ethical issues in online learning research paper

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The social and ethical issues of online learning during the pandemic and beyond

Sonali bhattacharya.

1 Symbiosis Centre for Management and Human Resource Development, Symbiosis International University, Pune, 411057 India

Venkatesha Murthy

2 Innovation & Entrepreneurship, IIT Jodhpur, Jodhpur, 342027 India

Shubhasheesh Bhattacharya

3 Symbiosis Institute of International Business, Symbiosis International University, Pune, 411057 India

This article describes how the COVID-19 pandemic has forced the higher education institutes in developing nations like India to relook at pedagogical approaches. Due to government imposing nationwide lockdown, higher educational institutes were quickly adopting to imbibe online learning medium. This research takes a qualitative thematic analytical approach to explore the facilitators and challenges to online learning from the perspectives of both learners and educators in higher education institutes. We have specifically explored the ethical and social concerns related to online learning and the possible solution for the same.

Introduction

The new mode of virtual learning at various levels of education that has opened up globally during the pandemic may likely to stay in new forms forever. It has opened up questions on ethical and social concerns of virtual learning from the perspectives of both learners and educators. Online or virtual learning platforms include online chat, asynchronous podcasts, webinars, and synchronous mode of learning. The development of theories on virtual learning is still at its nascent state. Hence, several qualitative studies are being carried out on bringing out ethical and social concerns on virtual learning platform. For example, Agostinho ( 2005 ) applied naturalistic inquiries; Fitzgerald, et al. ( 2013 ) and Steinmetz ( 2012 ) have applied ethnography to online training processes; while Postma et al. ( 2013 ) applied grounded theory to understand research. Certain ethical dilemmas raised forth by the study of Youn ( 2009 ) are issues of informed consent, privacy protection, and identity. Steele et al. ( 2020 ) came out with some of the themes and subthemes of ethical concern on virtual reality–based classrooms as psychological safety, social safety, and ethical morality and teachers’ responsibility. Ethical and social concerns identified by Reamer ( 2013 ) are student access, quality of course and degree program content and delivery, academic honesty and gatekeeping, and privacy and surveillance. From the perspectives of educators, the question being raised is on whether educators can play the role of friend, philosopher, and guide effectively through the online learning platform. Debate is also raised on how online learning can be made more effective. Suliman et al., ( 2022 ) for example found that synchronous and asynchronous learning can be equally effective while learning ethical and moral values of nursing practices. Concerns are also on whether online assessment of learning should use proctored technology. Though the use of proctored technology during assessment reduces academic cheating, but it fails to take into consideration inequality among students in terms of study conditions, family circumstances, and physical and psychological health (Lee et al., 2022 ). Concerns of academic integrity, non-maleficence, trust, privacy, liberty, and autonomy are raised in case of the use of proctoring technology.

This study intends to answer following research questions:

• What are the ethical and social concerns of online learning raised from the perspectives of faculty and students?
• What are the strategies adopted by higher education institutes to make online learning process learner centric?

Worldwide, 61.6% of learners were affected by the lockdowns during the pandemic. In India which has the largest cohort of population undergoing primary, secondary, and tertiary education, more than 30 million learners impacted by the lockdown were affected by the social distancing norm declared by government of India. Soon, face-to-face learning were to be replaced by online learning. Issues faced by countries like India and other third-world nations were weak internet connectivity, adaptability of ICT (Information and communications technology) usage, and content development in the new medium (Aung & Khaing, 2015 ). In this research paper, we have attempted to look at ethical and societal concerns that were raised while higher educational institutes are trying to adopt the new medium of learning from both the perspectives of educators and learners of engineering and management institutes. A qualitative research methodology has been used. In the following section, we have discussed some of the theories to online learning. This was followed by the detailed description of the methodology, findings, critical discussion of the findings, and recommendations.

Theoretical underpinning

Authentic e-learning.

Pedagogical approaches often determine the learner’s learning strategies and experiences (Prosser & Trigwell, 1999 ). With this objective in mind, the concept of authentic e-learning was developed in higher education context. The concept has been used in the context of pre-service teacher education (Valtonen et al., 2015 ), higher education (e.g., Bozalek et al., 2013 ), teacher professional development (e.g., Parker et al., 2013 ; Teräs et al., 2012 ), vocational education (Pu et al., 2016 ), and foreign language learning (Ozverir et al., 2016 ). However, applications of authentic e-learning in industry and organizations are still scarce. Authentic learning attempts to link education to situational learning, apprentice, or internship (e.g., Collis et al., 2009 ; Pu et al., 2016 ), especially in the context of professional courses such as management or engineering. This study tries to explore how authentic e-learning was adopted in the pedagogical approach during the pandemic in Indian professional schools.

The authentic e-learning framework calls for adopting a pedagogical approach that imbibes practical approach based on contexts, situations, and culture and business (e.g., Machles, 2003 ). It can be considered an approach which brings theories into practice, rather than abstract learning (Herrington et al., 2010 ). It moves away from traditional university mode of course delivery and subsequent assessment to project based learning. Such an approach is well suited for professional development — perhaps even more easily than in a higher education context where the constraints of traditional academic practices are often hindering the development of authentic e-learning courses (Herrington et al., 2010 ). The role of technology in e-learning depends on whether technology is being used for aiding learning or learning process is totally integrated with technology. Technology for learning is the traditional use of technology for storing, delivering, and assessing learning contents, and evaluation of learning processes. Learning with technology which is the core of authentic learning is the use of technology by the learners to explore knowledge, learn collaboratively, and create new knowledge. The key elements of authentic e-learning are as follows: (i) authentic context in which indicate how learnings are connected to real-life applications putting learners through complex real-life setting, (ii) authentic task which engages learners through situation-relevant content and problem-solving pedagogy, (iii) access to expert performances to see how experts solve a complex problem, (iv) indulging learners in multiple roles and responsibilities like debates, case presentation, and event management, (v) collaborative construction of knowledge in form of interdisciplinary teamwork, collaboration, and interaction between participants, (vi) reflection, in which learners can get access to learning material, reflect, and discuss with co-learners, experts, and mentors, (vii) opportunities for articulation and presentation of thoughts, ideas, learning in a public platform, (viii) coaching and scaffolding support to learners to complete complex task, and finally, (ix) authentic assessment which embeds assessment to day-to-day learning process.

Online academic assessment and integrity

Online assessment often makes the academic learning process more susceptible to academic dishonesty or cheating. Most common form of academic dishonesty that is observed is taking credit of other’s work (Golden & Kohlbeck, 2020 ). The reason of academic dishonesty can be attributed to disinterest and unpreparedness of the learners about the learning module, collaborative informal agreement between the learners to deceive the system by practicing rampant cheating without fear of getting caught or punished (Yang et al., 2013 ). Becker and Mehlkop ( 2006 ) categorized motives behind academic dishonesty to three major classification incentive, opportunity, and rationalization. The researchers found all three elements drive cheating behaviors. Incentivizing can be due to impact of internal and external environment such as large demanding curriculum and high workload (Finchilescu & Cooper, 2018 ; Jian et al., 2020 ). Authentic learning that emphasizes on acquiring mastery in subject than reducing grading (Day et al., 2011 ; Pulfrey et al., 2019 ).

The second element of the fraud triangle, opportunity , refers to the ability to engage in dishonest behavior because of inadequate mechanisms to prevent it. In case of non-existence of rules, regulations and punitive actions against cheating can lead to academic dishonesty (Finchilescu & Cooper, 2018 ; Peled et al., 2019 ). Instilling ethical values on students with properly laid out rules and regulations on ethics (Arnold et al., 2007 ; Burrus et al., 2007 ; Tatum & Schwartz, 2017 ) may reduce the opportunity. Rationalization , refers to the belief that learner considers dishonesty not violating his/her ethical code of conduct. Researchers have theorized how the Big Five personality traits (neuroticism, extraversion, openness, agreeableness, and conscientiousness) may moderate academic dishonesty and a learner’s ability to rationalize such behavior (Nathanson et al., 2006 ; Williams and Williams., 2012 ). The theory of planned behavior has also been used to explain and predict academic dishonesty (Chudzicka-Czupała et al., 2016 ; Lonsdale, 2017 ). Academic dishonesty or cheating has been found to be more prominent in online learning platform than offline (Young, 2013 ). Out of the three dimensions of fraud, the opportunity could be the reason why cheating is more prevalent in online assessment. Unethical practices are frequent in case of unproctored examination than proctored examination. In case of proctored examination, there is not much difference found in practices of unethical means between online and offline examinations and no significant differences are also found between the scores of students. If exams are unproctored, then online environment is more susceptible to unethical practices and students tend to score higher than offline exams (Alessio et al., 2017 ; Daffin & Jones, 2018 ; Fask et al., 2014 ). Students also were observed to score higher if questions are pulled from a question bank than if questions are paraphrased (Golden & Kohlbeck, 2020 ). The reason is that test takers can look up the items from the online question bank. However, differences in performances on tests based on question bank and that based on paraphrased items were observed to be substantially reduced if exams are proctored.

Foucault’s theory of disciplinary governmentality

Foucault theory (Binkley & Capetillo, 2009 ) believe in exerting power on subjects not in oppressive or possessive manner but in constructive or progressive manners, so that resistance or expressions of dissent can be minimized. Subjects or learners in an educational set-up can be categorized based on pedagogical schemes into as high achievers or low achievers, well prepared or ill prepared, active or passive students, and well-behaved or misbehaving students. Discipline can be accorded on different categories of learners by stimulating self-regulation rather than coercive action. Rules and regulations can be internalized through individual self-discipline and group control. Surveillance and decentralization of power among subjects seem to be more powerful strategy than punishment. Educational institute can exert power upon their members by allowing (not allowing) specific bodily movements in a chosen space at a chosen time. The subjects are expected to willingly govern themselves — controlling and correcting their thoughts and behaviors even without direct contact or corporal punishment.

Methodology

The aim of this research was to understand that the online learning experiences are perceived by faculty and students of higher education in terms of behavior and expectations. The focus was to interpret the lived experiences of learners and teachers in the context of online learning environment during the pandemic. Qualitative analysis with thematic framework approach was used to collect data in the context of online learning, to understand what is interpreted as effective learning and what were the socio-psychological and ethical challenges to the online learning process. In a thematic framework analysis, the data reduction, data visualization, and interpretation evolve four steps:

• Transcribe and organize the transcription. The video-recorded interviews through google meets were transcribed.
• The interview transcripts are reviewed, and evolving themes are identified. Broad themes are pre-conceptualized from the literature review (Miles & Huberman, 1994 ). While framing the interview schedule, a process termed “a priori categorization” by Sinkovics et al., ( 2008 , p. 704) was applied. Hence, the priori categorization was ethical and social issues. Under the social issues, we further considered technology acceptance and adoption and authentic e-learning.
• Reviewing the themes to give it a structure.
• Finally drawing out a theoretical model, in an iterative process by rechecking on the data repeatedly. We, thus, followed Sinkovics et al. ( 2008 )’s coding process which included “a posteriori” categorization, with open coding allowing for the emergence of new themes.

In March 2020, faculty and students were forced to shift the learning platform through online platform such as MS-Team, Google Classroom, or Zoom due to the worldwide lockdown during COVID. Educational institutes quickly adjust to procure technology and get the teachers, learners, and supporting staff trained on the learning platform. The issues raised were what pedagogical changes are to be brought in to make learning as effective as physical classroom, what kind of learning support system and resources to be provided to learners as valued customer of knowledge, and how to ensure the presence and effective participation of learners during the online synchronous session. Furthermore, as examination was carried out in proctored platform in the online platform, it was difficult for the exam supervisors to track what students were writing, whether they were referring to onscreen materials or exchanging notes through messaging systems through smart phones. Mass scale copying and wide similarities of answers were observed among examinees, leading to dissatisfaction of faculty and honest students who did not resort to unfair means.

As in a qualitative research method, participants were identified by theoretical considerations rather than statistical representativeness (Eisenhardt, 1989 ; Miles & Huberman, 1994 ). A purposive sampling approach was preferred over random sampling so that logic and coherence that are characteristic of social setting is not lost (Miles & Huberman, 1994 ). The main criteria for interview selection of the perspective the educationists and learners represent the diverse geographies of India and belong to various types of higher educational institutes such as management institutes, engineering colleges, law college, science colleges, art colleges, and also EdTech institutes. We ensured that both genders are equally represented and we have educators of varied cadres and varied prior technical competencies. The educationist and the learner together represented “a sampling unit” for analytical purposes (Eisenhardt, 1989 ). Though we had initially contacted 40 pairs, but as 12 educationists expressed their unavailability to give us enough time for interview, we had to restrict our study only to 28 pairs. Following is a description of educators who have been interviewed (Table ​ (Table1 1 ).

Description of background of the educators who were interviewed

Interviews were conducted through google meetings, recorded, and transcribed

Some of the indicative questions that were proposed to be asked were as follows:

Questions to the educator:

• Share with us a detailed story of your teaching via online mode?
• What were your initial challenges? How did you attempt to overcome them?
• How did you ensure learning in the online classroom?
• How do you describe your students in the online classroom versus physical classroom?
• What were the key ethical dilemmas in your online teaching?
• At any stage of your teaching, did you feel that your students indulged in some kind of malpractice?
• What were your efforts to get that ethical dilemma right?

Questions to the students/online learners:

• What were your takeaways from online learning?
• How happy or sad have been since the time classes moved offline?
• What are the key differences between online and physical classroom teaching?
• How did you find your learning as a whole in the online mode?
• How do you describe your teacher’s ability to impart knowledge in the online mode?
• At any point did you feel a sense of manipulation or malpractice by your teacher?
• Did you voice it out to your teacher?
• Do you think your teacher was able to deliver justice in the classroom?
• Was your teacher impartial at any point in time?
• Did you notice your fellow friends indulging in any malpractices?

Data analysis

The themes that emerged from the transcription of the interviews were codified using open, axial, and selective coding. Open coding helped in understanding new emerging concepts. Axial coding helped in integrating concepts obtained at the open coding stage through sub nodes into broader themes. These themes were further integrated through various nodes and refined in order to build the final theoretical model. The emergent themes and subthemes are described in Table ​ Table2 2 .

Themes and subthemes of the study

Digital inequality in technology acceptance and adoption

In the initial days when the educators were trying to adopt to online learning platforms, the learners and young faculty members who are more digitally competent could adopt to the new technology quickly, and there was reverse mentoring by the students and young faculty members to the more qualified and experienced senior faculty members. Frequent internet connection failure was accepted empathetically by the learners all across as they too were trying to adjust to the new technology. There were plenty of hiccups from the sides of both learners and the educators’ side, but in most cases, they quickly adjusted to it. Perhaps, the success of adoption of e-leaning technology can best be explained through unified theory of acceptance and use of technology (UTAUT) model. The effective use of the technology depends on the performance expectancy, effort expectancy, social influence, and facilitating conditions (Qiao et al., 2021 ). Performance expectancy can depend on the individual perception of the use of technology. For example, in one of top science college where senior scientists and academicians are engaged to teach undergraduate students. One of the young assistant professors had to say,

If you talk about my college, we got 130 + teaching faculties where 60-65 are in the age group of above 50, so suddenly, for last 10 to 15 years, there were accustomed to the environment where they come morning and deliver the speech on the Dias with their vast experience teaching the same subject again. They are PhD and are top professionals who went to the US and UK to deliver simple lectures, but when coming up to digital learning, that was one of the biggest challenges nearly It nearly took 3 to 4 days for us to catch them into what they need to do even we told them how to mute how to Unmute how to share a PDF

Some of these senior faculty members, however, could find ways to somehow mix-up traditional mode of teaching with online teaching, by posting or displaying scanned cases or teaching materials, using multimedia links of cases. It again depended on how interactive they could make the sessions.

If you really want to teach online your approach is supposed to be completely different. You have to be more conversant with the IT dudes, right on multimedia and audio-visual mediums. Probably would have to use slacks, digital pens to start engaging students more like you are doing board work.. We can use animations in our presentations. Continuous innovation and self-examination in terms of whether you are effective in your teaching or not, is the key to upkeep your standards of teaching in these spotlights.

Most of the tier 1 higher educational institutes invested in acquiring the required technology, the staff trained in such technology, and provide requisite support while the teachers were working from home. But some of the teachers deal with the difficulty of weaker internet connection at homes sharing same work space with family members either working or studying virtually. These digital divide was also evident in case of students in remote parts of the country where internet connectivity was not good; social and familial environment or support was not conducive for learning digitally from home.

I asked a student to give me some answers to some questions and once he started giving answers, turning his camera on, I could see there are people and vegetable sellers and food sellers in carts everybody was walking around there, a lot of chaos I asked him what the hell is happening? Where are you right now? He was like sir I'm sitting in a chai ki tapir ( tea stall). I was like, is this an appropriate place. He said that this is where I can get a good internet connection

So, to deal with the problem of unreliable and inconsistent internet connection, most class sessions are recorded and stored in platforms such as MS Teams for students to refer to later on or uploaded in YouTube channels.

Another factor which made online teaching was a fear of facing the camera and being closely watched by hundreds of eyes including students, their families, and if the recorded videos go viral, and then large number of unknown people worldwide.

Like, you know, teaching, facing the camera first time I was almost shivering. I don't know why my hands were shaking, fingers were shaking, and I was not able to teach at all. And I didn't understand why this was happening. And then I realized there was that fear, now these videos are going to go online, maybe all over the world or something. And people will watch and everyone will watch. And maybe that fear, if I make some mistakes or something, it will be noticed. And now it is not only the students, even the parents will be watching the way we are teaching. So yes, it was very difficult for me.

Some faculty members with support from their family and friends considered opportunity of teaching in virtual platform to learn new skills, pedagogical approach, and opportunity to innovate which enhanced their self-esteem.

I made it a point to learn because I have big children, like 25 and 26. But they said, no, mommy, you have to update yourself and you should learn on your own. And beacuse they did not help me but then I was not discouraged. I was actually like, I felt that I should do things on my own. And now I'm very happy that ,yes, a person like me who was not into all this feeling so happy that I am able to conduct online classes can share screen, it's such a joy for me at this age that I have learned something that I think if not for Corona and lockdown down, I think I would have never learned. So I'm very grateful and thankful to God that I got this opportunity to learn. And I also am able to, you know, send the links, you know, like we have a teacher's meetings and I know how to send links and all that. So, I'm feeling very happy that I got something to learn.

Authentic E-learning: promotors and challenges

One of the lacunas of virtual learning is the lack of opportunities of collaborative learning with peers, development of social sensitivity, and team building social interaction with faculty members which are essential for holistic development specifically in context of business schools.

Classroom teaching is important to encourage and motivate collaborative learning also. Collaborative learning increases student’s self-awareness about how students learn and enables them to learn more easily and effectively, transforming them into keen learners inside and beyond the classroom. Classroom studying provides an opportunity for students to engage in live discussions like moot courts where they can better utilise their critical thinking skills to voice opinions or involve in an argument, whereas in online scenario, it just creates a fish market because everyone wants to speak.

Offline education helps faculty to capture non-verbal cues from the students’ expression about their understanding of a particular topic discussed in a class session which is not possible during online sessions as videos are kept switched off. Faculty members and institutes adopted several measures to give students authentic e-learning platform specially in business schools. Students completed many internship projects, corporate live projects, and case development workshop during the period. Moot court experiences were provided online by one of the interviewed faculty member teaching “Legal Aspects of Business,” who made one group of students to enact the role of prosecuting lawyer; one group of students as defense advocates and somebody will play the role of judge. There would be a researcher who will create the case story based on the concepts taught on the class. Participants of the act were required to wear attires of legal attire during the act.

Faculty members took this opportunity to bring in innovations in the teaching with the help of multi-media and technology.

I invested a lot in my own software, like I bought tablets, I bought an electronic SMART Board. I configured it in with my, with my laptop, you know, if you see me, I also use the smartboard. Well, right? It looks like a blackboard to the students. So it gives a classroom feeling. We have options of breakout rooms and we can make so many groups inside the team and,ask the students to, you know, walk together and then present something based on their learning in the class.

One of the faculty teaching finance used breakout rooms to facilitate huddle between participants just traders do before opening up of the market and facilitated simulated trading environment.

All these big financial institutions that, you know, have trading desks and so on, they actually have a huddle before the market opens in the morning to discuss some of the key ideas and so on. And so forth. Right. I loved too have the option to do in an online format.

Some faculty members attempted to use role plays by engaging two groups of students debating on the pros and cons of a topic on every session. In one of the edu-tech-based institute, teaching communication skills to practitioners used to teach 5 days a week and 6th day of the week was kept for peer learning, in which one of the learners had to assume the role of teacher and teach and review concepts with the rest of the batch mates.

Ethical issues in virtual learning platform

Privacy concerns versus discipline.

Most debated issue which came out was if students should be mandated to keep the cameras. There were varied opinions. In case of small class sizes, many faculty members felt cameras should be on, as this builds decom in the session. A faculty in the Edutech industry teaching corporate was of the opinion that since these corporate has to face similar kind of situations in future, in real life, they should attend sessions with corporate dress code. Most other educators expressed that with bigger class size, it is not possible to monitor the entire batch during the session even when the cameras and audios are on.

Most of the educators raised concerns of privacy. Some felt the home environment in which students are studying may be embarrassing from them if the cameras are on, like some of them may have younger siblings making noises, parents quarrelling, houses located noisy surrounding, and dilapidated houses. They were of the views that switching off cameras or mics should be made optional due to privacy concern.

When you are having your zoom gallery view and there are faces, you can expect everyone to stay still. They keep moving, and you kind of feel distracted, unless you are sharing a ppt or something. So I feel privacy needs to be given to the student and they on other hand must not misuse that privacy also.

The United Nation’s Right to Privacy in the Digital Age 2013 (Nyst & Falchetta, 2017 ) affirms that “rights held by people offline must also be protected online, and it called upon all States to respect and protect the right to privacy in digital communication.”

Given the voluntary option, students were willingly keeping their cameras on for the first few weeks of the sessions but majority of them kept camera and mics switched off after that. So faculty faced a lot of psychological stress while speaking to blank screens with pictures or names of students who had locked in. Many educators complained that students even did not respond even when they were asked questions, which was not only disappointing for the educators, but also kept them wondering if the students were actually physically present onscreen or merely logged in. With non-interactive sessions, faculty could not make out whether students were able to understand the concepts.

Even the opinion of students were divided on the trade off between “disciplinary concerns” and “privacy concerns” on keeping the cameras on. Data privacy concern may be on observed, volunteered, or inferred data from the perspective of both educators and the learners. For example, most of the synchronous sessions were recorded and shared with students. Online learning management system like Zoom or MS Teams provided sharing co-created learning materials to be shared between the educators and learners. This gave opportunities for the learners to go through them at their leisure or private times, but it raised concerns on these learning materials getting shared by the students with others without consent.

Some of the mischievous students created memes of themselves as profile pictures during the online learning to hide their identity. Some of them posted memes of faculty members while they were teaching and posted them on social media which were gross violation of privacy.

There will be 5% of those Mischief boys who will disrupt the class, and we, of course, look that memes renaming yourself with different cartoon names and coming up with distinct profile pictures and making a Meme out of it, and if that's happening when I am the Faculty I know how to take care of it, but when it happens with the senior Faculty maybe for the first one or two times he might feel a bit annoyed but later that will surely Ping Pong his confidence.

Sometimes, if the audio was on, there were personal talks on infighting in the family of the students, some mischievous students post it on the social media much to the embarrassment of the victimized students.

Physical and mental well-being and perceived institutional support

Prolonged hours of online learning and dependency on screen medium for entertainment and other leisure activities caused vision impairment (CNNIC, 2020 ), lack of motivation increased (Li & Lin, 2016 ), attention deficiency (Baumgartner et al., 2014 ), sleep disturbances, and neck stiffness (Kwon et al., 2013 ). This was further aggravated by the fact that some of them suffered COVID or have witnessed family members suffering and losing lives due to COVID. They were in need of psychological support to get out of traumatic situations and consultancies for physical and psychological well-being. Some of the institution arranged for weekly activities such yoga, meditation, aerobics, salsa, and lectures on emotional well-being virtually with and without help of smarts apps for students and staffs. In some institutes, faculty mentors engaged in talking with student mentees frequently for academic, career, and psychological counseling. As one of the faculty said

I will say my students- Move your eyeballs, clockwise and clockwise. Okay. Move your shoulders clockwise. And anticlockwise okay. So, this is going to really affect your heads close your eyes for 30 minutes, introspect retrospect, and write down all the things in a diary that you wanted to do in life and figure out how will you complete your dreams? That's how you get motivation.

Yet, in some cases, students missed the institutional support for psychological well-being and took the support of professional psychiatrists, sometimes without informing family and friends. In some cases, students felt that some faculty members took the opportunity of virtual learning environment to merely touch upon various topics without promoting in-depth discussion. In some cases, student felt the need of complaining about such faculty members to higher authorities. As one of the students in a business school reported:

I discussed the issue with my classmates and made them understand the importance of voicing our thoughts. It was something which was not ethically incorrect. So we as a group decided to talk to the respective faculty regarding the issue. We developed a plan of action for it as well. Our first step was to talk to the teacher and sort the issue. If the problem still persists, our next step was to reach to the Head of Department and address the issue so that necessary steps can be taken for better learning experience. Final step would be to talk to the administration department and address the issue. However, our teachers were very cooperative and they made us understand the topics by giving us some reference materials and video links

Need for peer to peer support versus academic integrity during assessment

Most educational institutes practiced a continuous evaluation process. The continuous evaluation included pre-recorded video presentation on a topic or cases during class sessions, in-class case analysis by dividing students into groups and allow them to analyze and discuss the cases in breakout rooms and present it before the class, lots of group or individual research–based projects based on primary or secondary, simulation test, written tests, and quizzes based on multiple choice questions. In-class verbal presentation or submissions of video records of case analysis or presentation was less susceptible to ethical violation. Also, it aided the learning assessments of students with disabilities. Though many higher educational institutes used multiple choice questions in proctored examination environment, but large-scale malpractices were observed with all students engaged in cheating and showing elevated performance. Faculty themselves seemed not too keen to use MCQs for evaluation.

I give the options of correct answers to my system, and the computer automatically calculates for the students' grades. I don't even see what they're writing, why they're writing it, why have they given the answer like that? It's simply a shortcut of getting the scores without my active involvement in the training process. In physical classrooms, I avoid MCQs altogether because that is not the right way to judge or gauge a students’ performance.

The large-scale cheating was observed in written assignment submission as well as descriptive tests. Proctored environment or use of plagiarism software did not restrict the students from using unfair means. Proctored software was dodged by using sticky note on the laptop screen. Students used Google search options and Google lens to look for answers and pass it to peers using instant messaging systems. For example, one of the faculty members in a business school shared:

There were some issues, or instances where I think there was a lot of plagiarism between the students in terms of let's say, cooperation and plagiarism, both between the students when it came to some of the assessments. So that way, I think I would say that's a little bit of both, we could call that an integral dilemma.

One of the reason with such large-scale malpractices that was observed in almost all the higher educational institute faculty and students was the lack of collaborative learning environment, direct interaction with faculty members, and peer learning opportunities that were missing in the online mode. Most students were undertaking their final professional degree course and were looking for placement. Their academic performance at the degree program will only be considered for screening by their recruiters and all that mattered was how they performed during the selection process. Hence, they were willing to collaborate with each other during the evaluation process to get higher grades. Faculty members and institutions seemed to be liberal or helpless on handling the copying cases as there was a feeling it was informed choices of the students ruining their own future. However, in some cases, Foucault’s theory was satisfied wherein well laid out rules and imposition of self-regulation could check academic non-integrity, as one of the students says that.

I do think that copying and getting marks easily creates a situation of injustice for all those students who have worked hard. Our teachers understood the situation and guided all the students to follow assignment rules. Some of the teachers even warned to give no marks if the content is copied from the internet. The sense of getting less marks made all understand that assignments should be done properly. Also, one important point that all students realized was that it helped them as they were able to understand the topics and implement them.

Some faculty also suggested application based open book test with high difficulty would test students’ critical thinking ability and cognitive scale, better reducing the use of unfair means.

Hence, the result of the study can be diagrammatically represented as given in Fig.  1

Diagrammatic representation of the theoretical model

We have found through this research that success of sudden shift to online learning depends on the support that is provided by the institute during the switch and how faculty are able to accept and adopt the new technology and mold the teaching and learning pedagogy to the new medium to cater the needs of learners with different learning styles and capability. Performance expectancy was low initially. Performance expectancy to the online medium was largely dependent on the socio-demographic characteristics of the participants, both educators and learners and their social capital, the importance of socio-economic characteristics and the social capital, and the importance of socio-demographic characteristics in the online learning (Diep et al., 2016 ). Effort expectancy to participate actively in the online learning medium was found higher in the beginning but gradually faded out. One of the faculty for example pointed out that the reason why students’ participation that was initially 80–90% which went down to 30–40% could be participants got physically and psychologically tired of the long time that was required to be spent on-screen. Also, the provision of records of sessions made available to students made them feel that they could go through the sessions at leisure during nights when the family members would not be there to disturb them. The success of online learning medium was much dependent on how engaging and interactive the class sessions were made by engaging students to debate, role play, browse online linkage, participate in polls, participate in short quizzes, etc. Students missed out collaborative learning environment such as learning through peer interaction, socializing, and managing self and others which can be facilitated in residential colleges, development of negotiation skills, while organizing events. But this was facilitated through simulations, multiple projects, research paper writing, multiple virtual internship projects, and scopes of listening to several corporate giants and academicians through leadership talks, etc. Hence, authentic self-paced learning can be facilitated through online learning platform though it gets moderated digital divide among the learners and educators. Both learners and educators seemed to be emphatic towards each other considering that it was necessitated by the criticality of the pandemic situation and agreed hybrid learning could be adopted during the new normal. Aberrations were observed in cases when the students felt delivery of sessions were being made intentionally sub-standard by the educators. They protested by either lodging complaints or using internet tools to disgrace the faculty. Putting cameras during the sessions by learners was considered a mean of disciplining the learners and making them active listeners and participants, but issues about ethical and moral concerns related to privacy arose. Written assessments almost in all the surveyed institutes, either through proctored or non-proctored system, had seen the use of wide-scale unfair means. The only way of combating it could be through imposing self-discipline or making assessment more application-based. Long hours of online teaching and learning impacted adversely both teachers and learners. This was aggravated by frequent cases of COVID. But educational institutes have adopted measures of physical and emotional well-being of the stakeholders.

Limitations

There were limitations to the study. The sample comprised of leaners and educators of engineering and management school and hence may not be generalizable. Secondly, participants were recruited with voluntary participation and self-administered interview schedule which may lead to some bias. The scope can be broadened to encompass various streams and levels of educations in multi-cultural contexts in the future. Sample size though small was based on agreed satisfaction of theoretical saturation among the researchers about the interview responses. An interesting study would be on how the teaching and learning adopted to scientific experimentation in the online medium during the pandemic would have been interesting. The study was based on convenience sampling during a time when educational institutes had only partially opened for offline teaching. The study can form the base for a broader study on feasibility of implementing hybrid of learning in education.

Recommendations

Educators in the previous 2 years developed the art and science of planning, applying, reflecting, accessing various technologies, and finally evaluating to make online learning most effective. In the future, effective use of information and communication technology in combining classroom learning with asynchronous and synchronous mode of learning should be debated among experienced educators and learners (Johnston et al., 2018 ; Patterson & Han, 2019 ). Discussions should take into account social, physical, ethical, and psychological considerations of online learning. Best practices can be recorded and guidelines for hybrid mode of learning can be laid out for higher education.

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• Agostinho S. Naturalistic inquiry in e-learning research. International Journal of Qualitative Methods. 2005; 4 (1):13–26. doi: 10.1177/160940690500400102. [ CrossRef ] [ Google Scholar ]
• Alessio HM, Malay N, Maurer K, Bailer AJ, Rubin B. Examining the effect of proctoring on online test scores. Online Learning. 2017; 21 (1):146–161. doi: 10.24059/olj.v21i1.885. [ CrossRef ] [ Google Scholar ]
• Arnold DF, Bernardi RA, Neidermeyer PE, Schmee J. The effect of country and culture on perceptions of appropriate ethical actions prescribed by codes of conduct: A Western European perspective among accountants. Journal of Business Ethics. 2007; 70 (4):327–340. doi: 10.1007/s10551-006-9113-6. [ CrossRef ] [ Google Scholar ]
• Aung TN, Khaing SS (2015) Challenges of implementing e-learning in developing countries: A review. In International Conference on Genetic and Evolutionary Computing (pp 405–411). Springer, Cham
• Baumgartner SE, Weeda WD, van der Heijden LL, Huizinga M. The relationship between media multitasking and executive function in early adolescents. The Journal of Early Adolescence. 2014; 34 (8):1120–1144. doi: 10.1177/0272431614523133. [ CrossRef ] [ Google Scholar ]
• Becker R, Mehlkop G. Social class and delinquency: An empirical utilization of rational choice theory with cross-sectional data of the 1990 and 2000 German General Population Surveys (ALLBUS) Rationality and Society. 2006; 18 (2):193–235. doi: 10.1177/1043463106063323. [ CrossRef ] [ Google Scholar ]
• Binkley, S., & Capetillo, J. (Eds.). (2009). A Foucault for the 21st century: Governmentality, biopolitics and discipline in the new millennium . Cambridge Scholars Publishing.
• Bozalek V, Gachago D, Alexander L, Watters K, Wood D, Ivala E, Herrington J. The use of emerging technologies for authentic learning: A South African study in higher education. British Journal of Educational Technology. 2013; 44 (4):629–638. doi: 10.1111/bjet.12046. [ CrossRef ] [ Google Scholar ]
• Burrus RT, McGoldrick K, Schuhmann PW. Self-reports of student cheating: Does a definition of cheating matter? The Journal of Economic Education. 2007; 38 (1):3–16. doi: 10.3200/JECE.38.1.3-17. [ CrossRef ] [ Google Scholar ]
• Chudzicka-Czupała, A., Grabowski, D., Mello, A. L., Kuntz, J., Zaharia, D. V., Hapon, N., ... & Börü, D. (2016). Application of the theory of planned behavior in academic cheating research–cross-cultural comparison.Ethics & Behavior 26(8), 638-659
• CNNIC (2020). The 45th China statistical report on internet development. Beijing: CNNIC.
• Collis C, Foth M, Schroeter R. The Brisbane media map: Participatory design and authentic learning to link students and industry. Learning Inquiry. 2009; 3 (3):143–155. doi: 10.1007/s11519-009-0046-8. [ CrossRef ] [ Google Scholar ]
• Daffin LW, Jr, Jones AA. Comparing student performance on proctored and non-proctored exams in online psychology courses. Online Learning. 2018; 22 (1):131–145. doi: 10.24059/olj.v22i1.1079. [ CrossRef ] [ Google Scholar ]
• Day NE, Hudson D, Dobies PR, Waris R. Student or situation? Personality and classroom context as predictors of attitudes about business school cheating. Social Psychology of Education. 2011; 14 (2):261–282. doi: 10.1007/s11218-010-9145-8. [ CrossRef ] [ Google Scholar ]
• Diep NA, Cocquyt C, Zhu C, Vanwing T. Predicting adult learners’ online participation: Effects of altruism, performance expectancy, and social capital. Computers & Education. 2016; 101 :84–101. doi: 10.1016/j.compedu.2016.06.002. [ CrossRef ] [ Google Scholar ]
• Eisenhardt KM. Building theories from case study research. Academy of Management Review. 1989; 14 (4):532–550. doi: 10.2307/258557. [ CrossRef ] [ Google Scholar ]
• Fask A, Englander F, Wang Z. Do online exams facilitate cheating? An experiment designed to separate possible cheating from the effect of the online test taking environment. Journal of Academic Ethics. 2014; 12 (2):101–112. doi: 10.1007/s10805-014-9207-1. [ CrossRef ] [ Google Scholar ]
• Finchilescu G, Cooper A. Perceptions of academic dishonesty in a South African university: A Q-methodology approach. Ethics & Behavior. 2018; 28 (4):284–301. doi: 10.1080/10508422.2017.1279972. [ CrossRef ] [ Google Scholar ]
• Fitzgerald A, Dawson V, Hackling M. Examining the beliefs and practices of four effective Australian primary science teachers. Research in Science Education. 2013; 43 (3):981–1003. doi: 10.1007/s11165-012-9297-y. [ CrossRef ] [ Google Scholar ]
• Golden J, Kohlbeck M. Addressing cheating when using test bank questions in online Classes. Journal of Accounting Education. 2020; 52 :100671. doi: 10.1016/j.jaccedu.2020.100671. [ CrossRef ] [ Google Scholar ]
• Herrington, J., McKenzie, S., Pascoe, R., Woods-McConney, A., MacCallum, J., & Wright, P. (2010, May). A whole-school approach to technological literacy: Mobile learning and the iPhone. In  Global Learn  (pp. 181–186). Association for the Advancement of Computing in Education (AACE).
• Jian H, Li G, Wang W. Perceptions, contexts, attitudes, and academic dishonesty in Chinese senior college students: A qualitative content-based analysis. Ethics & Behavior. 2020; 30 (7):543–555. doi: 10.1080/10508422.2020.1711758. [ CrossRef ] [ Google Scholar ]
• Johnston E, Olivas G, Steele P, Smith C, Bailey L. Exploring pedagogical foundations of existing virtual reality educational applications: A content analysis study. Journal of Educational Technology Systems. 2018; 46 (4):414–439. doi: 10.1177/0047239517745560. [ CrossRef ] [ Google Scholar ]
• Kwon, M., Lee, J. Y., Won, W. Y., Park, J. W., Min, J. A., Hahn, C., ... & Kim, D. J. (2013).Development and validation of a smartphone addiction scale (SAS). PLoS ONE 8(2), e56936 [ PMC free article ] [ PubMed ]
• Lee, K., Fanguy, M., Bligh, B., & Lu, X. S. (2022). Adoption of online teaching during the COVID-19 pandemic: A systematic analysis of changes in university teaching activity.  Educational Review , 1–24.
• Li L, Lin TT. Exploring work-related smartphone dependency among young working adults in China: A qualitative approach. International Journal of Communication. 2016; 10 :19. [ Google Scholar ]
• Lonsdale D. Intentions to cheat: Ajzen’s planned behavior and goal-related personality facets. The Journal of Psychology. 2017; 151 (2):113–129. doi: 10.1080/00223980.2016.1241737. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Machles D. Situated Learning. Professional Safety. 2003; 48 (9):22–28. [ Google Scholar ]
• Miles MB, Huberman AM. Qualitative data analysis: An expanded sourcebook. Sage; 1994. [ Google Scholar ]
• Nathanson C, Paulhus DL, Williams KM. Predictors of a behavioral measure of scholastic cheating: Personality and competence but not demographics. Contemporary Educational Psychology. 2006; 31 (1):97–122. doi: 10.1016/j.cedpsych.2005.03.001. [ CrossRef ] [ Google Scholar ]
• Nyst C, Falchetta T. The right to privacy in the digital age. Journal of Human Rights Practice. 2017; 9 (1):104–118. doi: 10.1093/jhuman/huw026. [ CrossRef ] [ Google Scholar ]
• Ozverir I, Herrington J, Osam UV. Design principles for authentic learning of English as a foreign language. British Journal of Educational Technology. 2016; 47 (3):484–493. doi: 10.1111/bjet.12449. [ CrossRef ] [ Google Scholar ]
• Parker J, Maor D, Herrington J. Authentic online learning: Aligning learner needs, pedagogy and technology. Issues in Educational Research. 2013; 23 (2):227–241. [ Google Scholar ]
• Patterson T, Han I. Learning to teach with virtual reality: Lessons from one elementary teacher. TechTrends. 2019; 63 (4):463–469. doi: 10.1007/s11528-019-00401-6. [ CrossRef ] [ Google Scholar ]
• Peled Y, Eshet Y, Barczyk C, Grinautski K. Predictors of academic dishonesty among undergraduate students in online and face-to-face courses. Computers & Education. 2019; 131 :49–59. doi: 10.1016/j.compedu.2018.05.012. [ CrossRef ] [ Google Scholar ]
• Postma L, Blignaut AS, Swan K, Sutinen EA. Reflections on the use of grounded theory to uncover patterns of exclusion in an online discussion forum at an institution of higher education. International Journal of Qualitative Methods. 2013; 12 (1):529–550. doi: 10.1177/160940691301200128. [ CrossRef ] [ Google Scholar ]
• Prosser, M., & Trigwell, K. (1999). Understanding learning and teaching: The experience in higher education . McGraw-Hill Education (UK).
• Pu YH, Wu TT, Chiu PS, Huang YM. The design and implementation of authentic learning with mobile technology in vocational nursing practice course. British Journal of Educational Technology. 2016; 47 (3):494–509. doi: 10.1111/bjet.12443. [ CrossRef ] [ Google Scholar ]
• Pulfrey CJ, Vansteenkiste M, Michou A. Under pressure to achieve? The impact of type and style of task instructions on student cheating. Frontiers in Psychology. 2019; 10 :1624. doi: 10.3389/fpsyg.2019.01624. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Qiao P, Zhu X, Guo Y, Sun Y, Qin C. The development and adoption of online learning in pre-and post-COVID-19: Combination of technological system evolution theory and unified theory of acceptance and use of technology. Journal of Risk and Financial Management. 2021; 14 (4):162. doi: 10.3390/jrfm14040162. [ CrossRef ] [ Google Scholar ]
• Reamer FG. Social work in a digital age: Ethical and risk management challenges. Social Work. 2013; 58 (2):163–172. doi: 10.1093/sw/swt003. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sinkovics RR, Penz E, Ghauri PN. Enhancing the trustworthiness of qualitative research in international business. Management International Review. 2008; 48 (6):689–714. doi: 10.1007/s11575-008-0103-z. [ CrossRef ] [ Google Scholar ]
• Steele PB, Burleigh C, Young EA. Considering educators’ perceptions of ethics and safety in virtual learning environments (VLEs) Journal of Educational Research and Practice. 2020; 10 (1):404–418. doi: 10.5590/JERAP.2020.10.1.26. [ CrossRef ] [ Google Scholar ]
• Steinmetz KF. Message received: Virtual ethnography in online message boards. International Journal of Qualitative Methods. 2012; 11 (1):26–39. doi: 10.1177/160940691201100103. [ CrossRef ] [ Google Scholar ]
• Suliman M, Abdalrhim A, Tawalbeh L, Aljezawi M. The impact of online synchronous versus asynchronous classes on nursing students’ knowledge and ability to make legal and ethical decisions. Nurse Education Today. 2022; 109 :105245. doi: 10.1016/j.nedt.2021.105245. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tatum H, Schwartz BM. Honor codes: Evidence based strategies for improving academic integrity. Theory into Practice. 2017; 56 (2):129–135. doi: 10.1080/00405841.2017.1308175. [ CrossRef ] [ Google Scholar ]
• Teräs, H., Teräs, M., & Herrington, J. (2012, June). A reality check: Taking authentic e-learning from design to implementation. In  EdMedia+ Innovate Learning  (pp. 2219–2228). Association for the Advancement of Computing in Education (AACE).
• Valtonen T, Kukkonen J, Kontkanen S, Sormunen K, Dillon P, Sointu E. The impact of authentic learning experiences with ICT on pre-service teachers’ intentions to use ICT for teaching and learning. Computers & Education. 2015; 81 :49–58. doi: 10.1016/j.compedu.2014.09.008. [ CrossRef ] [ Google Scholar ]
• Williams MWM, Williams MN. Academic dishonesty, self-control, and general criminality: A prospective and retrospective study of academic dishonesty in a New Zealand university. Ethics & Behavior. 2012; 22 (2):89–112. doi: 10.1080/10508422.2011.653291. [ CrossRef ] [ Google Scholar ]
• Yang SC, Huang CL, Chen AS. An investigation of college students’ perceptions of academic dishonesty, reasons for dishonesty, achievement goals, and willingness to report dishonest behavior. Ethics & Behavior. 2013; 23 (6):501–522. doi: 10.1080/10508422.2013.802651. [ CrossRef ] [ Google Scholar ]
• Youn S. Determinants of online privacy concern and its influence on privacy protection behaviors among young adolescents. Journal of Consumer Affairs. 2009; 43 (3):389–418. doi: 10.1111/j.1745-6606.2009.01146.x. [ CrossRef ] [ Google Scholar ]
• Young D. Perspectives on cheating at a Thai University. Language Testing in Asia. 2013; 3 (1):1–15. doi: 10.1186/2229-0443-3-6. [ CrossRef ] [ Google Scholar ]

Ethical Issues in Online Learning Research Paper

Online education is a common form of learning today. Learners are now benefitting from online learning process without having to go to school. Online learning has been promoted by modern technologies. These technologies include computers, smart phones and the internet.

However, this promising innovation has its own set of challenges. The issue of identity is a major concern in online learning. Some experts argue that distance education can lead to cheating and academic dishonesty (Oosterhof, Conrad & Ely, 2007).

Student participating in the learning process might not be the ones who get the academic certificate. This has led to numerous ethical concerns and issues about online learning.

To minimize dishonesty in online education, it is necessary to have appropriate assessment methods. During the assessment period, it is necessary to have various methods to deter all forms of dishonesty. During the assessment process, examiners should use various methods to ensure the right person is assessed.

Examiners should employ various accreditation procedures to minimize chances of academic dishonesty. For instance, the assessment can be through online conferencing. This will ensure the registered student completes the assessment process.

The examiner can use electronic conferencing to verify the image of the student. This will ensure the right person completes the assessment process (Palloff & Pratt, 2009).

The assessment method should be able to minimize all forms of dishonesty. During online learning, the exam should be time-framed to ensure the student finishes the exercise within a given time. The approach will reduce the possibilities of cheating.

These methods will ensure there is accountability during the assessment process. The other important thing to be addressed is the student demographics. Whenever conducting an online assessment, the examiners should ensure all the registered students complete the examination at the same time (Palloff & Pratt, 2009).

This is an effective way that can help to reduce academic dishonesty during online learning. I support these methods because they will reduce all instances of cheating. The approaches will make online learning effective for both the institution and the student.

It is notable that online learning gives the learner an opportunity to cheat. Learners tend to use online information and content whenever they are assessed online. This results in plagiarism and academic dishonesty.

The online assessment methods should consider the ethical issues arising from the learning process. The learning procedure should reveal the skills and competencies of the learner. Any form of dishonesty will give false results (Oosterhof, Conrad & Ely, 2007).

The above assessment methods will preempt all forms of cheating and plagiarism. For instance, the use of anti-plagiarism software is an effective method that can minimize instances of plagiarism.

Plagiarism is a serious offense committed during online learning. The use of anti-plagiarism software will catch plagiarists. The plagiarists will also be punished accordingly.

The assessment methods should be able to prevent all forms of dishonesty during the learning process. The learners should consider the need for advanced tools and software to detect plagiarism in most of the submitted assignments and works.

The exam should be time-framed to ensure the learner delivers the required content within a given period (Palloff & Pratt, 2009). These approaches will deter all forms of dishonesty during online learning processes.

The registered learners should be able to complete the assessments and be provided with the relevant academic certificates. These methods will address the ethical issues associated with online learning.

Oosterhof, A., Conrad, R. & Ely, D. (2007). Assessing Learners Online. Upper Saddle River, NJ: Pearson.

Palloff, R. & Pratt, K. (2009). Assessing the Online Learner. San Francisco, CA: John Wiley & Sons.

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Ethics of research into learning and teaching with Web 2.0: reflections on eight case studies

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The unique features and educational affordances of Web 2.0 technologies pose new challenges for conducting learning and teaching research in ways that adequately address ethical issues of informed consent, beneficence, respect, justice, research merit and integrity. This paper reviews these conceptual bases of human research ethics and gives examples of their consideration in the literature of research into learning and teaching with Web 2.0. The paper goes on to give an account of reflective practice by two academic developers in relation to ethical issues they encountered, considered and addressed in eight case studies, which were part of a larger multi-university Australian study into learning and teaching with Web 2.0. The paper concludes that the human research ethics approval process needs to be understood as a series of measures that are important to protect not only the students but also the teacher-researchers and their institutions when doing learning and teaching research with Web 2.0. This understanding is important for educators and as well for educational developers, educational technologists and human research ethics review committees (also known as institutional review boards).

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Web 1.0 and Web 2.0: An Anglo-Portuguese Research Project on the Impact of Using Technology in Science Education Programmes

A Survey of Academics’ Views of the Use of Web2.0 Technologies in Higher Education

Perceptions of pedagogical formation students about Web 2.0 tools and educational practices

Alexander, B. (2006). Web 2.0: A new wave of innovation for teaching and learning? Educause Review, 41 (2), 32–44. http://connect.educause.edu/Library/EDUCAUSE+Review/Web20ANewWaveofInnovation/40615 . Accessed March 1, 2010.

Bakardjieva, M. (2008). Ethics 2.0: Balancing privacy, publicity and prudence. Paper presented at the Virtual Knowledge Studio, Amsterdam, Netherlands. http://virtualknowledgestudio.nl/events/lecture-by-maria-bakardjieva/ . Accessed March 1, 2010.

Block, J. J. (2008). Issue for DSM-V: Internet addiction. American Journal of Psychiatry . doi: 10.1176/appi.ajp.2007.07101556 http://ajp.psychiatryonline.org/cgi/content/full/165/3/306 . Accessed April 12, 2010.

Boud, D., Keogh, R., &Walker, D. (1985). Reflection: Turning experience into learning . London: Kogan Page.

Brookfield, S. (1995). Becoming a critically reflective teacher . San-Francisco: Jossey-Bass.

Google Scholar  

Buchanan, E. A. (2009). Internet research ethics: Past, present, and future. In R. Burnett, M. Consalvo, & C. Ess (Eds.), The handbook of internet studies (pp. 83–108). Chichester, UK: Wiley-Blackwell.

Buchanan, E. A., & Hvizdak, E. E. (2009). Online survey tools: Ethical and methodological concerns of human research ethics committees. Journal of Empirical Research on Human Research Ethics, 4 (2), 37–48.

Article   Google Scholar  

Byun, S., Ruffini, C., Mills, J., Douglas, A., Niang, M., Stepchenkova, S., et al. (2009). Internet addiction: Metasynthesis of 1996–2006 quantitative research. Cyberpsychology & Behavior, 12 (2), 203–207.

Chang, R. L, Gray, K., Polus, B. & Radloff, A. (2005). Scholarly teaching practice: Ethics issues and responses in research into teaching in tertiary education. In A. Brew & C. Asmar (Eds.), HERDSA Vol. 28. Higher education in a changing world (pp. 93–100). Sydney: HERDSA. Available online: http://conference.herdsa.org.au/2005/pdf/refereed/paper_284.pd .

Cherry, N. (2010). Doing qualitative research in the white spaces. In J. Higgs, N. Cherry, R. Macklin, & R. Ajjawi (Eds.), Researching practice: A discourse on qualitative methodologies (pp. 9–17). Rotterdam: Sense Publishers.

Cho, C. (1999). Internet addiction in Taiwan . National Science Council Project, under Project no. NSC 87-2511-S-009-013-N.

Chou, C., Condron, L., & Bellard, J. (2005). A review of the research in internet addiction. Educational Psychology Review, 17 (4), 363–388. doi: 10.1007/s10648-005-8138-1 .

Clegg, S. (2009). Forms of knowing and academic development practice. Studies in Higher Education, 34 (4), 403–416. doi: 10.1080/03075070902771937 .

Cunliffe, A. (2004). On becoming a critically reflexive practitioner. Journal of Management Education, 28 (4), 407–426.

Dalsgaard, C. (2006). Social software: E-learning beyond learning management systems . European Journal of Open, Distance and E - Learning , 2. http://www.eurodl.org/materials/contrib/2006/Christian_Dalsgaard.htm . Accessed April 1, 2008.

Del Carlo, D., Hinkhouse, H., & Isbell, L. (2010). Developing a reflective practitioner through the connection between education research and reflective practices. Journal of Science Education and Technology, 19 (1), 58–68.

Dohn, N. (2009). Web 2.0: Inherent tensions and evident challenges for education. International Journal of Computer-Supported Collaborative Learning, 4 , 343–363. doi: 10.1007/s11412-009-9066-8 .

dsm5.org. (2012). Substance-related disorders . http://www.dsm5.org/proposedrevisions/pages/substance-relateddisorders.aspx . Accessed April 3, 2012.

Duihua.org. (2007). Police document sheds additional light on Shi Tao case . http://www.duihua.org/2007/07/police-document-sheds-additional-light.html . Accessed April 12, 2010.

The Economist. (2011). Addicted? Really? Technology Quarterly (Q1). http://www.economist.com/18304258 . Accessed April 3, 2012.

Fendler, L. (2011). Web 2.0 and the future of educational research: pedagogical, political and epistemological implications. In D. Trohler & R. Barbu, (Eds.). Education Systems in Historical, Cultural, and Sociological Perspectives (pp. 124–141). Rotterdam: Sense Publishers. doi: 10.1007/978-94-6091-827-8_9 .

Fitzpatrick, J. (2009). Five best video sharing sites . http://lifehacker.com/5343601/five-best-video+sharing-sites . Accessed April 1, 2010.

Fowler, J., Gudmundsson, A., & Whicker, L. (2006). Group work: A guide to working in groups . Brisbane: Australian Academic Press.

Franklin, T., & van Harmelen, M. (2007). Web 2.0 for content for learning and teaching in higher education . JISC Report. http://www.jisc.ac.uk/media/documents/programmes/digitalrepositories/web2-content-learning-and-teaching.pdf . Accessed April 1, 2008.

Gilson, D. (2010). WikiLeaks gets a facelift . San Francisco: Mother Jones. http://motherjones.com/mojo/2010/05/wikileaks-assange-returns . Accessed June 17, 2010.

Gray, K. (2008). Educational technology practitioner-research ethics. In M. Quigley (Ed.), Encyclopedia of information ethics and security (pp. 164–169). Hershey, PA, USA: Idea Group Inc.

Gray, K., Kennedy, G., Waycott, J., Dalgarno, B., Bennett, S., Chang, R., et al. (2009). Educating the net generation: A toolkit of resources for educators in Australian Universities . Australian Learning and Teaching Council. http://www.netgen.unimelb.edu.au/outcomes/toolkit.html .

Gray, K., Thompson, C., Sheard, J., Clerehan, R., & Hamilton, M. (2010).Students as Web 2.0 authors: Implications for assessment design and conduct. Australasian Journal of Educational Technology, 26 (1), 105–122.

Greenhow, C., Robelia, B., & Hughes, J. (2009). Learning, teaching, and scholarship in a digital age: Web 2.0 and classroom research: What path should we take now? Educational Researcher . doi: 10.3102/0013189X09336671 .

Hutchings, P. (2002). Ethics and aspiration in the scholarship of teaching and learning. In P. Hutchings (Ed.), Ethics of inquiry: Issues in the scholarship of teaching and learning . Menola Park: Carnegie Publications.

Hutchings, P. (2003). Competing goods: Ethical issues in the scholarship of teaching and learning. Change . September/October, 27–33.

Kanuka, H., & Anderson, T. (2007). Ethical issues in qualitative e-learning research. International Journal of Qualitative Methods, 6 (2), Article 2. http://www.ualberta.ca/~iiqm/backissues/6_2/kanuka.pdf . Accessed January 21, 2009.

Kennedy, G., Dalgarno, B., Bennett, S., Gray K., Waycott, J., Judd, T. et al. (2009). Educating the net generation: A handbook of findings for practice and policy. http://www.netgen.unimelb.edu.au/downloads/handbook/Sec6PolicyGuidelines.pdf . Accessed April 10, 2012.

Kennedy, G., Judd, T., Dalgarno, B., & Waycott, J. (2010). Beyond natives and immigrants: Exploring the characteristics of net generation students. Journal of Computer Assisted Learning, 26 , 332–343.

Lindorff, M. (2010). Ethics, ethical human research and human ethics committees. Australian Universities Review, 52 (1), 51–59.

Liu, E. Z. F. (2011). Avoiding Internet addiction when integrating digital games into teaching. Social Behaviour and Personality, 39 (10), 1325–1336.

Moon, J. (2004). A handbook of reflective and experiential learning . London: Routledge Farmer.

Moreno, M., Fost, N., & Chritakis, D. (2008). Research ethics in the myspace era. Pediatrics, 121 (1), 157–161.

Moschini, E. (2008). The second life researcher toolkit—An exploration of in-world tools, methods and approaches for researching educational projects in second life. In ReLive08: Researching Learning in Virtual Environments Conference, 20 – 21 November 2008 (pp. 237–245). Open University, Milton Keynes, UK. http://www.open.ac.uk/relive08/documents/ReLIVE08_conference_proceedings_Lo.pdf#page=237 . Accessed January 21, 2009.

National Health and Medical Research Council (NHMRC)/Australian Research Council/Australian Vice-Chancellors’ Committee. (2007). National statement on ethical conduct in human research . Canberra: Australian Government. http://www.nhmrc.gov.au/publications/ethics/2007_humans/contents.htm . Accessed August 13, 2010.

Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, 9 (5), 1–6.

Race, P. (2001). The lecturers’ toolkit: A resource for developing learning, teaching and assessment (2nd ed.). London: RoutledgeFarmer.

Reeves, T., Herrington, J., & Oliver, R. (2005). Design research: A socially responsible approach to instructional technology research in higher education. Journal of Computing in Higher Education, 16 (2), 97–116.

Richardson, W. (2006). Blogs, wikis, podcasts, and other powerful web tools for classrooms . Thousand Oaks, CA: Corwin Press.

Sachs, B. (2010). The exceptional ethics of the investigator-subject relationship. Journal of Medicine and Philosophy . doi: 10.1093/jmp/jhp055 .

Schön, D. A. (1983). Educating the reflective practitioner . San Francisco: Jossey-Bass Publishers.

Tiropanis, T., Davis, H., Millard, D.,& Weal. M. (2009). Semantic technologies for learning and teaching in the Web 2.0 era. Intelligent systems, IEEE, 24 (6), 49–53. http://eprints.soton.ac.uk/268429/1/ieee-is-tiropanis-etal-2009.pdf . Accessed April 11, 2012.

Vavoula, G.N., & Sharples, M. (2008). Challenges in evaluating mobile learning. In Traxler, J., Riordan, B., & Dennett, C. (Eds.). Proceedings of the mLearn 2008 conference: The bridge from text to context, 7 – 10 October 2009 (pp. 296–303). Wolverhampton, UK: University of Wolverhampton, School of Computing and Information Technology. http://www.scit.wlv.ac.uk/brendan/mLearn2008.pdf . Accessed January 21, 2009.

Vella, J. (1994). Learning to listen, learning to teach: The power of dialogue in educating adults . San Francisco: Jossey-Bass Publishers.

Wang, F., & Hannafin, M. (2005). Design-based research and technology-enhanced learning environments. Educational Technology Research and Development, 53 (4), 5–23.

Waycott, J., Gray, K., Clerehan, R., Hamilton, M., Richardson, J., Sheard, J., & Thompson, C. (2010). Implications for academic integrity of using web2.0 for teaching, learning and assessment inhigher education . International Journal for Educational Integrity, 6 (2), 8–18. Available online at: http://www.ojs.unisa.edu.au/journals/index.php/IJEI/ . Accessed December 1, 2010.

Young, K. S. &Nabuco de Abreu, C. (Eds.). (2011) Internet addiction, a handbook and guide to evaluation and treatment . Hoboken: John Wiley & Sons, Inc.

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Acknowledgments

The authors would like to acknowledge advice from Dr Llew Mann and Dr Mark Schier and the Engineering and Science Education Research Group, Swinburne University; and Ms Cathy Schapper, and the journal’s reviewers. Support for the original work was provided by the Australian Learning and Teaching Council Ltd, an initiative of the Australian Government Department of Education, Employment and Workplace Relations. The views expressed in this article do not necessarily reflect the views of the Australian Learning and Teaching Council Ltd.

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Chang, R.L., Gray, K. Ethics of research into learning and teaching with Web 2.0: reflections on eight case studies. J Comput High Educ 25 , 147–165 (2013). https://doi.org/10.1007/s12528-013-9071-9

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Published on 25.4.2024 in Vol 26 (2024)

Leveraging Large Language Models for Improved Patient Access and Self-Management: Assessor-Blinded Comparison Between Expert- and AI-Generated Content

Authors of this article:

Original Paper

• Xiaolei Lv 1, 2, 3, 4, 5, 6 , MSc   ; 
• Xiaomeng Zhang 1, 2, 3, 4, 5, 6 , PhD   ; 
• Yuan Li 1, 2, 3, 4, 5, 6 , MSc   ; 
• Xinxin Ding 1, 2, 3, 4, 5, 6 , PhD   ; 
• Hongchang Lai 1, 2, 3, 4, 5, 6 , Prof Dr Med, PhD   ; 
• Junyu Shi 1, 2, 3, 4, 5, 6 , PhD  

1 Department of Oral and Maxillofacial Implantology, Shanghai PerioImplant Innovation Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

2 College of Stomatology, Shanghai Jiao Tong University, Shanghai, China

3 National Center for Stomatology, Shanghai, China

4 National Clinical Research Center for Oral Diseases, Shanghai, China

5 Shanghai Key Laboratory of Stomatology, Shanghai, China

6 Shanghai Research Institute of Stomatology, Shanghai, China

Corresponding Author:

Junyu Shi, PhD

Department of Oral and Maxillofacial Implantology, Shanghai PerioImplant Innovation Center

Shanghai Ninth People's Hospital

Shanghai Jiao Tong University School of Medicine

Quxi Road No 500

Shanghai, 200011

Phone: 86 21 23271699 ext 5298

Email: [email protected]

Background: While large language models (LLMs) such as ChatGPT and Google Bard have shown significant promise in various fields, their broader impact on enhancing patient health care access and quality, particularly in specialized domains such as oral health, requires comprehensive evaluation.

Objective: This study aims to assess the effectiveness of Google Bard, ChatGPT-3.5, and ChatGPT-4 in offering recommendations for common oral health issues, benchmarked against responses from human dental experts.

Methods: This comparative analysis used 40 questions derived from patient surveys on prevalent oral diseases, which were executed in a simulated clinical environment. Responses, obtained from both human experts and LLMs, were subject to a blinded evaluation process by experienced dentists and lay users, focusing on readability, appropriateness, harmlessness, comprehensiveness, intent capture, and helpfulness. Additionally, the stability of artificial intelligence responses was also assessed by submitting each question 3 times under consistent conditions.

Results: Google Bard excelled in readability but lagged in appropriateness when compared to human experts (mean 8.51, SD 0.37 vs mean 9.60, SD 0.33; P =.03). ChatGPT-3.5 and ChatGPT-4, however, performed comparably with human experts in terms of appropriateness (mean 8.96, SD 0.35 and mean 9.34, SD 0.47, respectively), with ChatGPT-4 demonstrating the highest stability and reliability. Furthermore, all 3 LLMs received superior harmlessness scores comparable to human experts, with lay users finding minimal differences in helpfulness and intent capture between the artificial intelligence models and human responses.

Conclusions: LLMs, particularly ChatGPT-4, show potential in oral health care, providing patient-centric information for enhancing patient education and clinical care. The observed performance variations underscore the need for ongoing refinement and ethical considerations in health care settings. Future research focuses on developing strategies for the safe integration of LLMs in health care settings.

Introduction

Since the launch of ChatGPT by OpenAI [ 1 ] in November 2022, the model has attracted significant global attention, securing over a million users within just 5 days of its release [ 2 ]. ChatGPT is a notable representative of large language models (LLMs), built upon the solid foundation of the GPT architecture [ 3 ]. In today’s technology landscape, other technology giants, including Google and Microsoft, have also developed proprietary and open-source LLMs. These models, pretrained on extensive unlabeled text data sets using self-supervised or semisupervised learning techniques, demonstrate exceptional natural language processing capabilities [ 4 ]. Their advanced capabilities in understanding and generating human-like responses make them particularly relevant for applications in health care, a sector that increasingly relies on digital information and interaction.

The significant potential of such models in the health care sector has captured wide attention among medical professionals [ 5 ]. Notably, without any specialized training or reinforcement, ChatGPT-3.5 performed at or near the passing threshold for the United States Medical Licensing Examination [ 6 ]. This underscores its vast capabilities within medicine, such as retrieving knowledge, aiding clinical decisions, summarizing key findings, triaging patients, and addressing primary care issues. Given its proficiency in generating human-like texts, one of the key applications of LLMs lies in improving health care access and quality through better patient information dissemination.

Early studies have primarily assessed its performance in responding to fundamental questions concerning cardiovascular diseases, cancers, and myopia, yielding encouraging results [ 7 - 10 ]. However, the broader impact of LLMs on patient health care access and quality, particularly in specialized areas such as oral health, has yet to be fully explored. Oral diseases affect over 3.5 billion people worldwide, leading to significant health and economic implications and substantially reducing the quality of life for those affected [ 11 ]. The historical marginalization of oral health care has resulted in considerable gaps in patient literacy, hygiene awareness, and medical consultations [ 12 - 14 ], highlighting a critical area where LLMs could make a significant difference. LLMs have the potential to bridge these gaps by providing accessible, accurate information and advice, thus enhancing patient understanding and self-management. Furthermore, the scarcity of health workers and disparities in resource distribution exacerbate these issues [ 15 , 16 ]. In this context, LLMs, with their rapid advancements, offer a promising avenue for enhancing health care access and quality across various domains [ 17 , 18 ]. A US survey revealed that about two-thirds of adults seek health information on the web and one-third attempt self-diagnosis via search engines [ 19 ]. This trend underscores the growing role of LLMs in digital health interventions [ 20 ], potentially enabling patients to overcome geographical and linguistic barriers in accessing high-quality medical information.

To explore this potential, this study focuses on oral health as an example, assessing the ability of the leading publicly available LLMs, such as Google Bard (Alphabet Inc; subsequently rebranded as Gemini) [ 21 ], ChatGPT-3.5, and ChatGPT-4, in providing patient recommendations for the prevention, screening, and preliminary management of common oral health issues compared to human experts. Both experienced dentists and lay users without medical backgrounds have been invited to evaluate the responses blindly along specified criteria. Our findings are intended to offer valuable insights into the potential benefits and risks associated with using LLMs for addressing common medical questions.

Ethical Considerations

Participants in this study were sourced from our earlier research project, “Bio-bank Construction of Terminal Dentition,” which was approved by the Ethical Committee of Shanghai Ninth People’s Hospital, China (SH9H-2021-T394-2). All participants provided written informed consent prior to the commencement of the study, which clarified their rights to participate and the ability to withdraw from the study at any time. All personal information in this study was anonymized to ensure the privacy and confidentiality of participant data. No compensation was provided to the participants.

Study Design

Figure 1 illustrates the overall study flow diagram. From August 9 to 23, 2023, a questionnaire survey was conducted among outpatients in the Department of Oral and Maxillofacial Implantology at Shanghai Ninth People’s Hospital to inquire about their primary concerns regarding periodontal and implant-related diseases. Informed by the latest consensus reports on periodontal and peri-implant diseases [ 22 ] and clinical experience in tertiary care for periodontology and implantology, our specialist panel (YL, Ke Deng, and Miaoxuan Dai) listed a set of initial questions. Patients rated these on a scale from 0=no concern to 10=extremely concerned and could add any other significant concerns. The questionnaire was administered in Chinese, and the translation and cultural adaptation to English adhered to established guidelines for cross-cultural questionnaire adaptation [ 23 ]. The back translation method was used to ensure both accuracy and cultural appropriateness. After collecting the surveys, the expert panel conducted a thorough review and consolidation process. This involved analyzing patient ratings and comments to identify the most pertinent questions. As a result, a refined set of 40 questions was developed ( Multimedia Appendix 1 ). These questions comprehensively covered 6 domains of periodontal and dental implant-related diseases, including patient education, self-prevention, diagnosis, treatment, management, and support.

From September 4 to 18, 2023, the panel was asked to generate human expert responses to these questions. At the same time, each question was also input into the ChatGPT-3.5, ChatGPT-4, and Google Bard interface, and the subsequent 3 sets of responses were recorded. For the interactions with the LLMs, all responses were generated based on default parameter settings, including temperature and maximum tokens, without any additional specific parameter adjustments. Each question corresponds to a new session and finally has 4 responses. The 4 sets of responses were anonymized and randomly shuffled for evaluation by 5 experienced dentists (JS, Xinyu Wu, Xiaoyu Yu, XZ, and XD) and 5 lay users, respectively, along the axes presented in Multimedia Appendix 2 . The assignment was concealed from the evaluators and outcome examiners (XL and Xue Jiang).

To further understand the stability of responses, each question was submitted to the artificial intelligence (AI) interfaces 3 times from October 28 to 30, 2023. This process was conducted at the same time each day over a 3-day span with constant environmental conditions and model parameters. Each set of 3 responses was independently evaluated by 2 experienced dentists (JS and XL).

Readability Evaluation

The readability of each response was assessed using Readable (Added Bytes), a web-based readability tool featuring multiple indices. Three of the most widely used parameters were recorded. (1) Flesch-Kincaid Grade Level: this is equivalent to the US grade level of education. It shows the required education to be able to understand a text; (2) Gunning Fog Index: this generates a grade level between 0 and 20. It estimates the education level required to understand the text. Text aimed at the public should aim for a grade level of around 8. Text above a 17 requires a graduate-level understanding; and (3) Flesch Reading Ease Score: this is between 1 and 100, with 100 being the highest readability score. Scoring between 70 and 80 is equivalent to school grade level 8. This means text should be fairly easy for the average adult to read.

Dentist Evaluation

Five experienced dentists (JS, Xinyu Wu, Xiaoyu Yu, XZ, and XD) were asked to grade each response using the visual analog scale (VAS) along the following 3 axes ( Multimedia Appendix 2 ). The final results were synthesized based on the scores from all 5 dentists. The average was calculated after removing extreme values to minimize errors.

• Appropriateness: How appropriate is this answer to the patient asking? (0=totally inappropriate or incorrect and 10=totally appropriate)
• Comprehensiveness: Does the answer omit any content it should not? (0=yes, great clinical significance and 10=no missing content)
• Harmlessness: What is the extent and likelihood of possible harm? (0=severe harm and 10=no harm)

Lay User Evaluation

Five lay users were also asked to grade each response using the VAS along the following 2 axes ( Multimedia Appendix 2 ). Final results were synthesized based on the scores from all 5 lay users, and the average was calculated after removing extreme values.

• Intent capture: How well does the answer address the intent of the question? (0=does not address query and 10=addresses query)
• Helpfulness: How helpful is this answer to the user? (0=not helpful at all and 10=very helpful)

Further Evaluation of LLMs in Different Conditions and Domains

To further investigate whether the responses of LLMs differ across various conditions and domains, detailed subanalyses were conducted on 2 oral issues (periodontitis and dental implant) and 6 medical care domains (patients’ education, prevention, diagnosis, treatment, management, and support).

Stability Evaluation

Each question was submitted to the AI interfaces 3 times, and the responses were recorded. Two experienced dentists (JS and XL) independently evaluated each set of 3 responses. Responses were graded as “correct” or “incorrect” based on clinical judgment and the content or as “unreliable” if the 3 responses were inconsistent. Any set with at least 1 incorrect response was graded as incorrect.

Statistical Analysis

Statistical analyses were conducted using SAS software (version 9.4; SAS Institute) and GraphPad Prism 9 (GraphPad Software, Inc). Quantitative data of normal distribution were summarized as means and SDs. Intraclass correlation coefficient (ICC) was used to access interrater agreement. Repeated measures ANOVA was used to compare scores across the LLMs and human experts. Additionally, paired chi-square tests were used to assess the stability of AI responses. Statistical significance was set at a P <.05.

Readability Evaluation Results

In the readability evaluation, detailed in Table 1 and Figure 2 , Google Bard was found to be the most readable for the public. It scored the lowest on Flesch-Kincaid Grade Levels (mean 7.86, SD 0.96) and Gunning Fog Index (mean 9.62, SD 1.11) and the highest on the Flesch Reading Ease Score (mean 61.72, SD 6.64), indicating it was easier to comprehend and had superior readability (all P <.001). Furthermore, the word count for all 3 LLMs, averaging over 300 words, was significantly higher than the approximately 100 words typical for human experts.

a Flesch-Kincaid Grade and Gunning Fog Index show the education level needed for understanding; a lower score means that it is easier.

b Flesch Reading Ease Scores from 1 to 100, with a higher score meaning easier to read.

Dentist Evaluation Results

Table 2 and Figure 3 present the evaluation results of dentists. Google Bard demonstrated significantly lower appropriateness score than human experts (mean 8.51, SD 0.37 vs mean 9.60, SD 0.33; P =.03), while ChatGPT-3.5 and ChatGPT-4 got comparable scores (mean 8.96, SD 0.35 and mean 9.34, SD 0.47, respectively). Google Bard also showed a great level of missing content than ChatGPT-3.5 (mean 8.40, SD 0.60 vs mean 9.46, SD 0.14; P =.04). No other difference of comprehensiveness was significant between groups. All 3 LLMs showed superior harmlessness scores comparable with human experts (Google Bard: mean 9.34, SD 0.11; ChatGPT-3.5: mean 9.65, SD 0.20; ChatGPT-4: mean 9.69, SD 0.41; and human experts: mean 9.68, SD 0.4, out of a maximum score of 10). The ICC indicated “substantial” agreement among dentists with a value of 0.715.

Lay User Evaluation Results

Table 2 and Figure 4 display the evaluation results of lay users. No significant difference between the responses of LLMs and human experts, with both effectively capturing user intent and providing helpful answers for them (all P >.05). The ICC indicated “moderate” agreement among lay users with a value of 0.586.

Subanalysis Results

Subanalyses were conducted across the 2 oral issues and 6 medical care domains. In periodontal questions, Google Bard still demonstrated significantly lower appropriateness than human experts ( P =.04). In implant questions, Google Bard performed less appropriately than ChatGPT-4 and human experts ( P =.03 and P =.01, respectively) and less comprehensively than ChatGPT-3.5 and 4 ( P =.02 and P =.05, respectively). All 3 LLMs performed consistently well in harmlessness across 6 medical care domains. In terms of appropriateness and comprehensiveness, all 3 LLMs achieved comparable VAS scores with human experts in the “prevention” and “treatment” domains. In the “education,” “diagnosis,” “management,” and “support” domains, 2 ChatGPT models achieved comparable scores, while Google Bard was significantly less appropriate than human experts ( P =.01, P =.02, P =.04, and P =.03, respectively). Consistently, Google Bard omits more content than 2 ChatGPT models and human experts in these domains. What is more, in terms of intent capture, Google Bard performed better in the domains of “prevention,” “management,” and “support” than in the “diagnosis.” Detailed subanalyses are shown in Multimedia Appendices 3 and 4 .

Stability Evaluation Results

Table 3 presents the stability evaluation results. All 3 AI models answered 40 questions, except Google Bard, which did not answer the question “Is dental implant surgery painful?” in 2 of 3 attempts. ChatGPT-4 achieved the highest number of correct answers (n=34, 85%), the fewest incorrect answers (n=4, 10%), and the fewest unreliable answers (n=2, 5%). ChatGPT-3.5 had more correct responses than Google Bard (n=29, 72% vs n=25, 62%) but also recorded more incorrect responses (n=8, 20% vs n=7, 17%). Moreover, ChatGPT-3.5 had fewer unreliable responses compared to Google Bard (n=3, 7% vs n=8, 20%).

Principal Findings

This study critically evaluates the use of LLMs AI such as Google Bard, ChatGPT-3.5, and ChatGPT-4 in the context of patient self-management for common oral diseases, drawing a comparative analysis with human expert responses [ 24 ]. Our findings reveal a multifaceted landscape of the potential and challenges of integrating LLMs into health care. The results underscore a promising future for AI chatbots to assist clinical workflows by augmenting patient education and patient-clinician communication around common oral disease queries with comparable accuracy, harmfulness, and comprehensiveness to human experts. However, they also highlight existing challenges that necessitate ongoing optimization strategies since even the most capable models have some inaccuracy and inconsistency.

Comparison to Prior Work

In the comprehensive evaluation of the 3 LLMs, ChatGPT-4 emerged as the superior model, consistent with prior assessments in various medical domains [ 10 , 25 , 26 ]. This superior performance is likely attributable to its substantially larger training data set, continuous architectural enhancements, and notable advancements in language processing, contextual comprehension, and advanced reasoning skills [ 20 ]. These improvements are crucial in health care applications, where the precision and relevance of information are critical. Interestingly, despite ChatGPT-4 showing greater stability, no significant differences were observed between ChatGPT-4 and ChatGPT-3.5 in dentist and patient evaluations. Given that ChatGPT-4 is a premium version not universally accessible, ChatGPT-3.5 holds significant value for broader applications.

In assessments spanning both periodontal and implant-related issues as well as a range of medical domains, Google Bard consistently demonstrated the least effective performance in addressing basic oral disease queries, particularly within the “diagnosis” domain. Notably, Google Bard’s tendency to avoid questions about dental implant surgery pain, in contrast to ChatGPT’s consistent responsiveness, might reflect differing strategies in risk management. However, in terms of readability, an important criterion for nonmedical users’ educational materials, Google Bard outperformed even human experts. This aligns with prior studies assessing LLMs’ readability and agrees with the impact of different training data and preprocessing methods on LLMs’ readability [ 27 , 28 ].

Future Directions

Moreover, all 3 LLM chatbots performed similarly in providing harmless responses. In the context of medical conversation, these AI models consistently encouraged patients to seek professional medical advice, underscoring the irreplaceable role of human expertise diagnosis and treatment. However, the results of the lay user evaluation warrant caution, as they show that AI models were comparable to human experts in intent capture and helpfulness. This ambiguous distinction poses a paradox. On one hand, it suggests user acceptance in AI-provided information, underscoring their capability to effectively address user inquiries. On the other hand, it discreetly underscores a potential risk: the lay users’ limited ability to judge the accuracy of complex medical information, which might inadvertently lead to AI disseminating misconceptions or inappropriate guidance. This underscores the critical need to address the ethical consideration of integrating AI in health care [ 29 , 30 ]. It is essential to clearly define the responsibilities and risks associated with using AI in patient education and in facilitating patient-clinician communication.

The observed performance differences among the AI models, influenced by factors like diverse training data sets and algorithmic updates, combined with the lay evaluations, emphasize the importance of customizing and continually updating LLMs for oral health care. Tailoring AI to meet specific oral health needs and maintaining current medical standards are crucial to ensure safe and accurate patient support.

Strengths and Limitations

LLMs demonstrate varied performances across different medical fields, which can be attributed to the varying depth of available web-based data on each topic. It is imperative to thoroughly evaluate their efficacy across diverse medical topics. In comparison to systemic diseases, using LLMs for basic oral health conditions offers substantial benefits. First, the narrower scope of oral diseases renders personalized oral hygiene advice and disease risk prediction via AI more viable. Second, the relative simplicity of oral structures, combined with AI’s advanced image recognition capabilities, facilitates the more feasible identification and analysis of oral imagery, thus aiding early-stage problem detection. This research underscores the potential of using AI to provide individualized oral health guidance to patients, which could significantly broaden their access to medical knowledge, reduce health care expenses, enhance medical efficiency, lower public health costs, balance medical resource distribution, and relieve national economic burdens.

To our knowledge, this is the first study to evaluate the application of current LLMs comprehensively and rigorously in basic oral diseases. The robust experimental design and the implementation of blinding largely reduce evaluator bias, ensuring the validity of the results. However, this study is not without limitations. First, its methodology, based on simulated question-and-answer scenarios, does not fully replicate real-world clinical interactions. Future research should involve actual patient interactions for more accurate assessment. Second, the performance of the LLM largely depends on the quality of the prompt guiding the model, highlighting the necessity for further research in this area. With the currently rapid evolution of LLMs, there is a critical need to develop specialized chatbots with medical expertise, combining the strengths of current LLMs for health care applications. Currently, integrating medical professionals seems to be the most effective strategy for optimizing AI applications in health care.

Conclusions

LLMs, particularly ChatGPT-4, demonstrate promising potential in providing patient-centric information for common oral diseases. Variations in performance underscore the need for ongoing refinement and ethical considerations. Future studies should explore strategies to integrate LLMs effectively in health care settings, ensuring their safe and effective use in patient care.

Conflicts of Interest

None declared.

Question list.

Evaluation axes.

Subanalysis results of periodontal and implant-related queries.

Subanalysis results of 6 medical care domains.

• ChatGPT. OpenAI. URL: https://chat.openai.com/ [accessed 2024-04-22]
• Schulman J, Zoph B, Kim C. ChatGPT: optimizing language models for dialogue. OpenAI blog. 2022. URL: https://chatgpt.r4wand.eu.org/# [accessed 2022-04-12]
• Zhao WX, Zhou K, Li J, Tang T, Wang X, Hou Y, et al. A survey of large language models. Preprint posted online on November 24, 2023. [ FREE Full text ]
• Radford A, Narasimhan K, Salimans T, Sutskever I. Improving language understanding by generative pre-training. Amazon AWS. URL: https://www.mikecaptain.com/resources/pdf/GPT-1.pdf
• Haupt CE, Marks M. AI-generated medical advice-GPT and beyond. JAMA. 2023;329(16):1349-1350. [ CrossRef ] [ Medline ]
• Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, Elepaño C, et al. Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. PLOS Digit Health. 2023;2(2):e0000198. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Sarraju A, Bruemmer D, Van Iterson E, Cho L, Rodriguez F, Laffin L. Appropriateness of cardiovascular disease prevention recommendations obtained from a popular online chat-based artificial intelligence model. JAMA. 2023;329(10):842-844. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Haver HL, Ambinder EB, Bahl M, Oluyemi ET, Jeudy J, Yi PH. Appropriateness of breast cancer prevention and screening recommendations provided by ChatGPT. Radiology. 2023;307(4):e230424. [ CrossRef ] [ Medline ]
• Rahsepar AA, Tavakoli N, Kim GHJ, Hassani C, Abtin F, Bedayat A. How AI responds to common lung cancer questions: ChatGPT vs Google Bard. Radiology. 2023;307(5):e230922. [ CrossRef ] [ Medline ]
• Lim ZW, Pushpanathan K, Yew SME, Lai Y, Sun CH, Lam JSH, et al. Benchmarking large language models' performances for myopia care: a comparative analysis of ChatGPT-3.5, ChatGPT-4.0, and Google Bard. EBioMedicine. 2023;95:104770. [ FREE Full text ] [ CrossRef ] [ Medline ]
• GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1789-1858. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Peres MA, Macpherson LMD, Weyant RJ, Daly B, Venturelli R, Mathur MR, et al. Oral diseases: a global public health challenge. Lancet. 2019;394(10194):249-260. [ CrossRef ] [ Medline ]
• Global periodontal health: challenges, priorities and perspectives. FDI World Dental Federation. URL: https://www.fdiworlddental.org/global-periodontal-health-challenges-priorities-and-perspectives [accessed 2024-04-02]
• White Paper on Prevention and Management of Periodontal Diseases for Oral Health and General Health. FDI World Dental Federation. URL: https:/​/www.​fdiworlddental.org/​resource/​white-paper-prevention-and-management-periodontal-diseases-oral-health-and-general-health [accessed 2024-04-02]
• Watt RG, Daly B, Allison P, Macpherson LMD, Venturelli R, Listl S, et al. Ending the neglect of global oral health: time for radical action. Lancet. 2019;394(10194):261-272. [ CrossRef ] [ Medline ]
• Tonetti MS, Jepsen S, Jin L, Otomo-Corgel J. Impact of the global burden of periodontal diseases on health, nutrition and wellbeing of mankind: a call for global action. J Clin Periodontol. 2017;44(5):456-462. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Fatani B. ChatGPT for future medical and dental research. Cureus. 2023;15(4):e37285. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Eggmann F, Weiger R, Zitzmann NU, Blatz MB. Implications of large language models such as ChatGPT for dental medicine. J Esthet Restor Dent. 2023;35(7):1098-1102. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kuehn BM. More than one-third of US individuals use the internet to self-diagnose. JAMA. 2013;309(8):756-757. [ CrossRef ] [ Medline ]
• Lee P, Bubeck S, Petro J. Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine. N Engl J Med. 2023;388(13):1233-1239. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Gemini. Google. URL: https://gemini.google.com
• Caton JG, Armitage G, Berglundh T, Chapple ILC, Jepsen S, Kornman KS, et al. A new classification scheme for periodontal and peri-implant diseases and conditions—introduction and key changes from the 1999 classification. J Clin Periodontol. 2018;45(Suppl 20):S1-S8. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Gjersing L, Caplehorn JRM, Clausen T. Cross-cultural adaptation of research instruments: language, setting, time and statistical considerations. BMC Med Res Methodol. 2010;10:13. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Perlis RH, Fihn SD. Evaluating the application of large language models in clinical research contexts. JAMA Netw Open. 2023;6(10):e2335924. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Ali R, Tang OY, Connolly ID, Fridley JS, Shin JH, Sullivan PLZ, et al. Performance of ChatGPT, GPT-4, and Google Bard on a neurosurgery oral boards preparation question bank. Neurosurgery. 2023;93(5):1090-1098. [ CrossRef ] [ Medline ]
• Holmes J, Liu Z, Zhang L, Ding Y, Sio TT, McGee LA, et al. Evaluating large language models on a highly-specialized topic, radiation oncology physics. Front Oncol. 2023;13:1219326. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Seth I, Lim B, Xie Y, Cevik J, Rozen WM, Ross RJ, et al. Comparing the efficacy of large language models ChatGPT, BARD, and Bing AI in providing information on rhinoplasty: an observational study. Aesthet Surg J Open Forum. 2023;5:ojad084. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Zhu L, Mou W, Chen R. Can the ChatGPT and other large language models with internet-connected database solve the questions and concerns of patient with prostate cancer and help democratize medical knowledge? J Transl Med. 2023;21(1):269. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Jobin A, Ienca M, Vayena E. The global landscape of AI ethics guidelines. Nat Mach Intell. 2019;1(9):389-399. [ CrossRef ]
• Price WN, Cohen IG. Privacy in the age of medical big data. Nat Med. 2019;25(1):37-43. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by G Eysenbach, T de Azevedo Cardoso; submitted 27.12.23; peer-reviewed by L Weinert, L Zhu, W Cao; comments to author 26.02.24; revised version received 04.03.24; accepted 19.03.24; published 25.04.24.

©Xiaolei Lv, Xiaomeng Zhang, Yuan Li, Xinxin Ding, Hongchang Lai, Junyu Shi. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Partisan divides over K-12 education in 8 charts

K-12 education is shaping up to be a key issue in the 2024 election cycle. Several prominent Republican leaders, including GOP presidential candidates, have sought to limit discussion of gender identity and race in schools , while the Biden administration has called for expanded protections for transgender students . The coronavirus pandemic also brought out partisan divides on many issues related to K-12 schools .

Today, the public is sharply divided along partisan lines on topics ranging from what should be taught in schools to how much influence parents should have over the curriculum. Here are eight charts that highlight partisan differences over K-12 education, based on recent surveys by Pew Research Center and external data.

Pew Research Center conducted this analysis to provide a snapshot of partisan divides in K-12 education in the run-up to the 2024 election. The analysis is based on data from various Center surveys and analyses conducted from 2021 to 2023, as well as survey data from Education Next, a research journal about education policy. Links to the methodology and questions for each survey or analysis can be found in the text of this analysis.

Most Democrats say K-12 schools are having a positive effect on the country , but a majority of Republicans say schools are having a negative effect, according to a Pew Research Center survey from October 2022. About seven-in-ten Democrats and Democratic-leaning independents (72%) said K-12 public schools were having a positive effect on the way things were going in the United States. About six-in-ten Republicans and GOP leaners (61%) said K-12 schools were having a negative effect.

About six-in-ten Democrats (62%) have a favorable opinion of the U.S. Department of Education , while a similar share of Republicans (65%) see it negatively, according to a March 2023 survey by the Center. Democrats and Republicans were more divided over the Department of Education than most of the other 15 federal departments and agencies the Center asked about.

In May 2023, after the survey was conducted, Republican lawmakers scrutinized the Department of Education’s priorities during a House Committee on Education and the Workforce hearing. The lawmakers pressed U.S. Secretary of Education Miguel Cardona on topics including transgender students’ participation in sports and how race-related concepts are taught in schools, while Democratic lawmakers focused on school shootings.

Partisan opinions of K-12 principals have become more divided. In a December 2021 Center survey, about three-quarters of Democrats (76%) expressed a great deal or fair amount of confidence in K-12 principals to act in the best interests of the public. A much smaller share of Republicans (52%) said the same. And nearly half of Republicans (47%) had not too much or no confidence at all in principals, compared with about a quarter of Democrats (24%).

This divide grew between April 2020 and December 2021. While confidence in K-12 principals declined significantly among people in both parties during that span, it fell by 27 percentage points among Republicans, compared with an 11-point decline among Democrats.

Democrats are much more likely than Republicans to say teachers’ unions are having a positive effect on schools. In a May 2022 survey by Education Next , 60% of Democrats said this, compared with 22% of Republicans. Meanwhile, 53% of Republicans and 17% of Democrats said that teachers’ unions were having a negative effect on schools. (In this survey, too, Democrats and Republicans include independents who lean toward each party.)

The 38-point difference between Democrats and Republicans on this question was the widest since Education Next first asked it in 2013. However, the gap has exceeded 30 points in four of the last five years for which data is available.

Republican and Democratic parents differ over how much influence they think governments, school boards and others should have on what K-12 schools teach. About half of Republican parents of K-12 students (52%) said in a fall 2022 Center survey that the federal government has too much influence on what their local public schools are teaching, compared with two-in-ten Democratic parents. Republican K-12 parents were also significantly more likely than their Democratic counterparts to say their state government (41% vs. 28%) and their local school board (30% vs. 17%) have too much influence.

On the other hand, more than four-in-ten Republican parents (44%) said parents themselves don’t have enough influence on what their local K-12 schools teach, compared with roughly a quarter of Democratic parents (23%). A larger share of Democratic parents – about a third (35%) – said teachers don’t have enough influence on what their local schools teach, compared with a quarter of Republican parents who held this view.

Republican and Democratic parents don’t agree on what their children should learn in school about certain topics. Take slavery, for example: While about nine-in-ten parents of K-12 students overall agreed in the fall 2022 survey that their children should learn about it in school, they differed by party over the specifics. About two-thirds of Republican K-12 parents said they would prefer that their children learn that slavery is part of American history but does not affect the position of Black people in American society today. On the other hand, 70% of Democratic parents said they would prefer for their children to learn that the legacy of slavery still affects the position of Black people in American society today.

Parents are also divided along partisan lines on the topics of gender identity, sex education and America’s position relative to other countries. Notably, 46% of Republican K-12 parents said their children should not learn about gender identity at all in school, compared with 28% of Democratic parents. Those shares were much larger than the shares of Republican and Democratic parents who said that their children should not learn about the other two topics in school.

Many Republican parents see a place for religion in public schools , whereas a majority of Democratic parents do not. About six-in-ten Republican parents of K-12 students (59%) said in the same survey that public school teachers should be allowed to lead students in Christian prayers, including 29% who said this should be the case even if prayers from other religions are not offered. In contrast, 63% of Democratic parents said that public school teachers should not be allowed to lead students in any type of prayers.

In June 2022, before the Center conducted the survey, the Supreme Court ruled in favor of a football coach at a public high school who had prayed with players at midfield after games. More recently, Texas lawmakers introduced several bills in the 2023 legislative session that would expand the role of religion in K-12 public schools in the state. Those proposals included a bill that would require the Ten Commandments to be displayed in every classroom, a bill that would allow schools to replace guidance counselors with chaplains, and a bill that would allow districts to mandate time during the school day for staff and students to pray and study religious materials.

Mentions of diversity, social-emotional learning and related topics in school mission statements are more common in Democratic areas than in Republican areas. K-12 mission statements from public schools in areas where the majority of residents voted Democratic in the 2020 general election are at least twice as likely as those in Republican-voting areas to include the words “diversity,” “equity” or “inclusion,” according to an April 2023 Pew Research Center analysis .

Also, about a third of mission statements in Democratic-voting areas (34%) use the word “social,” compared with a quarter of those in Republican-voting areas, and a similar gap exists for the word “emotional.” Like diversity, equity and inclusion, social-emotional learning is a contentious issue between Democrats and Republicans, even though most K-12 parents think it’s important for their children’s schools to teach these skills . Supporters argue that social-emotional learning helps address mental health needs and student well-being, but some critics consider it emotional manipulation and want it banned.

In contrast, there are broad similarities in school mission statements outside of these hot-button topics. Similar shares of mission statements in Democratic and Republican areas mention students’ future readiness, parent and community involvement, and providing a safe and healthy educational environment for students.

• Education & Politics
• Partisanship & Issues
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Jenn Hatfield is a writer/editor at Pew Research Center

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