned that students could lose opportunities to learn new skills or learn from their mistakes.

A solution to mediate this challenge may be to keep instructors involved. The role of AI systems in online education should not be to reduce learning to a set of canned and standardized procedures that reduce the student agency, but rather to enhance human thinking and augment the learning process. In practice, adaptive support is often jointly enacted by AI systems and human facilitators, such as instructors or peers (Holstein et al., 2020 ). In this context, Baker ( 2016 , p. 603) tried to reconcile humans with AI systems by combining “stupid tutoring systems and intelligent humans.” AI systems can process large amounts of information quickly, but do not respond well to complex contexts. Humans cannot process information as AI systems do, but instead they are flexible and intelligent in a variety of contexts. When AI systems bring human beings into the decision-making loop and try to inform them, humans can learn more efficiently and effectively (Baker, 2016 ). The human-in-the-loop is the solution to ensure students’ perceived agency in online learning. How to balance artificial and human intelligences to promote students’ agency is an important research direction (e.g., goldilocks conditions for human–AI interaction; Seo et al., 2020a ).

Third, even though AI strengthens the perceived connection between students and instructors, students are uncomfortable with the measurement of their unconscious behavior, such as facial expression analysis or eye tracking, as it feels like surveillance. While most students liked the Virtual Avatar system (Scenario 9) where AI simply delivers student facial expressions and body language to the instructor via an avatar, students declined to use the AI Facial Analytics (Scenario 11), which might miss the nuance of social interaction by flattening and simplifying it in a way that might cause more confusion. Interpreting social interaction from unconscious behavior could be the boundary beyond which AI systems are perceived as “invasive.” Students felt uncomfortable about being represented by their unconscious behavior because they did not know what their gaze or face was doing. Stark ( 2019 ) described facial recognition as the plutonium of AI: “[facial recognition] is dangerous, racializing, and has few legitimate uses; facial recognition needs regulation and control on par with nuclear waste.” Students complained about their presence being represented by the interpretation of the AI system. In a similar vein, the instructor negatively felt the AI system’s involvement in interpreting the meaning of student behavior.

Establishing clear, simple, and transparent data norms and agreements about the nature of data being collected from students and what kind of data is okay to be presented to instructors are important considerations for future research (Ferguson, 2019 ; Tsai et al., 2020 ).

While this study revealed important findings and implications for using AI systems in online learning, the study recognizes some limitations that should be considered when interpreting the patterns of the results. First, although this study attempted to capture various forms of AI systems in online learning based on the ideations from HCI designers and AI experts, it might be possible that other kinds of AI systems exist. Different AI systems might offer different insights. As such, further studies can be conducted with different kinds of AI systems. Next, students’ and instructors’ perceptions of AI systems could be affected by different disciplines. In the current study, we recruited students and instructors in diverse majors and subjects. Although this helped us to generalize our findings from participants with diverse backgrounds, there’s more room to investigate how students and instructors in different disciplines perceive AI systems differently. In our findings, we anecdotally found that instructors in humanities-based fields were more concerned about rapport with students and students’ creativity in courses compared to other disciplines. In order to fully investigate this, future research should consider the different learner–instructor interaction needs of participants from different majors (e.g., engineering vs. humanities).

Another limitation is that the study was conducted by reading the storyboards, rather than directly interacting with AI systems. This might have limited participants’ perceptions about the AI systems. If participants have continuous, direct interactions with the AI systems in the real world, their perceptions may change. As such, future researchers should examine students’ responses to direct exposures of AI systems. This can be accomplished in a variety of ways. For example, one could conduct a lab experiment using virtual reality, the wizard-of-oz method, or the user enactment method to see how students actually respond to AI systems. It would also be meaningful to conduct a longitudinal study to understand whether and/or how student perceptions would change over time.

Theoretical implications

This study provides theoretical implications for a learner–instructor interaction framework by highlighting and mapping key challenges in AI-related ethical issues (i.e. responsibility, agency, and surveillance) in online learning environments. Researchers have requested clear ethical guidelines for future research to prevent AI systems from accidently harming people (Loi et al., 2019 ). Although several ethical frameworks and professional codes of conduct have been developed to mitigate the potential dangers and risks of AI in education, significant debates continue about their specific impact on students and instructors (Williamson & Eynon, 2020 ). The results of this study increase our understanding of the boundaries that determine student and instructor trust and acceptance of AI systems, and provide a theoretical background for designing AI systems that positively support learner–instructor interactions in a variety of learning situations.

Practical implications

This study has practical implications for both students and instructors. Interestingly, most of the negative experiences with AI systems came from students’ unrealistic expectations and misunderstandings about AI systems. The AI system’s answer is nothing more than an algorithm based on accumulated data, yet students typically expect the AI system to be accurate. These misconceptions can be barriers to the effective use of AI systems by students and instructors. To address this, it is important to foster AI literacy in students and instructors without a technical background (Long & Magerko, 2020 ). For example, recent studies have published guides on how to incorporate AI into K-12 curricula (Touretzky et al., 2019 ), and researchers are exploring how to engage young learners in creative programming activities involving AI (Zimmermann-Niefield et al., 2019 ).

Furthermore, in order to minimize the negative impact of AI systems on learner–instructor interaction, it is important to address tensions where AI systems violate the boundaries between students and instructors (e.g., responsibility, agency, and surveillance issues). We proposed that future AI systems should ensure explainability, human-in-the-loop, and careful data collection and presentation. By doing so, AI systems will be more closely integrated into future online learning. It is important to note that the present study does not argue that AI systems will replace the entire role of human instructors. Rather, in the online learning of the future, AI systems and humans will work closely together, and for this, it is important to use these systems with consideration about perceived affordances and drawbacks.

Availability of data and materials

The full set of storyboards and an example of our codes can be viewed at https://osf.io/3aj5v/?view_only=bc5fa97e6f7d46fdb66872588ff1e22e .

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Acknowledgements

The authors would like to thank all students, instructors, and AI experts for their great support and inspiration.

This study was financially supported by Seoul National University of Science & Technology.

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Kyoungwon Seo

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Joice Tang & Dongwook Yoon

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KS: conceptualization, methodology, investigation, writing—original draft, visualization, project administration; JT: conceptualization, methodology, investigation, data curation, writing—original draft, project administration; IR: writing—review and editing, conceptualization; SF: writing—review and editing, supervision, project administration, funding acquisition; DY: writing—review and editing, conceptualization, supervision, project administration. KS and JT contribute equally. All authors read and approved the final manuscript.

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Appendix A: summary of the AI experts’ information

Appendix b: speed dating interview script.

1. Introduction

Hello, thank you for taking time for this interview today. We’re really looking forward to learning from your experience with online learning.

Today, we’ll be discussing a set of 11 storyboards that are related to AI systems for online courses. When reading the storyboards, try to think about them in the context of your discipline and experiences. Our goal is to reveal your perceptions of AI in online learning.

For your information, the interview will take about 60 min. The interview will be audio recorded but will be confidential and de-identified.

2. For each storyboard

Do you think this AI system supports learner–instructor interaction? Yes, no, or do you feel neutral? Why?

[When the participant is a student] Would the incorporation of this AI system into your courses change your interaction with the instructor?

[When the participant is an instructor] Would incorporating this AI system into the course change how you interact with students?

Do you have any reservations or concerns about this AI system?

3. After examining all storyboards (capturing participants’ holistic point of view)

Of the storyboards shown today, which AI systems do you think would work well in your online classroom? Why? Also, which ones wouldn’t work well?

How do you think the adoption of AI would affect the relationship between students and the instructor?

4. Conclusion

Do you have any final comments?

Thank you for taking the time to interview with us today. We really appreciate that you took time to participate in our study and share your expertise. Your insights were really helpful.

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Seo, K., Tang, J., Roll, I. et al. The impact of artificial intelligence on learner–instructor interaction in online learning. Int J Educ Technol High Educ 18 , 54 (2021). https://doi.org/10.1186/s41239-021-00292-9

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Research Paper – Structure, Examples and Writing Guide

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Research Paper

Definition:

Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.

Introduction

The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

• Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
• Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
• Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
• Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
• Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
• Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
• Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
• Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
• Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

• Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
• Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
• Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

• How often do you use social media per day?
• Less than 30 minutes
• 30 minutes to 1 hour
• 1 to 2 hours
• 2 to 4 hours
• More than 4 hours
• Which social media platforms do you use?
• Others (Please specify)
• How often do you experience the following on social media?
• Social comparison (comparing yourself to others)
• Cyberbullying
• Fear of Missing Out (FOMO)
• Have you ever experienced any of the following mental health problems in the past month?
• Do you think social media use has a positive or negative impact on your mental health?
• Very positive
• Somewhat positive
• Somewhat negative
• Very negative
• In your opinion, which factors contribute to the negative impact of social media on mental health?
• Social comparison
• In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
• Education on healthy social media use
• Counseling for mental health problems caused by social media
• Social media detox programs
• Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

• Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
• Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
• Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
• Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
• Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

• For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
• For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
• To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
• To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

• To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
• To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
• To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
• To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

• Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
• Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
• Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
• Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
• Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
• Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

• Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
• Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
• Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
• Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
• Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
• Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

• Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
• Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
• Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
• Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
• Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
• Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Research Paper Examples

Research paper examples are of great value for students who want to complete their assignments timely and efficiently. If you are a student in the university, your first stop in the quest for research paper examples will be the campus library where you can get to view the research sample papers of lecturers and other professionals in diverse fields plus those of fellow students who preceded you in the campus. Many college departments maintain libraries of previous student work, including large research papers, which current students can examine.

Embark on a journey of academic excellence with iResearchNet, your premier destination for research paper examples that illuminate the path to scholarly success. In the realm of academia, where the pursuit of knowledge is both a challenge and a privilege, the significance of having access to high-quality research paper examples cannot be overstated. These exemplars are not merely papers; they are beacons of insight, guiding students and scholars through the complex maze of academic writing and research methodologies.

At iResearchNet, we understand that the foundation of academic achievement lies in the quality of resources at one’s disposal. This is why we are dedicated to offering a comprehensive collection of research paper examples across a multitude of disciplines. Each example stands as a testament to rigorous research, clear writing, and the deep understanding necessary to advance in one’s academic and professional journey.

Access to superior research paper examples equips learners with the tools to develop their own ideas, arguments, and hypotheses, fostering a cycle of learning and discovery that transcends traditional boundaries. It is with this vision that iResearchNet commits to empowering students and researchers, providing them with the resources to not only meet but exceed the highest standards of academic excellence. Join us on this journey, and let iResearchNet be your guide to unlocking the full potential of your academic endeavors.

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Importance of Research Paper Examples

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A Sample Research Paper on Child Abuse

A research paper represents the pinnacle of academic investigation, a scholarly manuscript that encapsulates a detailed study, analysis, or argument based on extensive independent research. It is an embodiment of the researcher’s ability to synthesize a wealth of information, draw insightful conclusions, and contribute novel perspectives to the existing body of knowledge within a specific field. At its core, a research paper strives to push the boundaries of what is known, challenging existing theories and proposing new insights that could potentially reshape the understanding of a particular subject area.

The objective of writing a research paper is manifold, serving both educational and intellectual pursuits. Primarily, it aims to educate the author, providing a rigorous framework through which they engage deeply with a topic, hone their research and analytical skills, and learn the art of academic writing. Beyond personal growth, the research paper serves the broader academic community by contributing to the collective pool of knowledge, offering fresh perspectives, and stimulating further research. It is a medium through which scholars communicate ideas, findings, and theories, thereby fostering an ongoing dialogue that propels the advancement of science, humanities, and other fields of study.

Research papers can be categorized into various types, each with distinct objectives and methodologies. The most common types include:

• Analytical Research Paper: This type focuses on analyzing different viewpoints represented in the scholarly literature or data. The author critically evaluates and interprets the information, aiming to provide a comprehensive understanding of the topic.
• Argumentative or Persuasive Research Paper: Here, the author adopts a stance on a contentious issue and argues in favor of their position. The objective is to persuade the reader through evidence and logic that the author’s viewpoint is valid or preferable.
• Experimental Research Paper: Often used in the sciences, this type documents the process, results, and implications of an experiment conducted by the author. It provides a detailed account of the methodology, data collected, analysis performed, and conclusions drawn.
• Survey Research Paper: This involves collecting data from a set of respondents about their opinions, behaviors, or characteristics. The paper analyzes this data to draw conclusions about the population from which the sample was drawn.
• Comparative Research Paper: This type involves comparing and contrasting different theories, policies, or phenomena. The aim is to highlight similarities and differences, thereby gaining a deeper understanding of the subjects under review.
• Cause and Effect Research Paper: It explores the reasons behind specific actions, events, or conditions and the consequences that follow. The goal is to establish a causal relationship between variables.
• Review Research Paper: This paper synthesizes existing research on a particular topic, offering a comprehensive analysis of the literature to identify trends, gaps, and consensus in the field.

Understanding the nuances and objectives of these various types of research papers is crucial for scholars and students alike, as it guides their approach to conducting and writing up their research. Each type demands a unique set of skills and perspectives, pushing the author to think critically and creatively about their subject matter. As the academic landscape continues to evolve, the research paper remains a fundamental tool for disseminating knowledge, encouraging innovation, and fostering a culture of inquiry and exploration.

Browse Sample Research Papers

iResearchNet prides itself on offering a wide array of research paper examples across various disciplines, meticulously curated to support students, educators, and researchers in their academic endeavors. Each example embodies the hallmarks of scholarly excellence—rigorous research, analytical depth, and clear, precise writing. Below, we explore the diverse range of research paper examples available through iResearchNet, designed to inspire and guide users in their quest for academic achievement.

Anthropology Research Paper Examples

Our anthropology research paper examples delve into the study of humanity, exploring cultural, social, biological, and linguistic variations among human populations. These papers offer insights into human behavior, traditions, and evolution, providing a comprehensive overview of anthropological research methods and theories.

• Archaeology Research Paper
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Art Research Paper Examples

The art research paper examples feature analyses of artistic expressions across different cultures and historical periods. These papers cover a variety of topics, including art history, criticism, and theory, as well as the examination of specific artworks or movements.

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Cancer Research Paper Examples

Our cancer research paper examples focus on the latest findings in the field of oncology, discussing the biological mechanisms of cancer, advancements in diagnostic techniques, and innovative treatment strategies. These papers aim to contribute to the ongoing battle against cancer by sharing cutting-edge research.

• Breast Cancer Research Paper
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Communication Research Paper Examples

These examples explore the complexities of human communication, covering topics such as media studies, interpersonal communication, and public relations. The papers examine how communication processes affect individuals, societies, and cultures.

• Advertising Research Paper
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Crime Research Paper Examples

The crime research paper examples provided by iResearchNet investigate various aspects of criminal behavior and the factors contributing to crime. These papers cover a range of topics, from theoretical analyses of criminality to empirical studies on crime prevention strategies.

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Criminal Justice Research Paper Examples

Our criminal justice research paper examples delve into the functioning of the criminal justice system, exploring issues related to law enforcement, the judiciary, and corrections. These papers critically examine policies, practices, and reforms within the criminal justice system.

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Criminal Law Research Paper Examples

These examples focus on the legal aspects of criminal behavior, discussing laws, regulations, and case law that govern criminal proceedings. The papers provide an in-depth analysis of criminal law principles, legal defenses, and the implications of legal decisions.

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Criminology Research Paper Examples

iResearchNet’s criminology research paper examples study the causes, prevention, and societal impacts of crime. These papers employ various theoretical frameworks to analyze crime trends and propose effective crime reduction strategies.

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Culture Research Paper Examples

The culture research paper examples examine the beliefs, practices, and artifacts that define different societies. These papers explore how culture shapes identities, influences behaviors, and impacts social interactions.

• Advertising and Culture Research Paper
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Economics Research Paper Examples

Our economics research paper examples offer insights into the functioning of economies at both the micro and macro levels. Topics include economic theory, policy analysis, and the examination of economic indicators and trends.

• Budget Research Paper
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Education Research Paper Examples

These examples address a wide range of issues in education, from teaching methods and curriculum design to educational policy and reform. The papers aim to enhance understanding and improve outcomes in educational settings.

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Health Research Paper Examples

The health research paper examples focus on public health issues, healthcare systems, and medical interventions. These papers contribute to the discourse on health promotion, disease prevention, and healthcare management.

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History Research Paper Examples

Our history research paper examples cover significant events, figures, and periods, offering critical analyses of historical narratives and their impact on present-day society.

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Leadership Research Paper Examples

These examples explore the theories and practices of effective leadership, examining the qualities, behaviors, and strategies that distinguish successful leaders in various contexts.

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Mental Health Research Paper Examples

The mental health research paper examples provided by iResearchNet discuss psychological disorders, therapeutic interventions, and mental health advocacy. These papers aim to raise awareness and improve mental health care practices.

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Political Science Research Paper Examples

Our political science research paper examples analyze political systems, behaviors, and ideologies. Topics include governance, policy analysis, and the study of political movements and institutions.

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Psychology Research Paper Examples

These examples delve into the study of the mind and behavior, covering a broad range of topics in clinical, cognitive, developmental, and social psychology.

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Sociology Research Paper Examples

The sociology research paper examples examine societal structures, relationships, and processes. These papers provide insights into social phenomena, inequality, and change.

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Technology Research Paper Examples

Our technology research paper examples address the impact of technological advancements on society, exploring issues related to digital communication, cybersecurity, and innovation.

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Other Research Paper Examples

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• Other Sample Research Papers

Each category of research paper examples provided by iResearchNet serves as a valuable resource for students and researchers seeking to deepen their understanding of a specific field. By offering a comprehensive collection of well-researched and thoughtfully written papers, iResearchNet aims to support academic growth and encourage scholarly inquiry across diverse disciplines.

Sample Research Papers: To Read or Not to Read?

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Blueprint for Academic Success

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A research paper is an academic piece of writing, so you need to follow all the requirements and standards. Otherwise, it will be impossible to get the high results. To make it easier for you, we have analyzed the structure and peculiarities of a sample research paper on the topic ‘Child Abuse’.

The paper includes 7300+ words, a detailed outline, citations are in APA formatting style, and bibliography with 28 sources.

To write any paper you need to write a great outline. This is the key to a perfect paper. When you organize your paper, it is easier for you to present the ideas logically, without jumping from one thought to another.

In the outline, you need to name all the parts of your paper. That is to say, an introduction, main body, conclusion, bibliography, some papers require abstract and proposal as well.

A good outline will serve as a guide through your paper making it easier for the reader to follow your ideas.

I. Introduction

Ii. estimates of child abuse: methodological limitations, iii. child abuse and neglect: the legalities, iv. corporal punishment versus child abuse, v. child abuse victims: the patterns, vi. child abuse perpetrators: the patterns, vii. explanations for child abuse, viii. consequences of child abuse and neglect, ix. determining abuse: how to tell whether a child is abused or neglected, x. determining abuse: interviewing children, xi. how can society help abused children and abusive families, introduction.

An introduction should include a thesis statement and the main points that you will discuss in the paper.

A thesis statement is one sentence in which you need to show your point of view. You will then develop this point of view through the whole piece of work:

‘The impact of child abuse affects more than one’s childhood, as the psychological and physical injuries often extend well into adulthood.’

Child abuse is a very real and prominent social problem today. The impact of child abuse affects more than one’s childhood, as the psychological and physical injuries often extend well into adulthood. Most children are defenseless against abuse, are dependent on their caretakers, and are unable to protect themselves from these acts.

Childhood serves as the basis for growth, development, and socialization. Throughout adolescence, children are taught how to become productive and positive, functioning members of society. Much of the socializing of children, particularly in their very earliest years, comes at the hands of family members. Unfortunately, the messages conveyed to and the actions against children by their families are not always the positive building blocks for which one would hope.

In 2008, the Children’s Defense Fund reported that each day in America, 2,421 children are confirmed as abused or neglected, 4 children are killed by abuse or neglect, and 78 babies die before their first birthday. These daily estimates translate into tremendous national figures. In 2006, caseworkers substantiated an estimated 905,000 reports of child abuse or neglect. Of these, 64% suffered neglect, 16% were physically abused, 9% were sexually abused, 7% were emotionally or psychologically maltreated, and 2% were medically neglected. In addition, 15% of the victims experienced “other” types of maltreatment such as abandonment, threats of harm to the child, and congenital drug addiction (National Child Abuse and Neglect Data System, 2006). Obviously, this problem is a substantial one.

In the main body, you dwell upon the topic of your paper. You provide your ideas and support them with evidence. The evidence include all the data and material you have found, analyzed and systematized. You can support your point of view with different statistical data, with surveys, and the results of different experiments. Your task is to show that your idea is right, and make the reader interested in the topic.

In this example, a writer analyzes the issue of child abuse: different statistical data, controversies regarding the topic, examples of the problem and the consequences.

Several issues arise when considering the amount of child abuse that occurs annually in the United States. Child abuse is very hard to estimate because much (or most) of it is not reported. Children who are abused are unlikely to report their victimization because they may not know any better, they still love their abusers and do not want to see them taken away (or do not themselves want to be taken away from their abusers), they have been threatened into not reporting, or they do not know to whom they should report their victimizations. Still further, children may report their abuse only to find the person to whom they report does not believe them or take any action on their behalf. Continuing to muddy the waters, child abuse can be disguised as legitimate injury, particularly because young children are often somewhat uncoordinated and are still learning to accomplish physical tasks, may not know their physical limitations, and are often legitimately injured during regular play. In the end, children rarely report child abuse; most often it is an adult who makes a report based on suspicion (e.g., teacher, counselor, doctor, etc.).

Even when child abuse is reported, social service agents and investigators may not follow up or substantiate reports for a variety of reasons. Parents can pretend, lie, or cover up injuries or stories of how injuries occurred when social service agents come to investigate. Further, there is not always agreement about what should be counted as abuse by service providers and researchers. In addition, social service agencies/agents have huge caseloads and may only be able to deal with the most serious forms of child abuse, leaving the more “minor” forms of abuse unsupervised and unmanaged (and uncounted in the statistical totals).

While most laws about child abuse and neglect fall at the state levels, federal legislation provides a foundation for states by identifying a minimum set of acts and behaviors that define child abuse and neglect. The Federal Child Abuse Prevention and Treatment Act (CAPTA), which stems from the Keeping Children and Families Safe Act of 2003, defines child abuse and neglect as, at minimum, “(1) any recent act or failure to act on the part of a parent or caretaker which results in death, serious physical or emotional harm, sexual abuse, or exploitation; or (2) an act or failure to act which presents an imminent risk or serious harm.”

Using these minimum standards, each state is responsible for providing its own definition of maltreatment within civil and criminal statutes. When defining types of child abuse, many states incorporate similar elements and definitions into their legal statutes. For example, neglect is often defined as failure to provide for a child’s basic needs. Neglect can encompass physical elements (e.g., failure to provide necessary food or shelter, or lack of appropriate supervision), medical elements (e.g., failure to provide necessary medical or mental health treatment), educational elements (e.g., failure to educate a child or attend to special educational needs), and emotional elements (e.g., inattention to a child’s emotional needs, failure to provide psychological care, or permitting the child to use alcohol or other drugs). Failure to meet needs does not always mean a child is neglected, as situations such as poverty, cultural values, and community standards can influence the application of legal statutes. In addition, several states distinguish between failure to provide based on financial inability and failure to provide for no apparent financial reason.

Statutes on physical abuse typically include elements of physical injury (ranging from minor bruises to severe fractures or death) as a result of punching, beating, kicking, biting, shaking, throwing, stabbing, choking, hitting (with a hand, stick, strap, or other object), burning, or otherwise harming a child. Such injury is considered abuse regardless of the intention of the caretaker. In addition, many state statutes include allowing or encouraging another person to physically harm a child (such as noted above) as another form of physical abuse in and of itself. Sexual abuse usually includes activities by a parent or caretaker such as fondling a child’s genitals, penetration, incest, rape, sodomy, indecent exposure, and exploitation through prostitution or the production of pornographic materials.

Finally, emotional or psychological abuse typically is defined as a pattern of behavior that impairs a child’s emotional development or sense of self-worth. This may include constant criticism, threats, or rejection, as well as withholding love, support, or guidance. Emotional abuse is often the most difficult to prove and, therefore, child protective services may not be able to intervene without evidence of harm to the child. Some states suggest that harm may be evidenced by an observable or substantial change in behavior, emotional response, or cognition, or by anxiety, depression, withdrawal, or aggressive behavior. At a practical level, emotional abuse is almost always present when other types of abuse are identified.

Some states include an element of substance abuse in their statutes on child abuse. Circumstances that can be considered substance abuse include (a) the manufacture of a controlled substance in the presence of a child or on the premises occupied by a child (Colorado, Indiana, Iowa, Montana, South Dakota, Tennessee, and Virginia); (b) allowing a child to be present where the chemicals or equipment for the manufacture of controlled substances are used (Arizona, New Mexico); (c) selling, distributing, or giving drugs or alcohol to a child (Florida, Hawaii, Illinois, Minnesota, and Texas); (d) use of a controlled substance by a caregiver that impairs the caregiver’s ability to adequately care for the child (Kentucky, New York, Rhode Island, and Texas); and (e) exposure of the child to drug paraphernalia (North Dakota), the criminal sale or distribution of drugs (Montana, Virginia), or drug-related activity (District of Columbia).

One of the most difficult issues with which the U.S. legal system must contend is that of allowing parents the right to use corporal punishment when disciplining a child, while not letting them cross over the line into the realm of child abuse. Some parents may abuse their children under the guise of discipline, and many instances of child abuse arise from angry parents who go too far when disciplining their children with physical punishment. Generally, state statutes use terms such as “reasonable discipline of a minor,” “causes only temporary, short-term pain,” and may cause “the potential for bruising” but not “permanent damage, disability, disfigurement or injury” to the child as ways of indicating the types of discipline behaviors that are legal. However, corporal punishment that is “excessive,” “malicious,” “endangers the bodily safety of,” or is “an intentional infliction of injury” is not allowed under most state statutes (e.g., state of Florida child abuse statute).

Most research finds that the use of physical punishment (most often spanking) is not an effective method of discipline. The literature on this issue tends to find that spanking stops misbehavior, but no more effectively than other firm measures. Further, it seems to hinder rather than improve general compliance/obedience (particularly when the child is not in the presence of the punisher). Researchers have also explained why physical punishment is not any more effective at gaining child compliance than nonviolent forms of discipline. Some of the problems that arise when parents use spanking or other forms of physical punishment include the fact that spanking does not teach what children should do, nor does it provide them with alternative behavior options should the circumstance arise again. Spanking also undermines reasoning, explanation, or other forms of parental instruction because children cannot learn, reason, or problem solve well while experiencing threat, pain, fear, or anger. Further, the use of physical punishment is inconsistent with nonviolent principles, or parental modeling. In addition, the use of spanking chips away at the bonds of affection between parents and children, and tends to induce resentment and fear. Finally, it hinders the development of empathy and compassion in children, and they do not learn to take responsibility for their own behavior (Pitzer, 1997).

One of the biggest problems with the use of corporal punishment is that it can escalate into much more severe forms of violence. Usually, parents spank because they are angry (and somewhat out of control) and they can’t think of other ways to discipline. When parents are acting as a result of emotional triggers, the notion of discipline is lost while punishment and pain become the foci.

In 2006, of the children who were found to be victims of child abuse, nearly 75% of them were first-time victims (or had not come to the attention of authorities prior). A slight majority of child abuse victims were girls—51.5%, compared to 48% of abuse victims being boys. The younger the child, the more at risk he or she is for child abuse and neglect victimization. Specifically, the rate for infants (birth to 1 year old) was approximately 24 per 1,000 children of the same age group. The victimization rate for children 1–3 years old was 14 per 1,000 children of the same age group. The abuse rate for children aged 4– 7 years old declined further to 13 per 1,000 children of the same age group. African American, American Indian, and Alaska Native children, as well as children of multiple races, had the highest rates of victimization. White and Latino children had lower rates, and Asian children had the lowest rates of child abuse and neglect victimization. Regarding living arrangements, nearly 27% of victims were living with a single mother, 20% were living with married parents, while 22% were living with both parents but the marital status was unknown. (This reporting element had nearly 40% missing data, however.) Regarding disability, nearly 8% of child abuse victims had some degree of mental retardation, emotional disturbance, visual or hearing impairment, learning disability, physical disability, behavioral problems, or other medical problems. Unfortunately, data indicate that for many victims, the efforts of the child protection services system were not successful in preventing subsequent victimization. Children who had been prior victims of maltreatment were 96% more likely to experience another occurrence than those who were not prior victims. Further, child victims who were reported to have a disability were 52% more likely to experience recurrence than children without a disability. Finally, the oldest victims (16–21 years of age) were the least likely to experience a recurrence, and were 51% less likely to be victimized again than were infants (younger than age 1) (National Child Abuse and Neglect Data System, 2006).

Child fatalities are the most tragic consequence of maltreatment. Yet, each year, children die from abuse and neglect. In 2006, an estimated 1,530 children in the United States died due to abuse or neglect. The overall rate of child fatalities was 2 deaths per 100,000 children. More than 40% of child fatalities were attributed to neglect, but physical abuse also was a major contributor. Approximately 78% of the children who died due to child abuse and neglect were younger than 4 years old, and infant boys (younger than 1) had the highest rate of fatalities at 18.5 deaths per 100,000 boys of the same age in the national population. Infant girls had a rate of 14.7 deaths per 100,000 girls of the same age (National Child Abuse and Neglect Data System, 2006).

One question to be addressed regarding child fatalities is why infants have such a high rate of death when compared to toddlers and adolescents. Children under 1 year old pose an immense amount of responsibility for their caretakers: they are completely dependent and need constant attention. Children this age are needy, impulsive, and not amenable to verbal control or effective communication. This can easily overwhelm vulnerable parents. Another difficulty associated with infants is that they are physically weak and small. Injuries to infants can be fatal, while similar injuries to older children might not be. The most common cause of death in children less than 1 year is cerebral trauma (often the result of shaken-baby syndrome). Exasperated parents can deliver shakes or blows without realizing how little it takes to cause irreparable or fatal damage to an infant. Research informs us that two of the most common triggers for fatal child abuse are crying that will not cease and toileting accidents. Both of these circumstances are common in infants and toddlers whose only means of communication often is crying, and who are limited in mobility and cannot use the toilet. Finally, very young children cannot assist in injury diagnoses. Children who have been injured due to abuse or neglect often cannot communicate to medical professionals about where it hurts, how it hurts, and so forth. Also, nonfatal injuries can turn fatal in the absence of care by neglectful parents or parents who do not want medical professionals to possibly identify an injury as being the result of abuse.

Estimates reveal that nearly 80% of perpetrators of child abuse were parents of the victim. Other relatives accounted for nearly 7%, and unmarried partners of parents made up 4% of perpetrators. Of those perpetrators that were parents, over 90% were biological parents, 4% were stepparents, and 0.7% were adoptive parents. Of this group, approximately 58% of perpetrators were women and 42% were men. Women perpetrators are typically younger than men. The average age for women abusers was 31 years old, while for men the average was 34 years old. Forty percent of women who abused were younger than 30 years of age, compared with 33% of men being under 30. The racial distribution of perpetrators is similar to that of victims. Fifty-four percent were white, 21% were African American, and 20% were Hispanic/Latino (National Child Abuse and Neglect Data System, 2006).

There are many factors that are associated with child abuse. Some of the more common/well-accepted explanations are individual pathology, parent–child interaction, past abuse in the family (or social learning), situational factors, and cultural support for physical punishment along with a lack of cultural support for helping parents here in the United States.

The first explanation centers on the individual pathology of a parent or caretaker who is abusive. This theory focuses on the idea that people who abuse their children have something wrong with their individual personality or biological makeup. Such psychological pathologies may include having anger control problems; being depressed or having post-partum depression; having a low tolerance for frustration (e.g., children can be extremely frustrating: they don’t always listen; they constantly push the line of how far they can go; and once the line has been established, they are constantly treading on it to make sure it hasn’t moved. They are dependent and self-centered, so caretakers have very little privacy or time to themselves); being rigid (e.g., having no tolerance for differences—for example, what if your son wanted to play with dolls? A rigid father would not let him, laugh at him for wanting to, punish him when he does, etc.); having deficits in empathy (parents who cannot put themselves in the shoes of their children cannot fully understand what their children need emotionally); or being disorganized, inefficient, and ineffectual. (Parents who are unable to manage their own lives are unlikely to be successful at managing the lives of their children, and since many children want and need limits, these parents are unable to set them or adhere to them.)

Biological pathologies that may increase the likelihood of someone becoming a child abuser include having substance abuse or dependence problems, or having persistent or reoccurring physical health problems (especially health problems that can be extremely painful and can cause a person to become more self-absorbed, both qualities that can give rise to a lack of patience, lower frustration tolerance, and increased stress).

The second explanation for child abuse centers on the interaction between the parent and the child, noting that certain types of parents are more likely to abuse, and certain types of children are more likely to be abused, and when these less-skilled parents are coupled with these more difficult children, child abuse is the most likely to occur. Discussion here focuses on what makes a parent less skilled, and what makes a child more difficult. Characteristics of unskilled parents are likely to include such traits as only pointing out what children do wrong and never giving any encouragement for good behavior, and failing to be sensitive to the emotional needs of children. Less skilled parents tend to have unrealistic expectations of children. They may engage in role reversal— where the parents make the child take care of them—and view the parent’s happiness and well-being as the responsibility of the child. Some parents view the parental role as extremely stressful and experience little enjoyment from being a parent. Finally, less-skilled parents tend to have more negative perceptions regarding their child(ren). For example, perhaps the child has a different shade of skin than they expected and this may disappoint or anger them, they may feel the child is being manipulative (long before children have this capability), or they may view the child as the scapegoat for all the parents’ or family’s problems. Theoretically, parents with these characteristics would be more likely to abuse their children, but if they are coupled with having a difficult child, they would be especially likely to be abusive. So, what makes a child more difficult? Certainly, through no fault of their own, children may have characteristics that are associated with child care that is more demanding and difficult than in the “normal” or “average” situation. Such characteristics can include having physical and mental disabilities (autism, attention deficit hyperactivity disorder [ADHD], hyperactivity, etc.); the child may be colicky, frequently sick, be particularly needy, or cry more often. In addition, some babies are simply unhappier than other babies for reasons that cannot be known. Further, infants are difficult even in the best of circumstances. They are unable to communicate effectively, and they are completely dependent on their caretakers for everything, including eating, diaper changing, moving around, entertainment, and emotional bonding. Again, these types of children, being more difficult, are more likely to be victims of child abuse.

Nonetheless, each of these types of parents and children alone cannot explain the abuse of children, but it is the interaction between them that becomes the key. Unskilled parents may produce children that are happy and not as needy, and even though they are unskilled, they do not abuse because the child takes less effort. At the same time, children who are more difficult may have parents who are skilled and are able to handle and manage the extra effort these children take with aplomb. However, risks for child abuse increase when unskilled parents must contend with difficult children.

Social learning or past abuse in the family is a third common explanation for child abuse. Here, the theory concentrates not only on what children learn when they see or experience violence in their homes, but additionally on what they do not learn as a result of these experiences. Social learning theory in the context of family violence stresses that if children are abused or see abuse (toward siblings or a parent), those interactions and violent family members become the representations and role models for their future familial interactions. In this way, what children learn is just as important as what they do not learn. Children who witness or experience violence may learn that this is the way parents deal with children, or that violence is an acceptable method of child rearing and discipline. They may think when they become parents that “violence worked on me when I was a child, and I turned out fine.” They may learn unhealthy relationship interaction patterns; children may witness the negative interactions of parents and they may learn the maladaptive or violent methods of expressing anger, reacting to stress, or coping with conflict.

What is equally as important, though, is that they are unlikely to learn more acceptable and nonviolent ways of rearing children, interacting with family members, and working out conflict. Here it may happen that an adult who was abused as a child would like to be nonviolent toward his or her own children, but when the chips are down and the child is misbehaving, this abused-child-turned-adult does not have a repertoire of nonviolent strategies to try. This parent is more likely to fall back on what he or she knows as methods of discipline.

Something important to note here is that not all abused children grow up to become abusive adults. Children who break the cycle were often able to establish and maintain one healthy emotional relationship with someone during their childhoods (or period of young adulthood). For instance, they may have received emotional support from a nonabusing parent, or they received social support and had a positive relationship with another adult during their childhood (e.g., teacher, coach, minister, neighbor, etc.). Abused children who participate in therapy during some period of their lives can often break the cycle of violence. In addition, adults who were abused but are able to form an emotionally supportive and satisfying relationship with a mate can make the transition to being nonviolent in their family interactions.

Moving on to a fourth familiar explanation for child abuse, there are some common situational factors that influence families and parents and increase the risks for child abuse. Typically, these are factors that increase family stress or social isolation. Specifically, such factors may include receiving public assistance or having low socioeconomic status (a combination of low income and low education). Other factors include having family members who are unemployed, underemployed (working in a job that requires lower qualifications than an individual possesses), or employed only part time. These financial difficulties cause great stress for families in meeting the needs of the individual members. Other stress-inducing familial characteristics are single-parent households and larger family size. Finally, social isolation can be devastating for families and family members. Having friends to talk to, who can be relied upon, and with whom kids can be dropped off occasionally is tremendously important for personal growth and satisfaction in life. In addition, social isolation and stress can cause individuals to be quick to lose their tempers, as well as cause people to be less rational in their decision making and to make mountains out of mole hills. These situations can lead families to be at greater risk for child abuse.

Finally, cultural views and supports (or lack thereof) can lead to greater amounts of child abuse in a society such as the United States. One such cultural view is that of societal support for physical punishment. This is problematic because there are similarities between the way criminals are dealt with and the way errant children are handled. The use of capital punishment is advocated for seriously violent criminals, and people are quick to use such idioms as “spare the rod and spoil the child” when it comes to the discipline or punishment of children. In fact, it was not until quite recently that parenting books began to encourage parents to use other strategies than spanking or other forms of corporal punishment in the discipline of their children. Only recently, the American Academy of Pediatrics has come out and recommended that parents do not spank or use other forms of violence on their children because of the deleterious effects such methods have on youngsters and their bonds with their parents. Nevertheless, regardless of recommendations, the culture of corporal punishment persists.

Another cultural view in the United States that can give rise to greater incidents of child abuse is the belief that after getting married, couples of course should want and have children. Culturally, Americans consider that children are a blessing, raising kids is the most wonderful thing a person can do, and everyone should have children. Along with this notion is the idea that motherhood is always wonderful; it is the most fulfilling thing a woman can do; and the bond between a mother and her child is strong, glorious, and automatic—all women love being mothers. Thus, culturally (and theoretically), society nearly insists that married couples have children and that they will love having children. But, after children are born, there is not much support for couples who have trouble adjusting to parenthood, or who do not absolutely love their new roles as parents. People look askance at parents who need help, and cannot believe parents who say anything negative about parenthood. As such, theoretically, society has set up a situation where couples are strongly encouraged to have kids, are told they will love kids, but then society turns a blind or disdainful eye when these same parents need emotional, financial, or other forms of help or support. It is these types of cultural viewpoints that increase the risks for child abuse in society.

The consequences of child abuse are tremendous and long lasting. Research has shown that the traumatic experience of childhood abuse is life changing. These costs may surface during adolescence, or they may not become evident until abused children have grown up and become abusing parents or abused spouses. Early identification and treatment is important to minimize these potential long-term effects. Whenever children say they have been abused, it is imperative that they be taken seriously and their abuse be reported. Suspicions of child abuse must be reported as well. If there is a possibility that a child is or has been abused, an investigation must be conducted.

Children who have been abused may exhibit traits such as the inability to love or have faith in others. This often translates into adults who are unable to establish lasting and stable personal relationships. These individuals have trouble with physical closeness and touching as well as emotional intimacy and trust. Further, these qualities tend to cause a fear of entering into new relationships, as well as the sabotaging of any current ones.

Psychologically, children who have been abused tend to have poor self-images or are passive, withdrawn, or clingy. They may be angry individuals who are filled with rage, anxiety, and a variety of fears. They are often aggressive, disruptive, and depressed. Many abused children have flashbacks and nightmares about the abuse they have experienced, and this may cause sleep problems as well as drug and alcohol problems. Posttraumatic stress disorder (PTSD) and antisocial personality disorder are both typical among maltreated children. Research has also shown that most abused children fail to reach “successful psychosocial functioning,” and are thus not resilient and do not resume a “normal life” after the abuse has ended.

Socially (and likely because of these psychological injuries), abused children have trouble in school, will have difficulty getting and remaining employed, and may commit a variety of illegal or socially inappropriate behaviors. Many studies have shown that victims of child abuse are likely to participate in high-risk behaviors such as alcohol or drug abuse, the use of tobacco, and high-risk sexual behaviors (e.g., unprotected sex, large numbers of sexual partners). Later in life, abused children are more likely to have been arrested and homeless. They are also less able to defend themselves in conflict situations and guard themselves against repeated victimizations.

Medically, abused children likely will experience health problems due to the high frequency of physical injuries they receive. In addition, abused children experience a great deal of emotional turmoil and stress, which can also have a significant impact on their physical condition. These health problems are likely to continue occurring into adulthood. Some of these longer-lasting health problems include headaches; eating problems; problems with toileting; and chronic pain in the back, stomach, chest, and genital areas. Some researchers have noted that abused children may experience neurological impairment and problems with intellectual functioning, while others have found a correlation between abuse and heart, lung, and liver disease, as well as cancer (Thomas, 2004).

Victims of sexual abuse show an alarming number of disturbances as adults. Some dislike and avoid sex, or experience sexual problems or disorders, while other victims appear to enjoy sexual activities that are self-defeating or maladaptive—normally called “dysfunctional sexual behavior”—and have many sexual partners.

Abused children also experience a wide variety of developmental delays. Many do not reach physical, cognitive, or emotional developmental milestones at the typical time, and some never accomplish what they are supposed to during childhood socialization. In the next section, these developmental delays are discussed as a means of identifying children who may be abused.

There are two primary ways of identifying children who are abused: spotting and evaluating physical injuries, and detecting and appraising developmental delays. Distinguishing physical injuries due to abuse can be difficult, particularly among younger children who are likely to get hurt or receive injuries while they are playing and learning to become ambulatory. Nonetheless, there are several types of wounds that children are unlikely to give themselves during their normal course of play and exploration. These less likely injuries may signal instances of child abuse.

While it is true that children are likely to get bruises, particularly when they are learning to walk or crawl, bruises on infants are not normal. Also, the back of the legs, upper arms, or on the chest, neck, head, or genitals are also locations where bruises are unlikely to occur during normal childhood activity. Further, bruises with clean patterns, like hand prints, buckle prints, or hangers (to name a few), are good examples of the types of bruises children do not give themselves.

Another area of physical injury where the source of the injury can be difficult to detect is fractures. Again, children fall out of trees, or crash their bikes, and can break limbs. These can be normal parts of growing up. However, fractures in infants less than 12 months old are particularly suspect, as infants are unlikely to be able to accomplish the types of movement necessary to actually break a leg or an arm. Further, multiple fractures, particularly more than one on a bone, should be examined more closely. Spiral or torsion fractures (when the bone is broken by twisting) are suspect because when children break their bones due to play injuries, the fractures are usually some other type (e.g., linear, oblique, compacted). In addition, when parents don’t know about the fracture(s) or how it occurred, abuse should be considered, because when children get these types of injuries, they need comfort and attention.

Head and internal injuries are also those that may signal abuse. Serious blows to the head cause internal head injuries, and this is very different from the injuries that result from bumping into things. Abused children are also likely to experience internal injuries like those to the abdomen, liver, kidney, and bladder. They may suffer a ruptured spleen, or intestinal perforation. These types of damages rarely happen by accident.

Burns are another type of physical injury that can happen by accident or by abuse. Nevertheless, there are ways to tell these types of burn injuries apart. The types of burns that should be examined and investigated are those where the burns are in particular locations. Burns to the bottom of the feet, genitals, abdomen, or other inaccessible spots should be closely considered. Burns of the whole hand or those to the buttocks are also unlikely to happen as a result of an accident.

Turning to the detection and appraisal of developmental delays, one can more readily assess possible abuse by considering what children of various ages should be able to accomplish, than by noting when children are delayed and how many milestones on which they are behind schedule. Importantly, a few delays in reaching milestones can be expected, since children develop individually and not always according to the norm. Nonetheless, when children are abused, their development is likely to be delayed in numerous areas and across many milestones.

As children develop and grow, they should be able to crawl, walk, run, talk, control going to the bathroom, write, set priorities, plan ahead, trust others, make friends, develop a good self-image, differentiate between feeling and behavior, and get their needs met in appropriate ways. As such, when children do not accomplish these feats, their circumstances should be examined.

Infants who are abused or neglected typically develop what is termed failure to thrive syndrome. This syndrome is characterized by slow, inadequate growth, or not “filling out” physically. They have a pale, colorless complexion and dull eyes. They are not likely to spend much time looking around, and nothing catches their eyes. They may show other signs of lack of nutrition such as cuts, bruises that do not heal in a timely way, and discolored fingernails. They are also not trusting and may not cry much, as they are not expecting to have their needs met. Older infants may not have developed any language skills, or these developments are quite slow. This includes both verbal and nonverbal means of communication.

Toddlers who are abused often become hypervigilant about their environments and others’ moods. They are more outwardly focused than a typical toddler (who is quite self-centered) and may be unable to separate themselves as individuals, or consider themselves as distinct beings. In this way, abused toddlers cannot focus on tasks at hand because they are too concerned about others’ reactions. They don’t play with toys, have no interest in exploration, and seem unable to enjoy life. They are likely to accept losses with little reaction, and may have age-inappropriate knowledge of sex and sexual relations. Finally, toddlers, whether they are abused or not, begin to mirror their parents’ behaviors. Thus, toddlers who are abused may mimic the abuse when they are playing with dolls or “playing house.”

Developmental delays can also be detected among abused young adolescents. Some signs include the failure to learn cause and effect, since their parents are so inconsistent. They have no energy for learning and have not developed beyond one- or two-word commands. They probably cannot follow complicated directions (such as two to three tasks per instruction), and they are unlikely to be able to think for themselves. Typically, they have learned that failure is totally unacceptable, but they are more concerned with the teacher’s mood than with learning and listening to instruction. Finally, they are apt to have been inadequately toilet trained and thus may be unable to control their bladders.

Older adolescents, because they are likely to have been abused for a longer period of time, continue to get further and further behind in their developmental achievements. Abused children this age become family nurturers. They take care of their parents and cater to their parents’ needs, rather than the other way around. In addition, they probably take care of any younger siblings and do the household chores. Because of these default responsibilities, they usually do not participate in school activities; they frequently miss days at school; and they have few, if any, friends. Because they have become so hypervigilant and have increasingly delayed development, they lose interest in and become disillusioned with education. They develop low self-esteem and little confidence, but seem old for their years. Children this age who are abused are still likely to be unable to control their bladders and may have frequent toileting accidents.

Other developmental delays can occur and be observed in abused and neglected children of any age. For example, malnutrition and withdrawal can be noticed in infants through teenagers. Maltreated children frequently have persistent or untreated illnesses, and these can become permanent disabilities if medical conditions go untreated for a long enough time. Another example can be the consequences of neurological damage. Beyond being a medical issue, this type of damage can cause problems with social behavior and impulse control, which, again, can be discerned in various ages of children.

Once child abuse is suspected, law enforcement officers, child protection workers, or various other practitioners may need to interview the child about the abuse or neglect he or she may have suffered. Interviewing children can be extremely difficult because children at various stages of development can remember only certain parts or aspects of the events in their lives. Also, interviewers must be careful that they do not put ideas or answers into the heads of the children they are interviewing. There are several general recommendations when interviewing children about the abuse they may have experienced. First, interviewers must acknowledge that even when children are abused, they likely still love their parents. They do not want to be taken away from their parents, nor do they want to see their parents get into trouble. Interviewers must not blame the parents or be judgmental about them or the child’s family. Beyond that, interviews should take place in a safe, neutral location. Interviewers can use dolls and role-play to help children express the types of abuse of which they may be victims.

Finally, interviewers must ask age-appropriate questions. For example, 3-year-olds can probably only answer questions about what happened and who was involved. Four- to five-year-olds can also discuss where the incidents occurred. Along with what, who, and where, 6- to 8-year-olds can talk about the element of time, or when the abuse occurred. Nine- to 10-year-olds are able to add commentary about the number of times the abuse occurred. Finally, 11-year-olds and older children can additionally inform interviewers about the circumstances of abusive instances.

A conclusion is not a summary of what a writer has already mentioned. On the contrary, it is the last point made. Taking every detail of the investigation, the researcher makes the concluding point. In this part of a paper, you need to put a full stop in your research. You need to persuade the reader in your opinion.

Never add any new information in the conclusion. You can present solutions to the problem and you dwell upon the results, but only if this information has been already mentioned in the main body.

Child advocates recommend a variety of strategies to aid families and children experiencing abuse. These recommendations tend to focus on societal efforts as well as more individual efforts. One common strategy advocated is the use of public service announcements that encourage individuals to report any suspected child abuse. Currently, many mandatory reporters (those required by law to report abuse such as teachers, doctors, and social service agency employees) and members of communities feel that child abuse should not be reported unless there is substantial evidence that abuse is indeed occurring. Child advocates stress that this notion should be changed, and that people should report child abuse even if it is only suspected. Public service announcements should stress that if people report suspected child abuse, the worst that can happen is that they might be wrong, but in the grander scheme of things that is really not so bad.

Child advocates also stress that greater interagency cooperation is needed. This cooperation should be evident between women’s shelters, child protection agencies, programs for at-risk children, medical agencies, and law enforcement officers. These agencies typically do not share information, and if they did, more instances of child abuse would come to the attention of various authorities and could be investigated and managed. Along these lines, child protection agencies and programs should receive more funding. When budgets are cut, social services are often the first things to go or to get less financial support. Child advocates insist that with more resources, child protection agencies could hire more workers, handle more cases, conduct more investigations, and follow up with more children and families.

Continuing, more educational efforts must be initiated about issues such as punishment and discipline styles and strategies; having greater respect for children; as well as informing the community about what child abuse is, and how to recognize it. In addition, Americans must alter the cultural orientation about child bearing and child rearing. Couples who wish to remain child-free must be allowed to do so without disdain. And, it must be acknowledged that raising children is very difficult, is not always gloriously wonderful, and that parents who seek help should be lauded and not criticized. These kinds of efforts can help more children to be raised in nonviolent, emotionally satisfying families, and thus become better adults.

Bibliography

When you write a paper, make sure you are aware of all the formatting requirements. Incorrect formatting can lower your mark, so do not underestimate the importance of this part.

Organizing your bibliography is quite a tedious and time-consuming task. Still, you need to do it flawlessly. For this reason, analyze all the standards you need to meet or ask professionals to help you with it. All the comas, colons, brackets etc. matter. They truly do.

Bibliography:

• American Academy of Pediatrics: https://www.aap.org/
• Bancroft, L., & Silverman, J. G. (2002). The batterer as parent. Thousand Oaks, CA: Sage.
• Child Abuse Prevention and Treatment Act, 42 U.S.C.A. § 5106g (1998).
• Childhelp: Child Abuse Statistics: https://www.childhelp.org/child-abuse-statistics/
• Children’s Defense Fund: https://www.childrensdefense.org/
• Child Stats.gov: https://www.childstats.gov/
• Child Welfare League of America: https://www.cwla.org/
• Crosson-Tower, C. (2008). Understanding child abuse and neglect (7th ed.). Boston: Allyn & Bacon.
• DeBecker, G. (1999). Protecting the gift: Keeping children and teenagers safe (and parents sane). New York: Bantam Dell.
• Family Research Laboratory at the University of New Hampshire: https://cola.unh.edu/family-research-laboratory
• Guterman, N. B. (2001). Stopping child maltreatment before it starts: Emerging horizons in early home visitation services. Thousand Oaks, CA: Sage.
• Herman, J. L. (2000). Father-daughter incest. Cambridge, MA: Harvard University Press.
• Medline Plus, Child Abuse: https://medlineplus.gov/childabuse.html
• Myers, J. E. B. (Ed.). (1994). The backlash: Child protection under fire. Newbury Park, CA: Sage.
• National Center for Missing and Exploited Children: https://www.missingkids.org/home
• National Child Abuse and Neglect Data System. (2006). Child maltreatment 2006: Reports from the states to the National Child Abuse and Neglect Data System. Washington, DC: U.S. Department of Health and Human Services, Administration for Children and Families.
• New York University Silver School of Social Work: https://socialwork.nyu.edu/
• Pitzer, R. L. (1997). Corporal punishment in the discipline of children in the home: Research update for practitioners. Paper presented at the National Council on Family Relations Annual Conference, Washington, DC.
• RAND, Child Abuse and Neglect: https://www.rand.org/topics/child-abuse-and-neglect.html
• Richards, C. E. (2001). The loss of innocents: Child killers and their victims. Wilmington, DE: Scholarly Resources.
• Straus, M. A. (2001). Beating the devil out of them: Corporal punishment in American families and its effects on children. Edison, NJ: Transaction.
• Thomas, P. M. (2004). Protection, dissociation, and internal roles: Modeling and treating the effects of child abuse. Review of General Psychology, 7(15).
• U.S. Department of Health and Human Services, Administration for Children and Families: https://www.acf.hhs.gov/

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CS&E Colloquium: Co-Designing Algorithms and Hardware for Efficient Machine Learning (ML): Advancing the Democratization of ML

The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m. This week's speaker,  Caiwen Ding ( University of Connecticut ), will be giving a talk titled, "Co-Designing Algorithms and Hardware for Efficient Machine Learning (ML): Advancing the Democratization of ML". 

The rapid deployment of ML has witnessed various challenges such as prolonged computation and high memory footprint on systems. In this talk, we will present several ML acceleration frameworks through algorithm-hardware co-design on various computing platforms. The first part presents a fine-grained crossbar-based ML accelerator. Instead of attempting to map the trained positive/negative weights afterwards, our key principle is to proactively ensure that all weights in the same column of a crossbar have the same sign, to reduce area. We divide the crossbar into sub-arrays, providing a unique opportunity for input zero-bit skipping. Next, we focus on co-designing Transformer architecture, and introduce on-the-fly attention and attention-aware pruning to significantly reduce runtime latency. Then, we will focus on co-design graph neural network training. To explore training sparsity and assist explainable ML, we propose a hardware friendly MaxK nonlinearity, and tailor a GPU kernel. Our methods outperform the state-of-the-arts on different tasks. Finally, we will discuss today's challenges related to secure edge AI and large language models (LLMs)-aided agile hardware design, and outline our research plans aimed at addressing these issues.

Caiwen Ding is an assistant professor in the School of Computing at the University of Connecticut (UConn). He received his Ph.D. degree from Northeastern University, Boston, in 2019, supervised by Prof. Yanzhi Wang. His research interests mainly include efficient embedded and high-performance systems for machine learning, machine learning for hardware design, and efficient privacy-preserving machine learning. His work has been published in high-impact venues (e.g., DAC, ICCAD, ASPLOS, ISCA, MICRO, HPCA, SC, FPGA, Oakland, NeurIPS, ICCV, IJCAI, AAAI, ACL, EMNLP). He is a recipient of the 2024 NSF CAREER Award, Amazon Research Award, and CISCO Research Award. He received the best paper nomination at 2018 DATE and 2021 DATE, the best paper award at the DL-Hardware Co-Design for AI Acceleration (DCAA) workshop at 2023 AAAI, outstanding student paper award at 2023 HPEC, publicity paper at 2022 DAC, and the 2021 Excellence in Teaching Award from UConn Provost. His team won first place in accuracy and fourth place overall at the 2022 TinyML Design Contest at ICCAD. He was ranked among Stanford’s World’s Top 2% Scientists in 2023. His research has been mainly funded by NSF, DOE, DOT, USDA, SRC, and multiple industrial sponsors.

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Wellbeing research

An increasing body of evidence demonstrates that wellbeing profoundly influences our ability to learn, thrive and lead fulfilling lives. In an ever-evolving education landscape, prioritizing and fostering wellbeing becomes a vital investment in the future of education.

This page highlights research into the existing scientific landscape, emphasizing the foundational evidence that positions wellbeing at the core of the educational ecosystem. Moving beyond a narrow focus on academic achievement, a systemic approach to wellbeing necessitates creating a holistic, interconnected and supportive learning environment.

Foundational research

The Wellbeing Research Centre at the University of Oxford conducted a series of in-depth literature reviews to understand the science behind student, teacher and school community wellbeing. Based on these studies the IB can develop supports for teachers and schools to define wellbeing in their own context and to learn how best to improve it.

Wellbeing in education in childhood and adolescence

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Wellbeing for schoolteachers

Dr Laura Taylor, Dr Wanying Zhou, Leoni Boyle, Sabina Funk and Prof Jan-Emmanuel De Neve— Wellbeing Research Centre, University of Oxford This foundational literature review provides an overview of the latest research into teacher wellbeing and its importance for teachers themselves, students and the school community. The report is intended to give the International Baccalaureate, policymakers and educational leaders an understanding of the definitions of adult wellbeing, what influences teacher wellbeing and what interventions might be used to improve teacher wellbeing. The researchers also present a teacher wellbeing framework which identifies drivers of teacher wellbeing. Coming soon

School wellbeing ecosystem

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UC San Diego Roboticists Shine at Human Robot Interaction 2024 Conference

• Katie E. Ismael - [email protected]
• Kimberley Clementi - [email protected]

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University of California, San Diego robotics research– from supporting patient home care and stroke rehabilitation to facilitating mission critical teamwork among first responders– was on display during the ACM/IEEE International Conference on Human-Robot Interaction (HRI). 

Researchers from UC San Diego’s Healthcare Robotics Lab are at the epicenter of human robot interaction and its applications in the medical field. Led by the lab’s director Laurel Riek, a professor in the Department of Computer Science and Engineering in the Jacobs School of Engineering with a joint appointment in the Department of Emergency Medicine, the group’s groundbreaking research shined at the 19 th Annual HRI conference held in Boulder, Colorado.

An impressive nine papers from the Healthcare Robotics Lab were presented  at the conference, covering the technologies, clinical practicalities, and ethical considerations of implementing robotic systems into complex, socio-technical medical settings.

Riek, who has worked at the intersection of Artificial Intelligence and Robotics for decades, spoke at four HRI conference workshops and was keynote speaker for three of them: Scarecrows in Oz: Large Language Models in HRI; Disability Ethics, Accessibility, & Assistive Applications in HRI; and HRI for Aging in Place.

UC San Diego papers at Human-Robot Interaction

" CARMEN: A Cognitively Assistive Robot for Personalized Neurorehabilitation at Home "  Bouzida, A., Kubota, A., Cruz-Sandoval, D., Twamley, E., and Riek, L.D. (nominated for best paper)

For individuals living with dementia or mild cognitive impairment, simple day-to-day tasks-- such as those related to memory, attention, organization, problem-solving and planning-- can be daunting. The UC San DIego team built CARMEN, or Cognitively Assistive Robot for Motivation and Neurorehabilitation, to one day improve access to care and increase patient independence with the help of custom AI algorithms.

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One of CARMEN’s distinguishing features is its flexibility, rapidly integrating stakeholder input to align with clinical practice.

This work was led by CSE PhD student Anya Bouzida, alumna  Alyssa Kubota (PhD ’23), and postdoc Dago Cruz-Sandoval, in collaboration with Elizabeth Twamley in the UC San Diego Department of Psychiatry. 

Riek was senior author for eight additional papers presented at the 2024 HRI conference.

Work led by alumnus Benjamin Bestmann (BS '23) presented GARRY, a neurorehabilitation robot that could impact the lives of 12.2 million new stroke patients each year. CSE PhD student Pratysha Ghosh led research on how telemedical robots could be used to support people experiencing Long Covid.

PhD students Rabeya Jamshad and Sachiko Matsumoto, master’s student Arthi Haripriyan, and postdoc Preeti Ramaraj led work on building robots that integrate into action teams, such as first responders and emergency medical personnel.  Finally, PhD students Sandhya Jayaraman and Pratyusha Ghosh led research exploring the privacy and ethical concerns of deploying robots in healthcare, which can have global implications.

• Bestmann, B., Chow, A., Kubota, A., and Riek, L.D. (2024). " GARRY: The Gait Rehabilitation Robotic System .”
• Ghosh, P., Haripriyan, A., Chow, A., Redfield, S., and Riek, L.D. (2024). " Envisioning Mobile Telemanipulator Robots for Long Covid .” 
• Matsumoto, S. and Riek, L.D. " Telepresence Robots for Dynamic, Safety-Critical Environments ". 
• Jamshad, R., Haripriyan, A., Sonti, A., Simkins, S., and Riek, L.D. (2024). “ Human-Robot Action Teams: How Robots Can Be Proactive Teammates .”
• Ramaraj, P., Hairpriyan, A., Jamshad, R., and Riek, L.D. (2024). " Analysis of Social Signals in Human-Robot Action Teams .” 
• Jayaraman, S., Philips, E., Church, D., and Riek, L.D. (2024). " Social Robots in Healthcare: Characterizing Privacy Considerations .”
• Ghosh, P., Leido, B., and Riek, L.D. (2024). " The Problem of Ableist Paternalism in Assistive Robotics .” 
• Jayaraman, S., Cruz-Sandoval, D., Kubota, A., and Riek, L.D. (2024).  “ What a professional care provider wants, what a disabled person needs: Exploring stakeholder design tensions in assistive robotics .” 

Other UC San Diego faculty with papers at HRI 2024 include Amy Eguchi, School of Education, Hortense Gerardo, the Jacobs School of Engineering, and Robert Twomey, Clarke Imagination Center.

• Eguchi, A., Gerardo, H., Twomey, R. (2024). "Beyond the Black Box: Human Robot Interaction through Human Robot Performances.”

CSE robotics alumni had a strong presence as well, including a second  best paper nomination for a paper by Angelique Taylor (PhD ’21), an assistant professor at Cornell University, and postdoctoral alumna Hee Rin Lee, now an assistant professor at Michigan State University. 

• Taylor, A., Tanjim, T., Cao, H., Lee, H.R. (2024). "Towards Collaborative Crash Cart Robots that Support Clinical Teamwork." (nominated for best paper) 

CSE alumnus Tariq Iqbal (PhD ’17), currently an assistant professor at the University of Virginia, also had a paper accepted.

• Yasar, M., Islam, M., Iqbal, T. (2024). "PoseTron: Enabling Close-Proximity Human-Robot Collaboration Through Multi-human Motion Prediction.”

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Acs fall 2024.

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Aug 18–22, 2024

Join us in Denver, Colorado from August 18-22 to get the latest research in chemistry, network, and attend career events. The theme of this meeting is "Elevating Chemistry" and will explore a variety of topics such as:

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Cao co-author of newly published research in North-East Greenland

Dr. Wentao Cao

Department of Geology and Environmental Sciences Assistant Professor Wentao Cao had his research paper, “Exhumation of an Ultrahigh-Pressure Slice from the Upper Plate of the Caledonian Orogen – A Record from Titanite in North-East Greenland,” published in the journal Tectonics.

In this research paper, Dr. Cao and three co-authors examine the tectonics of Caledonian (geological term) collisional mountain belt in North-East Greenland (a geographical division of Greenland) using radiometric U-Pb dating of titanite (a calcium titanium silicate mineral, CaTiSiO₅) from rocks that reached a depth ~120 km (~75 miles).

Petrographic examination shows that titanite overgrows on the titanium mineral rutile (TiO2), interpreted as formed after the rutile (TiO2). Titanite was analyzed using ion microprobe at Stanford University after secondary electron microscopy (SEM) examination. Rare earth element patterns of the titanite show variable core to rim zoning that are representative of complex stages in different specimens, but fit the overall mineral succession sequences by modeling.

Radiometric dating by U-Pb system shows weighted mean ages from 347 million to 320 million years ago, which is consistent with previous study using U-Pb zircon geochronology. The data supports the exhumation of the crustal materials by buoyancy forces in translational tectonics.

Interested readers can check out the paper here

The journal Tectonics (impact factor = 4.2) is the scholarly journal of the American Geophysical Union, with editorial collaboration from the European Geosciences Union. According to the journal’s webpage, it “presents original scientific contributions that describe and explain the evolution, structure, and deformation of Earth’s lithosphere.”

• Geology and Environmental Sciences
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