Undergraduate medical students’ and teachers’ perspectives on ethical challenges and coping strategies of using generative artificial intelligence for academic assignments: A qualitative study

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Given that the quality of medical education directly shapes students’ professional competencies, exploring both medical students’ and teachers’ perspectives on GenAI use for academic assignments carries significant implications for medical education. Objective To explore medical students’ and teachers’ perspectives on ethical challenges and coping strategies of using GenAI for academic assignments. Methods This study employed a descriptive phenomenological approach using semi-structured in-depth interviews. Purposive sampling was used to recruit undergraduate medical students and their teachers from one medical university between January and April 2025 in Guangzhou, China. Data were analyzed through Colaizzi’s phenomenological method, supplemented by deductive analysis guided by the Responsible Innovation framework to identify key themes. Results A total of 19 participants were interviewed, including 11 undergraduate medical students and 8 teachers. The participants expressed a consensus on the ethical challenges arising from the use of GenAI for academic assignments. Following a thematic analysis, three themes were identified: (1) Subversion posed by GenAI, (2) Limitations and potential risks of using GenAI, and (3) Coping strategies in response to utilisation of GenAI. Conclusions The integration of GenAI in education has raised significant academic and ethical concerns. However, existing regulatory policies and student evaluation mechanisms have yet to adapt to the ethical challenges posed by GenAI. Therefore, strategies such as implementing academic integrity policies for GenAI-assisted assignments, establishing transparent oversight mechanisms, and promoting GenAI literacy education should be adopted to address these issues. Generative artificial intelligence education ethical challenges academic integrity qualitative research Figures Figure 1 Background Generative artificial intelligence (GenAI) has advanced remarkably in recent years, with applications expanding into domains like education and healthcare [ 1 ]. It has the capability to simulate human-like dialogues, analyse and interpret conversational data, and generate a wide array of content through deep learning models [ 2 , 3 ]. In the educational field, GenAI tools such as ChatGPT, DeepSeek, Kimi, GitHub Copilot, and Scribe to Code have demonstrated significant potential, driving profound transformations in educational philosophies, teaching methodologies and learning environments [ 4 , 5 ]. A growing body of research shows that GenAI can enhance teaching efficiency and improve student learning outcomes in medical education [ 6 , 7 ]. Medical education, as a domain depending on perpetual knowledge renewal and hands-on practice, seeks to develop medical students’ competence in managing complex patient conditions [ 8 ]. This objective demands not only a solid foundation in medical knowledge but also the development of clinical thinking, communication skills, and problem-solving abilities [ 9 ]. GenAI has provided support for achieving this goal by enhancing medical students’ capabilities in medical writing, case analysis, and diagnostic reasoning [ 10 ]. While GenAI can improve academic performance to some extent, the negative impacts, such as posing a threat to comprehensive competency development, have also emerged [ 11 – 13 ]. The utilisation of GenAI for academic assignments may cause cognitive passivity and dependency as students increasingly rely on these tools [ 14 ]. Excessive dependence on these technologies not only restricts students’ engagement in critical thinking but also undermines their academic growth and clinical decision-making capabilities [ 15 ]. Additionally, the immediate access to knowledge facilitated by GenAI has diminished the students’ sense of delayed gratification, which may potentially impact their long-term motivation to learn and their capacity for self-discipline [ 14 ]. Moreover, the misuse of GenAI poses significant challenges to academic integrity systems [ 16 ]. The phenomenon of students using GenAI to complete their academic assignments undermines the reliability of traditional student evaluation methods and raises concerns about academic integrity and originality [ 17 ]. Existing scholarly norms struggle to reliably distinguish AI-generated content from human-authored work, resulting in significant difficulties in determining misconduct cases [ 18 ]. It is worth noting that current universities fail to provide guidelines specifically addressing GenAI utilisation in student work [ 19 ]. The absence of a proper GenAI academic regulatory leads to distorted academic assessments that fail to accurately measure either learning outcomes or scholarly effort, thereby compromising the fairness and reliability of evaluation systems and ultimately undermining educational quality [ 20 ]. The quality of medical education directly impacts students’ professional competencies, thereby influencing the overall quality of healthcare services [ 21 ]. As such, teachers have come to increasingly recognize the importance of guiding students in the rational and ethical use of GenAI tools [ 22 ]. While GenAI demonstrates unique advantages in medical education, further exploration is needed to establish rational and standardized guidelines for its appropriate use [ 23 ]. Notably, studies remain scarce regarding medical students’ and teachers’ perspectives on GenAI, and the ethical dilemmas students face when using it. Given that the quality of medical education directly shapes students’ professional competencies, it is critical to address these emerging challenges. Therefore, a qualitative study is employed to explore their perspectives, seeking to establish a theoretical foundation and inform robust educational policies for the judicious application of GenAI in medical education, ultimately contributing to advancing the field. A review of literature indicated that the Responsible Innovation framework could offer a valuable lens for examining medical students’ and teachers’ perspectives related to the use of GenAI in academic assignments [ 24 ]. The framework was initially proposed to raise, discuss, and address issues related to the ethical acceptability, sustainability, and societal appropriateness of emerging technologies when they are applied in our society. Exploring these questions provides insightful guidance for governance, enabling the more appropriate integration of the technologies into society. The framework comprises four dimensions [ 24 ], including (1) anticipation, referring to proactively assessing potential ethical and societal impacts of emerging technologies, (2) reflexivity, which encourages stakeholders to critically evaluate their roles and assumptions in technology adoption, (3) inclusion, involving the engagement of diverse perspectives (e.g., students, teachers) in dialogues about technology use, and (4) responsiveness, which involves responding to feedback and constructing strategic policies and technological standards. Over the past decade, this framework has been widely applied in technology governance and ethics research to balance innovation with accountability and adapted for educational contexts [ 25 , 26 ]. Based on the high applicability of this framework to new technologies, we have applied it to explore students’ and teachers’ perspectives on the ethical challenges and coping strategies in GenAI-assisted assignments. Methods Study design A descriptive qualitative study, based on the Consolidated Criteria for Reporting Qualitative Studies (COREQ) guideline [ 27 ], was conducted among undergraduate medical students and their teachers from a government-funded medical university in Guangzhou, China between January and April 2025. Study participants Participants in this study were recruited using purposive maximum variation sampling to ensure representation across gender, age, grade, and major for students, and gender, age, teaching specialisation, and professional title for teachers [ 28 ]. The inclusion criteria for students were (1) being undergraduate medical students and (2) having prior experience in using GenAI. The inclusion criteria for teachers were (1) having prior experience in using GenAI and (2) currently teaching the undergraduate medical courses. The exclusion criteria for both were being on sick or maternity leave for over one month in the past three months before joining the study. The sample size was determined by data saturation [ 29 ]. Data collection ceased when the research team observed no emergence of new codes during three consecutive interviews, indicating stabilized data patterns. Data collection The Responsible Innovation framework, which originates from key questions emerging in public debates about new technological areas, emphasizes aligning technological development with ethical principles, societal values, and accountability. These questions draw on analysis of cross-cutting public concerns from 17 UK public dialogues on science and technology, categorizing them based on their relation to the products, processes, or purposes of innovation. Building upon this theoretical foundation, this study adopted the core questions from these questions as the basis for designing the interview guide. Following the literature review, the research team discussed the core objectives of the study and subsequently developed the interview guide draft. A pilot study was conducted with two medical students and two teachers to refine the interview guide. The final interview guide is provided in Table 1 . Table 1 Interview guide based on Responsible Innovation. Participants Product questions Process questions Purpose questions Students 1. What impacts do you think the use of GenAI in academic assignments has on you? 2. Do you think that students using GenAI to assist with academic assignments constitutes cheating? Why? 3. Could you describe your process when using GenAI for assignments? 4. When evaluating assignments, if some students submit work completed with GenAI assistance while others submit entirely independent work, how should they be graded? Should adjustments be made to the grading criteria? Why? 5. What strategies do you think would help you use GenAI appropriately? 6. Have you ever used GenAI to assist with your academic assignments? If so, what kinds of GenAI tools have you used? And for what types of assignments have you used GenAI? 7. What are your perspectives on using GenAI to complete assignments? Teachers 1. What impacts do you think students' use of GenAI in academic assignments has on them? 2. Do you think that students using GenAI to assist with academic assignments constitutes cheating? Why? 3. Are you able to determine whether students completed their assignments independently or with the assistance of GenAI? Why? 4. When evaluating assignments, if some students submit work completed with GenAI assistance while others submit entirely independent work, how should they be graded? Should adjustments be made to the grading criteria? Why? 5. What strategies do you think would help students use GenAI appropriately? 6. Have you ever used GenAI? If so, what kinds of GenAI tools have you used? 7. What are your perspectives on students' use of GenAI in academic assignments? Data were collected by two interviewers (G.A. and W.T.X.), both trained in qualitative research methods at the university. After obtaining participants’ consent, one-to-one face-to-face interviews were conducted in Mandarin, each lasting 30 to 45 minutes. In addition, the facial expressions and body postures of the participants were recorded in the field notes. Interviews took place in quiet locations according to participants’ preferences, including dormitories, classrooms, and offices at the university. All interviews were audio-recorded with participants’ consent. Data analysis The audio recordings were imported into NVivo 12 for analysis by the two interviewers (W.T.X. and G.A.). Within 24 hours after each interview, one interviewer transcribed the recordings verbatim while the other cross-checked the transcripts against the audio for accuracy. A mixed analytical approach was adopted for data analysis, where semantic units were first extracted following Colaizzi’s phenomenological steps and then deductively categorized using the four dimensions of the Responsible Innovation framework [ 30 ]. To ensure study credibility, researcher triangulation was implemented during data analysis, with an additional researcher K.L.L. replacing G.A. in the analytical process. Firstly, W.T.X. and K.L.L. independently and iteratively reviewed all interview transcripts to establish a foundational understanding before extracting semantic units. Subsequently, they developed descriptive codes based on these units. Then, a deductive analysis was applied to map descriptive codes to the four dimensions (anticipation, reflexivity, inclusion, responsiveness) of the Responsible Innovation framework, with openness to emergent themes. Through repeatedly extracting, analysing, iterative comparison, and grouping, codes were refined into conceptually distinct clusters that subsequently informed themes and categories. Finally, all codes and categories were grouped under the themes based on the four dimensions, using a thematic mapping technique. There was a movement back and forth between the raw data, codes, and initial themes to ensure coherence between the codes. Disagreements in codes, categories, and themes were resolved by discussion of the entire research group. Ultimately, the generated thematic structure was returned to the participants for verification, asking whether it captured their true perspectives to ensure the accuracy of the results. The corresponding author (D.L.L.), with extensive teaching and qualitative research experience in the field of medical education, ensured that the data were thoroughly covered and the themes were accurately represented. Ethical considerations The study protocol was approved by the Ethics Committee of The Second Affiliated Hospital, Guangzhou Medical University (LYZX-2025-044-01). Before data collection, all participants received detailed study information (purpose, methodology, and right to withdraw) and provided written informed consent. During the interview process, no participants dropped out. To ensure confidentiality, all participant information was anonymized by assigning random numbering based on interview sequence, de-identified, encrypted, and stored with restricted access under the first author’s supervision. Study Rigor The study’s rigor was enhanced through the application of Lincoln and Guba’s criteria[ 31 ]. To ensure credibility, the first author had pre-existing relationships with participants, facilitating good rapport. The research team also employed methodological triangulation to maintain analytical consistency and objectivity. Specifically, data collection was conducted by G.A. and W.T.X., while data analysis was performed independently by W.T.X. and K.L.L. Teacher and student interview data were triangulated to validate central themes. Furthermore, the study meticulously documented participants’ demographic characteristics to enable readers to assess the potential transferability of findings to similar educational contexts. Moreover, for confirmability, the research team engaged in detailed discussions and preparations prior to the interviews to ensure the neutrality of the interview questions. All interviewers (G.A. and W.T.X.) received systematic training to maintain impartiality during interviews. Regular team meetings were held to reflect on potential biases arising from researchers’ backgrounds. For instance, the team explicitly discussed W.T.X.’s and K.L.L.’s views on “students using GenAI to assist with assignments”. They did not view it as either positive or negative but rather genuinely wanted to better understand the phenomenon. Lastly, to maintain dependability, all interview recordings, manuscripts, notes, and coding documentation were systematically archived for independent audit. Results A total of 11 medical students and 8 teachers participated in the study. The details regarding the characteristics of the students and teachers are shown in Table 2 and Table 3 , respectively. Guided by the Responsible Innovation framework, a thematic map was developed and is shown in Fig. 1 . The thematic map illustrates the relationships among the themes, categories, and codes that describe the ethical challenges and coping strategies that students encounter when using GenAI for academic assignments. Table 2 The characteristics of student participants. Student Gender Age Grade Major S1 M 22 Fourth year Clinical Medicine S2 F 22 Fourth year Nursing S3 F 22 Fourth year Clinical Medicine S4 F 22 Fourth year Nursing S5 M 19 First year Integrated Chinese-Western Medicine S6 F 20 Second year Dentistry S7 F 21 Second year Nursing S8 F 21 Third year Clinical Medicine S9 F 21 Third year Psychology S10 M 21 Third year Nursing S11 F 19 First year Anaesthesiology Table 3 The characteristics of teacher participants. Teacher Gender Age Teaching Specialization Professional Title T1 F 35 Clinical Medicine Associate Professor T2 F 40 Anaesthesiology Lecturer T3 F 36 Clinical Medicine Lecturer T4 M 48 Dentistry Professor T5 F 39 Integrated Chinese-Western Medicine Associate Professor T6 M 45 Psychology Associate Professor T7 F 43 Nursing Associate Professor T8 F 31 Nursing Lecturer Theme 1: Subversion posed by GenAI This theme focuses on the effects of GenAI on teachers’ and students’ motivation shift and technology adaptation in medical education. Category 1: Shift in students ’ learning paradigm and motivation Some students acknowledged that while GenAI enhanced their learning efficiency, it has altered their traditional learning methods and pathways. This prompted them to rethink the significance of learning. As illustrated by a student in the semi-structured interview, “Initially, I was sceptical about GenAI ’ s capabilities. I think the traditional learning motivation comes when you explore things and solve problems by yourself. But with GenAI, it ’ s like having an instant helper that can answer questions tailored just for you. It ’ s really shifted from traditional learning methods.” (S1) Another student also said, “I sometimes ponder whether the memorization of knowledge is still necessary when GenAI can provide precise answers instantaneously. Has the essence of human learning shifted from "acquiring knowledge" to "mastering tools"? In this paradigm shift, how should I reorient my efforts? I am confused.” (S3) When GenAI could conveniently provide solutions, learners’ motivation for independent exploration and the sense of fulfilment gained through problem-solving were gradually diminishing. For example, in the semi-structured interviews, some students reported that they were more inclined to use GenAI to complete tasks quickly rather than invest time and effort in delving into problems. One student further illustrated as follow, “It’s especially noticeable in Problem-Based Learning (PBL) courses. We used to work through patient cases step by step, uncovering underlying health issues along the way. It was challenging, but there was a real sense of motivation in this process. Now, I will hand the case to GenAI and get solutions in seconds. However, as a medical student, I think consistently relying on immediate, pre-existing answers would gradually erode my intellectual curiosity and diminish my learning motivation.” (S1) Another student similarly said , “In the past, whenever I came up with a new idea through my own efforts or solved a difficult learning problem, I would feel a great sense of achievement. However, now that I see GenAI has stronger innovation capabilities and problem-solving skills than I do, this is so frustrating. I ’ m afraid I ’ ll miss out on the key elements that can truly help me become a good doctor.” (S8) Category 2: Anxiety triggered by GenAI GenAI threatened the students psychologically, with most reporting widespread anxiety over its adoption. One student further explained this in the semi-structured interview, “I used to take pride in my learning advantages, particularly my strength in linguistic logic. But now, seeing GenAI surpass my abilities in this aspect, I feel intense pressure and fear. I start to question whether I still need to develop this skill, and I don ’ t know whether I should learn anymore.” (S4) Another student also said, “I see my classmates using GenAI to complete their assignments. They finish their work faster and better than me. I feel stressed that I’ll fall behind if I don’t use it too, so I just go along with it.” (S9) Likewise, the teachers also reported concerns to use GenAI in medical training as they thought that the superficial benefits to efficiency degraded authentic learning. As indicated by a teacher in the semi-structured interview, “ When using GenAI, students perceive an enhancement in their learning efficiency. However, this perception may be somewhat misconceived. When students bypass the critical components of the learning process, such as reading textbooks, seeking guidance from instructors, and engaging in practical exercises, they will be inadequately prepared for the complex clinical practice. Upon entering the professional arena and confronting intricate and dynamic clinical challenges, they may lack the requisite problem-solving capabilities. Therefore, I deeply worry in the era of GenAI.” (T4) Category 3: Reconceptualisation of teacher-student relationships and roles GenAI technological adaptation presented a dual challenge for teachers and students, requiring both relational restructuring and role reconceptualisation. Students pointed out that the efficiency and convenience of GenAI have transformed their role from mere recipients of knowledge to users of knowledge, and the relationship between teachers and students has also changed. This was further illustrated by a student in the interview, “In the past, we mainly listened to the teacher ’ s lectures, and the teacher was the one in charge. But now with GenAI, the teacher ’ s role seems to have changed. It ’ s no longer that traditional "I talk, you listen" pattern. This change feels pretty cool to me. I feel that I’ve transitioned from a passive receiver to an active user of knowledge. However, I really hope that teachers can also sense this change and guide us on how to use these new technologies well. And we can build a new kind of interactive relationship between teachers and students. (S5) Meanwhile, teachers have also been reflecting on their shifting roles and attempting to adjust their teaching approaches. And they expressed a cautious and limited endorsement of students’ use of GenAI. One teacher expressed in the semi-structured interview, “Students seem to be seeking help from teachers less frequently than before. This situation often confuses me and even leads me to question whether my role as a teacher is still necessary. Faced with such changes, I have been thinking about what adjustments my teaching role needs to make.” (T2) Another teacher also said , “Although I can see some benefits that GenAI brings to students, I am also keenly aware that there are currently no clear regulations for GenAI, which may give rise to many academic ethical issues. Therefore, I am cautious about students ’ use of GenAI. I need to help them keep an eye on things and guide them in using these tools correctly to prevent them into the academic ethical dilemma.” (T5) In a more profound reflection, one teacher emphasized that only by thoroughly mastering and effectively utilising GenAI, teachers could more accurately assess students’ learning trajectories and outcomes. As stated by the teacher in the interview, “If we as teachers aren ’ t familiar with GenAI, we might not even realize, let alone identify, which content is AI-generated. This may result in students utilising GenAI for assignments achieving higher grades than their non-AI-assisted peers. Interestingly, my observations reveal that teachers who frequently employ GenAI can readily discern AI-generated work, whereas those less accustomed to such tools often fail to detect it. After all, as teachers, we have to guide our students to use these new technologies properly, rather than being replaced by them. Precisely for this reason, the current environment compels us to learn and master these technologies. This is not just a challenge but also a necessary process of adaptation. (T2) Theme 2: Limitations and potential risks of using GenAI In assessing GenAI’s impact on assignments, participants identified risks of over-reliance, and they held polarized views on its implications for academic integrity. Category 1: Students’ over-reliance on GenAI Some participants stated that over-reliance of GenAI might lead students to have difficulty in completing assignments without the assistance of GenAI. One student further illustrated in the semi-structured interview, “Indeed, I have realized that my reliance on AI has increased. Initially, I merely utilised GenAI to refine the expression in my reflective journals. However, I now find myself unable to articulate even a single sentence without its assistance.” (S7) When students have used GenAI to complete assignments and gradually developed a dependency habit, they reported that they might uncritically accept the AI-generated content. In the semi-structured interview, one student said, “We all know that AI-generated treatment plans are standardized and don ’ t take into account individual patient differences. For example, during a case discussion, the patient was clearly allergic to penicillin, but I was too reliant on GenAI at the time. I didn ’ t even think about whether the answer given by GenAI was right or wrong. I just copied its treatment plan directly, ignored the patient ’ s allergy history, and ended up choosing a penicillin-based drug. (S3) Category 2: Threats to academic integrity posed by GenAI Both teachers and students mentioned the effects of using GenAI for assignments on academic integrity. Since the extent to which students rely on GenAI to complete assignments varies significantly, and these differences contributed to divergent views among them regarding whether such use constituted cheating. Some participants maintained that utilising GenAI for assignments constituted academic dishonesty, even when students paraphrased the AI-generated content. As illustrated by one student, “I consider this (using GenAI) is cheating. Submitting AI-generated content is equivalent to having someone or a machine do the work for you. Even if you reword it, it ’ s still cheating because it breaks the rules of academic honesty.” (S6) Another student also said , Undoubtedly, this is cheating. I ’ ve noticed some classmates directly using GenAI to generate content for their assignments without critical thinking. Actually, sometimes GenAI tends to fabricate references. It poses a significant challenge to academic integrity. (T3) Nevertheless, some participants argued that GenAI merely provided students with ideas and frameworks for assignment assistance, maintaining that such usage constituted legitimate academic support rather than cheating. As illustrated by one student, “I don ’ t think it counts as cheating because when I complete my assignments, I only draw on some ideas rather than copying everything verbatim.” (S8) Another participant also said , “I don ’ t think this should be considered cheating because using GenAI to complete assignments demonstrates students’ digital literacy. If students can achieve better results by using GenAI, then its application should be permitted.” (T1) Theme 3: Coping strategies in response to the utilisation of GenAI Category 1: Academic integrity management of GenAI usage Both teachers and students have expressed their perspectives on the management of academic integrity. They proposed the need to establish clear boundaries for the use of GenAI in academic assignments to maintain integrity and fairness. In the interview, one student said, I believe it ’ s essential to establish clear academic integrity standards regarding students ’ use of GenAI. If students who rely on AI can still earn higher grades, then what motivation do I have to put in the effort? It will ruin the whole value of learning.” (S6) One teacher similarly said , “I think GenAI can really help students learn a lot if being used properly, but the key is to know the limits. There needs to be a clear boundary, or students would easily go off the track.” (T6) While clearly defining the applicable boundaries, another teacher emphasized that it is equally important to require students to label the content generated by AI. As illustrated by that teacher, “Students need to inform me that they have used an AI tool. If you fail to disclose its use and I discover it, that would lead to a different form of plagiarism. We need to set up a proper penalty system for this.” (T5) Additionally, some teachers have suggested that establishing oversight mechanisms was also a necessary intervention for managing academic integrity. One teacher said, “I believe that academic integrity partly relies on students ’ self-discipline, but it also requires a supervisory mechanism to ensure compliance. The oversight mechanism can help us manage their overuse of GenAI for academic assignments.” (T3) Category 2: Reconstruction of student evaluation mechanisms Participants argued that optimizing student evaluation mechanisms could mitigate academic inequities caused by Gen AI. In the semi-structured interview, one teacher said, “Reverting to traditional student evaluation methods could be a good way to uphold educational integrity. I think replacing take-home essays with closed-book exams and incorporating in-class performance into final assessments could effectively reduce GenAI ’ s influence on grading outcomes.” (T7) One student similarly reported that , “I think teachers should grade us in more diverse ways. Maybe cutting down on assignments that can be done with GenAI would help.” (S2) Furthermore, both teachers and students suggested that implementing AI detection tools could help ensure appropriate GenAI use in academic assignments. This was supported by a student as follows, “I believe detection shouldn ’ t rely solely on teachers ’ judgment. It should integrate AI writing detection tools to identify AI-generated content.” (S2) Also, one student said , “I believe that plagiarism detection software can be employed to identify traces generated by AI, essentially using technology to counteract technology.” (T3) Category 3: Development of students’ GenAl competencies Participants proposed that universities should offer courses to help students improve their GenAI usage competency. They also emphasized that integrating academic integrity education was essential to maintain fairness and educational integrity. As explained by one teacher, “I think we should start teaching students how to work with GenAI the right way. Instead of just telling them not to use it, we could show them how to use tools like GenAI properly for their assignments.” (T1) Another teacher similarly said , “Academic integrity has always been at the heart of education. We should make students recognize its critical importance by integrating GenAI literacy and academic integrity courses for students.” (T6) Furthermore, one teacher proposed utilising GenAI as a dynamic interactive tool to transform traditional learning paradigms into human-AI collaborative frameworks. As illustrated by that teacher in the interview, “If students can learn to use GenAI properly by using it as a tool for inquiry, counter-questioning, and interactive dialogue, I honestly think GenAI could help them learn knowledge more efficiently. We need to learn how to use AI effectively. For instance, we can first conceptualize ideas and let AI help refine them.” (T6) Discussion We conducted in-depth interviews with 11 undergraduate medical students and 8 teachers to explore their perspectives on the ethical challenges and coping strategies of using GenAI for academic assignments. Guided by the Responsible Innovation framework, we identified three themes and eight categories, which focused on students’ and teachers’ perspectives on using GenAI for academic assignments. Their descriptions specifically elaborated on the shift of students’ learning paradigm and motivation, anxiety triggered by GenAI, reconceptualisation of teacher-student relationships and roles, over-reliance on GenAI, threats to academic integrity, and coping strategies for the usage of GenAI. The theoretical framework is based on the premise that emerging techonologies have not only generated understanding and knowledge, but also raised questions, dilemmas, and unintended consequences. In our study, students reported that GenAI, while reshaping traditional learning processes, might erode their intrinsic motivation to pursue in-depth knowledge. According to Bloom’s Taxonomy in educational theory, learning progresses from remembering and understanding to applying, analysing, evaluating, and creating [ 32 , 33 ]. Students derive satisfaction and intrinsic motivation by mastering each stage [ 34 ]. However, with GenAI, they can now reach the applying stage without necessarily going through the stages of remembering and understanding, which consequently weakens their learning motivation [ 23 ]. This shift in learning motivation may cause students to bypass crucial cognitive exploration during the learning process, thereby affecting the comprehensive development of their academic competencies. This perspective aligns with scholarly findings that when GenAI reduces essential learning experiences to formulaic assignments, students often cease to actively engage in knowledge exploration and deep comprehension [ 23 , 35 ]. Moreover, this study revealed a concern that over-reliance on GenAI might lead to superficial learning among students. This technological dependence may create an illusion of competence, masking students’ actual skill deficiencies [ 19 ]. Stirling et al. suggest that issues such as technological dependence and lock-in, may arise during the process of utilizing technology. Over-reliance on GenAI could lead students into technological lock-in, making it difficult for them to break free from AI dependence. Over time, students may develop the misconception that relying solely on such tools suffices for completing academic assignments. Over-reliance on GenAI could diminish learning motivation derived from students’ anxiety about knowledge gaps, which is a crucial motivational driver for the professional development of students. Ultimately, this may impede the professional growth of medical students [ 36 ]. This study also found that GenAI has challenged the teaching relationship between students and teachers, shifting from traditional, single-scenario, and teacher-dominated modes to hybrid and interactive learning modes. As indicated by both students and teachers in the semi-structured interviews, the conventional binary teaching structure of teacher-student has inevitably supplanted by a ternary structure of teacher-AI-student, thereby forging a novel paradigm in educational practice. Likewise, the application of GenAI was found to risk eroding the social and moral value of learning. Although both teachers and students expressed that they have continuously adapted and strived to better integrate into this novel pedagogical paradigm, a set of universal guidelines has not been established for addressing role transformation and responsibility allocation in technology integration. This qualitative finding aligns with Li’s research perspective [ 37 ]. There is a need for more anticipatory discussions on GenAI’s educational applications, so that teachers and students can proactively shape how GenAI integrates with education [ 38 ]. Teachers should persistently question and examine the educational values and potential risks behind the use of GenAI technology, ensuring the essence of education amid technological changes. Additionally, in the semi-structured interviews, students and teachers extensively discussed whether using GenAI for academic assignments would threaten academic integrity. Academic integrity establishes a foundation of fairness, and ethical responsibility in education, ensuring that academic achievements reflect genuine learning and effort [ 39 ]. The majority of students in this study believed that using GenAI for academic assignments would have a negative impact on academic integrity. The students’ concerns about GenAI’s threat to academic integrity align with broader debates about technology-mediated learning [ 40 ]. Their skepticism primarily stemmed from erosion of authentic learning and unfair advantage dilemma. Moreover, participants believed that whether GenAI constitutes academic misconduct depends on the extent of its utilisation. As illustrated in the semi-structured interviews, the judicious application of GenAI reflects essential digital literacy in the modern era, whereas its misuse crosses ethical boundaries. This divergence in perception among our participants reveals two underlying tensions. Firstly, students lack clarity regarding the permissible scope of GenAI usage in academic assignments [ 41 ]. Secondly, universities have yet to reach a consensus on integrity policies concerning this emerging technology. Currently, university regulations on GenAI-assisted assignments predominantly remain confined to binary “prohibition or permission” approaches. Some universities currently impose a blanket ban on using GenAI for assignments, treating violations as academic misconduct [ 42 ]. Universities like the Massachusetts Institute of Technology and Harvard University in the United States, as well as Fudan University in China, have explicitly permitted student use of GenAI under specified conditions [ 36 , 43 – 45 ]. Therefore, a multi-stakeholder consensus is needed to establish clear boundaries for GenAI use, optimizing its educational benefits while protecting academic integrity. In the Responsible Innovation framework, responsiveness involves responding to feedback and constructing strategic policies and technological standards. Therefore, in this study, participants proposed several recommendations for regulating the use of GenAI. Firstly, regarding academic integrity management of GenAI usage, beyond providing students with clear guidelines defining the permissible scope of GenAI-assisted assignments, universities should also establish oversight mechanisms and require students to explicitly label Al-generated content to address academic misconduct caused by GenAI. Secondly, concerning the reconstruction of student evaluation mechanisms, teachers are proposed to reduce dependence on traditional written assignments by designing diverse assessment formats. Interactive and application-oriented methods, such as hands-on tasks, oral presentations, and group discussions, that are resistant to direct AI completion should be integrated. Formative assessments tracking student learning processes, including submissions of draft work, revision records, and task completion workflows, should be emphasized. AI detection tools are also proposed to develop to assist teachers in identifying AI-generated content in student work. Thirdly, for the development of students’ GenAI competencies, teachers should provide students with GenAI courses that simultaneously foster effective human-AI collaboration skills and cultivate critical independent thinking. Additionally, academic integrity training should also be integrated into standard curricula to promote ethical GenAI usage [ 46 ]. Strengths and limitations A major strength of this study lies in the originality of the exploration of GenAI’s ethical challenges and coping strategies in academic assignments through qualitative interviews with both undergraduate medical students and teachers. However, the study also has limitations. Firstly, although this study involved teachers and students from multiple specialties, the sample size within each specialty was limited, and all participants were from one university. This may restrict the generalizability and representativeness of the findings. Secondly, this study only involved frontline teachers but not institutional administrators, which may result in the research findings overlooking the dimension of institutional management-level response strategies. Finally, it is essential to recognize that GenAI, as an emerging technology, has continually evolving capabilities; consequently, a general understanding of it may remain incomplete. Future research might lean on experimental approaches and surveys to thoroughly investigate GenAI’s ethical norms and long-term governance mechanisms in the medical educational context. Conclusion In this study, the research team identified three core themes through the lens of the Responsible Innovation framework, which are (1) Subversion posed by GenAI, (2) Limitations and potential risks of using GenAI, and (3) Coping strategies in response to the utilisation of GenAI. These findings provide critical insights to help university policymakers and teachers refine educational practices and implement institutional and pedagogical strategies for addressing ethical challenges in GenAI-assisted academic assignments. Declarations Acknowledgments The authors would like to thank all undergraduate medical students and teachers who participated in this study. Author contributions All authors contributed to the study conception and design. D.L.L. led the conceptualization of the research framework, secured funding, established the methodology, and oversaw project administration. K.Y.H. contributed to conceptual development and methodological design, and participated in manuscript review and editing. W.T.X. and G.A. were responsible for data collection. W.T.X. and K.L.L. performed data analysis and prepared the initial manuscript draft. J.Y. provided critical feedback on the initial draft and contributed to manuscript editing. J.D.P., T.L., H.C., S.F.Z., and Z.J.Z. engaged in analytical discussions of emerging themes during data interpretation and participated in manuscript editing. All authors reviewed and approved the final manuscript. F unding This study was funded by Educational Science Planning Project of Guangzhou Medical University. The project number is 2024-41. Data availability The qualitative data collected in this study are available from the corresponding author (D.L.L) upon reasonable request, provided the request meets research ethics and governance criteria. Clinical trial numbe r Not applicable. Ethics approval and consent to participate The study protocol was approved by the Ethics Committee of The Second Affiliated Hospital, Guangzhou Medical University (LYZX-2025-044-01). Informed consent was obtained from each participant prior to the interviews. 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Relationship between students’ academic self-concept, intrinsic motivation, and academic performance. Int J Sch Educ Psychol. 2024;12(1):41–53. Fawaz M, El-Malti W, Alreshidi SM, Kavuran E. Exploring Health Sciences Students' Perspectives on Using Generative Artificial Intelligence in Higher Education: A Qualitative Study. Nurs Health Sci. 2025;27(1):e70030. d. Leng L. Challenge, integration, and change: ChatGPT and future anatomical education. Med Educ Online. 2024;29(1):2304973. 10.1080/10872981.2024.2304973 . Li Z, Generative AI, in Higher Education Academic Assignments. : Policy Implications from a Systematic Review of Student and Teacher Perceptions[dissertation]. Cambridge (MA): Massachusetts Institute of Technology; 2023. [2025-04-10]. Available from: https://dspace.mit.edu/handle/1721.1/155977 Leng L. Challenge, integration, and change: ChatGPT and future anatomical education. Med Educ Online. 2024;29(1):2304973. Currie GM. Academic integrity and artificial intelligence: is ChatGPT hype, hero or heresy? Semin Nucl Med. 2023;53(5):719–30. Eke DO. ChatGPT and the rise of generative AI: Threat to academic integrity? J Responsib Technol. 2023;13:100060. Kazley AS, Andresen C, Mund A, Blankenship C, Segal R. Is use of ChatGPT cheating? Students of health professions perceptions. Med Teach. 2024;3:1–5. Chan CK, Hu W. Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. Int J Educ Technol High Educ. 2023;17(1):43. MID. MBA AI Policy. - Master of Business Administration [EB/OL]. IMD, 2023 [2025-04-20]. https://www.imd.org/degree/mba/program/ai-policy/ Taylor K. Supporting students and educators in using generative artificial intelligence. APUBS. 2023;28. Fudan University. Announcing the First AI Regulation in Domestic Universities [EB/OL]. (2024-12-20) [2025-04-20]. https://news.fudan.edu.cn/2024/1220/c3163a143685/page.htm Love AS, Niu C, Labay-Marquez J. Artificial Intelligence in Public Health Education: Navigating Ethical Challenges and Empowering the Next Generation of Professionals. Health Promot Pract. 2025:15248399251320989. Application of artificial. intelligence-based technologies in the healthcare industry: Opportunitie. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6803964","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469546682,"identity":"4490bd27-9ba9-4ed7-a4a1-45c2540f6e85","order_by":0,"name":"Wen-Ting Xu","email":"","orcid":"","institution":"The Second Affiliated Hospital, Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wen-Ting","middleName":"","lastName":"Xu","suffix":""},{"id":469546683,"identity":"2e45b0a5-fd1a-4aab-a6fc-07188d08b4e8","order_by":1,"name":"Ke-Lan Lin","email":"","orcid":"","institution":"The Second Affiliated Hospital, 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Ling","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYBACPmYgwQNiMDAwPmBgOAAWlcCnhQ2mhY2BgdmAOC0MCC1sEsRpYec9/OJNxR27Nvazxyq//LkTbXCA+eBtHga7PNwO40uznHPmWXIbT17abRmeZ7kbDrAlW/MwJBfj1sJjZszbdjiZjSHH7LaExGGgFh4zaR6GA4kNBLXwvzErljAAaeH/RkiL8WOgFjs2iRwzxg8JYFvYCNrCOOfM4QQ2iTfG0gwHDufOPMxmbDnHIBmnFn7+M8Yf3lQctufnzzH8+OPP4dy+480Pb7ypsMOphQESHQxgBcygCGIARS6DAW71ICUfgIQ9iMX4A6/CUTAKRsEoGKkAAHY0U7BpLHWAAAAAAElFTkSuQmCC","orcid":"","institution":"The Second Affiliated Hospital, Guangzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Dong-Lan","middleName":"","lastName":"Ling","suffix":""}],"badges":[],"createdAt":"2025-06-02 16:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6803964/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6803964/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84680629,"identity":"55c78134-4a71-4fa7-b03a-efc8a79ae386","added_by":"auto","created_at":"2025-06-16 08:17:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":159324,"visible":true,"origin":"","legend":"\u003cp\u003eA thematic map of ethical challenges and coping strategies in using GenAI for academic assignments.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6803964/v1/7d1bd96eea29394dbdde0c44.png"},{"id":104781171,"identity":"6e68a84a-1d40-4692-b7e9-b154ae766eaa","added_by":"auto","created_at":"2026-03-17 07:55:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1298626,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6803964/v1/c04cc558-bcc2-4db8-8a9e-761fa86e7f60.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Undergraduate medical students’ and teachers’ perspectives on ethical challenges and coping strategies of using generative artificial intelligence for academic assignments: A qualitative study","fulltext":[{"header":"Background","content":"\u003cp\u003eGenerative artificial intelligence (GenAI) has advanced remarkably in recent years, with applications expanding into domains like education and healthcare [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It has the capability to simulate human-like dialogues, analyse and interpret conversational data, and generate a wide array of content through deep learning models [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In the educational field, GenAI tools such as ChatGPT, DeepSeek, Kimi, GitHub Copilot, and Scribe to Code have demonstrated significant potential, driving profound transformations in educational philosophies, teaching methodologies and learning environments [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA growing body of research shows that GenAI can enhance teaching efficiency and improve student learning outcomes in medical education [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Medical education, as a domain depending on perpetual knowledge renewal and hands-on practice, seeks to develop medical students\u0026rsquo; competence in managing complex patient conditions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This objective demands not only a solid foundation in medical knowledge but also the development of clinical thinking, communication skills, and problem-solving abilities [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. GenAI has provided support for achieving this goal by enhancing medical students\u0026rsquo; capabilities in medical writing, case analysis, and diagnostic reasoning [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile GenAI can improve academic performance to some extent, the negative impacts, such as posing a threat to comprehensive competency development, have also emerged [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The utilisation of GenAI for academic assignments may cause cognitive passivity and dependency as students increasingly rely on these tools [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Excessive dependence on these technologies not only restricts students\u0026rsquo; engagement in critical thinking but also undermines their academic growth and clinical decision-making capabilities [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, the immediate access to knowledge facilitated by GenAI has diminished the students\u0026rsquo; sense of delayed gratification, which may potentially impact their long-term motivation to learn and their capacity for self-discipline [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, the misuse of GenAI poses significant challenges to academic integrity systems [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The phenomenon of students using GenAI to complete their academic assignments undermines the reliability of traditional student evaluation methods and raises concerns about academic integrity and originality [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Existing scholarly norms struggle to reliably distinguish AI-generated content from human-authored work, resulting in significant difficulties in determining misconduct cases [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. It is worth noting that current universities fail to provide guidelines specifically addressing GenAI utilisation in student work [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The absence of a proper GenAI academic regulatory leads to distorted academic assessments that fail to accurately measure either learning outcomes or scholarly effort, thereby compromising the fairness and reliability of evaluation systems and ultimately undermining educational quality [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe quality of medical education directly impacts students\u0026rsquo; professional competencies, thereby influencing the overall quality of healthcare services [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. As such, teachers have come to increasingly recognize the importance of guiding students in the rational and ethical use of GenAI tools [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. While GenAI demonstrates unique advantages in medical education, further exploration is needed to establish rational and standardized guidelines for its appropriate use [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Notably, studies remain scarce regarding medical students\u0026rsquo; and teachers\u0026rsquo; perspectives on GenAI, and the ethical dilemmas students face when using it. Given that the quality of medical education directly shapes students\u0026rsquo; professional competencies, it is critical to address these emerging challenges. Therefore, a qualitative study is employed to explore their perspectives, seeking to establish a theoretical foundation and inform robust educational policies for the judicious application of GenAI in medical education, ultimately contributing to advancing the field.\u003c/p\u003e \u003cp\u003eA review of literature indicated that the Responsible Innovation framework could offer a valuable lens for examining medical students\u0026rsquo; and teachers\u0026rsquo; perspectives related to the use of GenAI in academic assignments [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The framework was initially proposed to raise, discuss, and address issues related to the ethical acceptability, sustainability, and societal appropriateness of emerging technologies when they are applied in our society. Exploring these questions provides insightful guidance for governance, enabling the more appropriate integration of the technologies into society. The framework comprises four dimensions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], including (1) anticipation, referring to proactively assessing potential ethical and societal impacts of emerging technologies, (2) reflexivity, which encourages stakeholders to critically evaluate their roles and assumptions in technology adoption, (3) inclusion, involving the engagement of diverse perspectives (e.g., students, teachers) in dialogues about technology use, and (4) responsiveness, which involves responding to feedback and constructing strategic policies and technological standards. Over the past decade, this framework has been widely applied in technology governance and ethics research to balance innovation with accountability and adapted for educational contexts [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Based on the high applicability of this framework to new technologies, we have applied it to explore students\u0026rsquo; and teachers\u0026rsquo; perspectives on the ethical challenges and coping strategies in GenAI-assisted assignments.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eA descriptive qualitative study, based on the Consolidated Criteria for Reporting Qualitative Studies (COREQ) guideline [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], was conducted among undergraduate medical students and their teachers from a government-funded medical university in Guangzhou, China between January and April 2025.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy participants\u003c/h3\u003e\n\u003cp\u003eParticipants in this study were recruited using purposive maximum variation sampling to ensure representation across gender, age, grade, and major for students, and gender, age, teaching specialisation, and professional title for teachers [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The inclusion criteria for students were (1) being undergraduate medical students and (2) having prior experience in using GenAI. The inclusion criteria for teachers were (1) having prior experience in using GenAI and (2) currently teaching the undergraduate medical courses. The exclusion criteria for both were being on sick or maternity leave for over one month in the past three months before joining the study. The sample size was determined by data saturation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Data collection ceased when the research team observed no emergence of new codes during three consecutive interviews, indicating stabilized data patterns.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003e The Responsible Innovation framework, which originates from key questions emerging in public debates about new technological areas, emphasizes aligning technological development with ethical principles, societal values, and accountability. These questions draw on analysis of cross-cutting public concerns from 17 UK public dialogues on science and technology, categorizing them based on their relation to the products, processes, or purposes of innovation. Building upon this theoretical foundation, this study adopted the core questions from these questions as the basis for designing the interview guide. Following the literature review, the research team discussed the core objectives of the study and subsequently developed the interview guide draft. A pilot study was conducted with two medical students and two teachers to refine the interview guide. The final interview guide is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInterview guide based on Responsible Innovation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProduct questions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProcess questions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePurpose questions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. What impacts do you think the use of GenAI in academic assignments has on you?\u003c/p\u003e \u003cp\u003e2. Do you think that students using GenAI to assist with academic assignments constitutes cheating? Why?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3. Could you describe your process when using GenAI for assignments?\u003c/p\u003e \u003cp\u003e4. When evaluating assignments, if some students submit work completed with GenAI assistance while others submit entirely independent work, how should they be graded? Should adjustments be made to the grading criteria? Why?\u003c/p\u003e \u003cp\u003e5. What strategies do you think would help you use GenAI appropriately?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6. Have you ever used GenAI to assist with your academic assignments? If so, what kinds of GenAI tools have you used? And for what types of assignments have you used GenAI?\u003c/p\u003e \u003cp\u003e7. What are your perspectives on using GenAI to complete assignments?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTeachers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. What impacts do you think students' use of GenAI in academic assignments has on them?\u003c/p\u003e \u003cp\u003e2. Do you think that students using GenAI to assist with academic assignments constitutes cheating? Why?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3. Are you able to determine whether students completed their assignments independently or with the assistance of GenAI? Why?\u003c/p\u003e \u003cp\u003e4. When evaluating assignments, if some students submit work completed with GenAI assistance while others submit entirely independent work, how should they be graded? Should adjustments be made to the grading criteria? Why?\u003c/p\u003e \u003cp\u003e5. What strategies do you think would help students use GenAI appropriately?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6. Have you ever used GenAI? If so, what kinds of GenAI tools have you used?\u003c/p\u003e \u003cp\u003e7. What are your perspectives on students' use of GenAI in academic assignments?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData were collected by two interviewers (G.A. and W.T.X.), both trained in qualitative research methods at the university. After obtaining participants\u0026rsquo; consent, one-to-one face-to-face interviews were conducted in Mandarin, each lasting 30 to 45 minutes. In addition, the facial expressions and body postures of the participants were recorded in the field notes. Interviews took place in quiet locations according to participants\u0026rsquo; preferences, including dormitories, classrooms, and offices at the university. All interviews were audio-recorded with participants\u0026rsquo; consent.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe audio recordings were imported into NVivo 12 for analysis by the two interviewers (W.T.X. and G.A.). Within 24 hours after each interview, one interviewer transcribed the recordings verbatim while the other cross-checked the transcripts against the audio for accuracy. A mixed analytical approach was adopted for data analysis, where semantic units were first extracted following Colaizzi\u0026rsquo;s phenomenological steps and then deductively categorized using the four dimensions of the Responsible Innovation framework [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. To ensure study credibility, researcher triangulation was implemented during data analysis, with an additional researcher K.L.L. replacing G.A. in the analytical process. Firstly, W.T.X. and K.L.L. independently and iteratively reviewed all interview transcripts to establish a foundational understanding before extracting semantic units. Subsequently, they developed descriptive codes based on these units. Then, a deductive analysis was applied to map descriptive codes to the four dimensions (anticipation, reflexivity, inclusion, responsiveness) of the Responsible Innovation framework, with openness to emergent themes. Through repeatedly extracting, analysing, iterative comparison, and grouping, codes were refined into conceptually distinct clusters that subsequently informed themes and categories. Finally, all codes and categories were grouped under the themes based on the four dimensions, using a thematic mapping technique. There was a movement back and forth between the raw data, codes, and initial themes to ensure coherence between the codes. Disagreements in codes, categories, and themes were resolved by discussion of the entire research group. Ultimately, the generated thematic structure was returned to the participants for verification, asking whether it captured their true perspectives to ensure the accuracy of the results. The corresponding author (D.L.L.), with extensive teaching and qualitative research experience in the field of medical education, ensured that the data were thoroughly covered and the themes were accurately represented.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003e The study protocol was approved by the Ethics Committee of The Second Affiliated Hospital, Guangzhou Medical University (LYZX-2025-044-01). Before data collection, all participants received detailed study information (purpose, methodology, and right to withdraw) and provided written informed consent. During the interview process, no participants dropped out. To ensure confidentiality, all participant information was anonymized by assigning random numbering based on interview sequence, de-identified, encrypted, and stored with restricted access under the first author\u0026rsquo;s supervision.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy Rigor\u003c/h2\u003e \u003cp\u003eThe study\u0026rsquo;s rigor was enhanced through the application of Lincoln and Guba\u0026rsquo;s criteria[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. To ensure credibility, the first author had pre-existing relationships with participants, facilitating good rapport. The research team also employed methodological triangulation to maintain analytical consistency and objectivity. Specifically, data collection was conducted by G.A. and W.T.X., while data analysis was performed independently by W.T.X. and K.L.L. Teacher and student interview data were triangulated to validate central themes. Furthermore, the study meticulously documented participants\u0026rsquo; demographic characteristics to enable readers to assess the potential transferability of findings to similar educational contexts. Moreover, for confirmability, the research team engaged in detailed discussions and preparations prior to the interviews to ensure the neutrality of the interview questions. All interviewers (G.A. and W.T.X.) received systematic training to maintain impartiality during interviews. Regular team meetings were held to reflect on potential biases arising from researchers\u0026rsquo; backgrounds. For instance, the team explicitly discussed W.T.X.\u0026rsquo;s and K.L.L.\u0026rsquo;s views on \u0026ldquo;students using GenAI to assist with assignments\u0026rdquo;. They did not view it as either positive or negative but rather genuinely wanted to better understand the phenomenon. Lastly, to maintain dependability, all interview recordings, manuscripts, notes, and coding documentation were systematically archived for independent audit.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":" \u003cp\u003eA total of 11 medical students and 8 teachers participated in the study. The details regarding the characteristics of the students and teachers are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, respectively. Guided by the Responsible Innovation framework, a thematic map was developed and is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The thematic map illustrates the relationships among the themes, categories, and codes that describe the ethical challenges and coping strategies that students encounter when using GenAI for academic assignments.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe characteristics of student participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMajor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFourth year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClinical Medicine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFourth year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNursing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFourth year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClinical Medicine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFourth year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNursing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFirst year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIntegrated Chinese-Western Medicine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSecond year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDentistry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSecond year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNursing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThird year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClinical Medicine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThird year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePsychology\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThird year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNursing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFirst year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnaesthesiology\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe characteristics of teacher participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTeacher\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTeaching Specialization\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProfessional Title\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClinical Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAssociate Professor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnaesthesiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLecturer\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClinical Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLecturer\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDentistry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProfessor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntegrated Chinese-Western Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAssociate Professor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePsychology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAssociate Professor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNursing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAssociate Professor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNursing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLecturer\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eTheme 1: Subversion posed by GenAI\u003c/h3\u003e\n\u003cp\u003eThis theme focuses on the effects of GenAI on teachers\u0026rsquo; and students\u0026rsquo; motivation shift and technology adaptation in medical education.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCategory 1: Shift in students\u003c/b\u003e \u003cem\u003e\u0026rsquo;\u003c/em\u003e \u003cb\u003elearning paradigm and motivation\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSome students acknowledged that while GenAI enhanced their learning efficiency, it has altered their traditional learning methods and pathways. This prompted them to rethink the significance of learning. As illustrated by a student in the semi-structured interview,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Initially, I was sceptical about GenAI\u003c/em\u003e\u0026rsquo;\u003cem\u003es capabilities. I think the traditional learning motivation comes when you explore things and solve problems by yourself. But with GenAI, it\u003c/em\u003e\u0026rsquo;\u003cem\u003es like having an instant helper that can answer questions tailored just for you. It\u003c/em\u003e\u0026rsquo;\u003cem\u003es really shifted from traditional learning methods.\u0026rdquo; (S1)\u003c/em\u003e\u003c/p\u003e \u003cp\u003eAnother student also said,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;I sometimes ponder whether the memorization of knowledge is still necessary when GenAI can provide precise answers instantaneously. Has the essence of human learning shifted from \"acquiring knowledge\" to \"mastering tools\"? In this paradigm shift, how should I reorient my efforts? I am confused.\u0026rdquo; (S3)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWhen GenAI could conveniently provide solutions, learners\u0026rsquo; motivation for independent exploration and the sense of fulfilment gained through problem-solving were gradually diminishing. For example, in the semi-structured interviews, some students reported that they were more inclined to use GenAI to complete tasks quickly rather than invest time and effort in delving into problems. One student further illustrated as follow,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;It\u0026rsquo;s especially noticeable in Problem-Based Learning (PBL) courses. We used to work through patient cases step by step, uncovering underlying health issues along the way. It was challenging, but there was a real sense of motivation in this process. Now, I will hand the case to GenAI and get solutions in seconds. However, as a medical student, I think consistently relying on immediate, pre-existing answers would gradually erode my intellectual curiosity and diminish my learning motivation.\u0026rdquo; (S1)\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAnother student similarly said\u003c/em\u003e,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;In the past, whenever I came up with a new idea through my own efforts or solved a difficult learning problem, I would feel a great sense of achievement. However, now that I see GenAI has stronger innovation capabilities and problem-solving skills than I do, this is so frustrating. I\u003c/em\u003e\u0026rsquo;\u003cem\u003em afraid I\u003c/em\u003e\u0026rsquo;\u003cem\u003ell miss out on the key elements that can truly help me become a good doctor.\u0026rdquo; (S8)\u003c/em\u003e\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCategory 2: Anxiety triggered by GenAI\u003c/h2\u003e \u003cp\u003eGenAI threatened the students psychologically, with most reporting widespread anxiety over its adoption. One student further explained this in the semi-structured interview,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;I used to take pride in my learning advantages, particularly my strength in linguistic logic. But now, seeing GenAI surpass my abilities in this aspect, I feel intense pressure and fear. I start to question whether I still need to develop this skill, and I don\u003c/em\u003e\u0026rsquo;\u003cem\u003et know whether I should learn anymore.\u0026rdquo; (S4)\u003c/em\u003e\u003c/p\u003e \u003cp\u003eAnother student also said,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;I see my classmates using GenAI to complete their assignments. They finish their work faster and better than me. I feel stressed that I\u0026rsquo;ll fall behind if I don\u0026rsquo;t use it too, so I just go along with it.\u0026rdquo; (S9)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eLikewise, the teachers also reported concerns to use GenAI in medical training as they thought that the superficial benefits to efficiency degraded authentic learning. As indicated by a teacher in the semi-structured interview,\u003c/p\u003e \u003cp\u003e\u0026ldquo;\u003cem\u003eWhen using GenAI, students perceive an enhancement in their learning efficiency. However, this perception may be somewhat misconceived. When students bypass the critical components of the learning process, such as reading textbooks, seeking guidance from instructors, and engaging in practical exercises, they will be inadequately prepared for the complex clinical practice. Upon entering the professional arena and confronting intricate and dynamic clinical challenges, they may lack the requisite problem-solving capabilities. Therefore, I deeply worry in the era of GenAI.\u0026rdquo; (T4)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCategory 3: Reconceptualisation of teacher-student relationships and roles\u003c/h2\u003e \u003cp\u003eGenAI technological adaptation presented a dual challenge for teachers and students, requiring both relational restructuring and role reconceptualisation. Students pointed out that the efficiency and convenience of GenAI have transformed their role from mere recipients of knowledge to users of knowledge, and the relationship between teachers and students has also changed. This was further illustrated by a student in the interview,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;In the past, we mainly listened to the teacher\u003c/em\u003e\u0026rsquo;\u003cem\u003es lectures, and the teacher was the one in charge. But now with GenAI, the teacher\u003c/em\u003e\u0026rsquo;\u003cem\u003es role seems to have changed. It\u003c/em\u003e\u0026rsquo;\u003cem\u003es no longer that traditional \"I talk, you listen\" pattern. This change feels pretty cool to me. I feel that I\u0026rsquo;ve transitioned from a passive receiver to an active user of knowledge. However, I really hope that teachers can also sense this change and guide us on how to use these new technologies well. And we can build a new kind of interactive relationship between teachers and students. (S5)\u003c/em\u003e\u003c/p\u003e \u003cp\u003eMeanwhile, teachers have also been reflecting on their shifting roles and attempting to adjust their teaching approaches. And they expressed a cautious and limited endorsement of students\u0026rsquo; use of GenAI. One teacher expressed in the semi-structured interview,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Students seem to be seeking help from teachers less frequently than before. This situation often confuses me and even leads me to question whether my role as a teacher is still necessary. Faced with such changes, I have been thinking about what adjustments my teaching role needs to make.\u0026rdquo; (T2)\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAnother teacher also said\u003c/em\u003e,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Although I can see some benefits that GenAI brings to students, I am also keenly aware that there are currently no clear regulations for GenAI, which may give rise to many academic ethical issues. Therefore, I am cautious about students\u003c/em\u003e\u0026rsquo; \u003cem\u003euse of GenAI. I need to help them keep an eye on things and guide them in using these tools correctly to prevent them into the academic ethical dilemma.\u0026rdquo; (T5)\u003c/em\u003e\u003c/p\u003e \u003cp\u003eIn a more profound reflection, one teacher emphasized that only by thoroughly mastering and effectively utilising GenAI, teachers could more accurately assess students\u0026rsquo; learning trajectories and outcomes. As stated by the teacher in the interview,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;If we as teachers aren\u003c/em\u003e\u0026rsquo;\u003cem\u003et familiar with GenAI, we might not even realize, let alone identify, which content is AI-generated. This may result in students utilising GenAI for assignments achieving higher grades than their non-AI-assisted peers. Interestingly, my observations reveal that teachers who frequently employ GenAI can readily discern AI-generated work, whereas those less accustomed to such tools often fail to detect it. After all, as teachers, we have to guide our students to use these new technologies properly, rather than being replaced by them. Precisely for this reason, the current environment compels us to learn and master these technologies. This is not just a challenge but also a necessary process of adaptation. (T2)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTheme 2: Limitations and potential risks of using GenAI\u003c/h2\u003e \u003cp\u003eIn assessing GenAI\u0026rsquo;s impact on assignments, participants identified risks of over-reliance, and they held polarized views on its implications for academic integrity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCategory 1: Students\u0026rsquo; over-reliance on GenAI\u003c/h2\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSome participants stated that over-reliance of GenAI might lead students to have difficulty in completing assignments without the assistance of GenAI. One student further illustrated in the semi-structured interview,\u003c/h2\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Indeed, I have realized that my reliance on AI has increased. Initially, I merely utilised GenAI to refine the expression in my reflective journals. However, I now find myself unable to articulate even a single sentence without its assistance.\u0026rdquo; (S7)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWhen students have used GenAI to complete assignments and gradually developed a dependency habit, they reported that they might uncritically accept the AI-generated content. In the semi-structured interview, one student said,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;We all know that AI-generated treatment plans are standardized and don\u003c/em\u003e\u0026rsquo;\u003cem\u003et take into account individual patient differences. For example, during a case discussion, the patient was clearly allergic to penicillin, but I was too reliant on GenAI at the time. I didn\u003c/em\u003e\u0026rsquo;\u003cem\u003et even think about whether the answer given by GenAI was right or wrong. I just copied its treatment plan directly, ignored the patient\u003c/em\u003e\u0026rsquo;\u003cem\u003es allergy history, and ended up choosing a penicillin-based drug. (S3)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCategory 2: Threats to academic integrity posed by GenAI\u003c/h2\u003e \u003cp\u003eBoth teachers and students mentioned the effects of using GenAI for assignments on academic integrity. Since the extent to which students rely on GenAI to complete assignments varies significantly, and these differences contributed to divergent views among them regarding whether such use constituted cheating. Some participants maintained that utilising GenAI for assignments constituted academic dishonesty, even when students paraphrased the AI-generated content. As illustrated by one student,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;I consider this (using GenAI) is cheating. Submitting AI-generated content is equivalent to having someone or a machine do the work for you. Even if you reword it, it\u003c/em\u003e\u0026rsquo;\u003cem\u003es still cheating because it breaks the rules of academic honesty.\u0026rdquo; (S6)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eAnother student also said\u003c/em\u003e,\u003c/p\u003e \u003cp\u003e \u003cem\u003eUndoubtedly, this is cheating. I\u003c/em\u003e\u0026rsquo;\u003cem\u003eve noticed some classmates directly using GenAI to generate content for their assignments without critical thinking. Actually, sometimes GenAI tends to fabricate references. It poses a significant challenge to academic integrity. (T3)\u003c/em\u003e\u003c/p\u003e \u003cp\u003eNevertheless, some participants argued that GenAI merely provided students with ideas and frameworks for assignment assistance, maintaining that such usage constituted legitimate academic support rather than cheating. As illustrated by one student,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;I don\u003c/em\u003e\u0026rsquo;\u003cem\u003et think it counts as cheating because when I complete my assignments, I only draw on some ideas rather than copying everything verbatim.\u0026rdquo; (S8)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eAnother participant also said\u003c/em\u003e,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;I don\u003c/em\u003e\u0026rsquo;\u003cem\u003et think this should be considered cheating because using GenAI to complete assignments demonstrates students\u0026rsquo; digital literacy. If students can achieve better results by using GenAI, then its application should be permitted.\u0026rdquo; (T1)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTheme 3: Coping strategies in response to the utilisation of GenAI\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003eCategory 1: Academic integrity management of GenAI usage\u003c/h2\u003e \u003cp\u003eBoth teachers and students have expressed their perspectives on the management of academic integrity. They proposed the need to establish clear boundaries for the use of GenAI in academic assignments to maintain integrity and fairness. In the interview, one student said,\u003c/p\u003e \u003cp\u003e \u003cem\u003eI believe it\u003c/em\u003e\u0026rsquo;\u003cem\u003es essential to establish clear academic integrity standards regarding students\u003c/em\u003e\u0026rsquo; \u003cem\u003euse of GenAI. If students who rely on AI can still earn higher grades, then what motivation do I have to put in the effort? It will ruin the whole value of learning.\u0026rdquo; (S6)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eOne teacher similarly said\u003c/em\u003e,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;I think GenAI can really help students learn a lot if being used properly, but the key is to know the limits. There needs to be a clear boundary, or students would easily go off the track.\u0026rdquo; (T6)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWhile clearly defining the applicable boundaries, another teacher emphasized that it is equally important to require students to label the content generated by AI. As illustrated by that teacher,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Students need to inform me that they have used an AI tool. If you fail to disclose its use and I discover it, that would lead to a different form of plagiarism. We need to set up a proper penalty system for this.\u0026rdquo; (T5)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAdditionally, some teachers have suggested that establishing oversight mechanisms was also a necessary intervention for managing academic integrity. One teacher said,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;I believe that academic integrity partly relies on students\u003c/em\u003e\u0026rsquo;\u003cem\u003eself-discipline, but it also requires a supervisory mechanism to ensure compliance. The oversight mechanism can help us manage their overuse of GenAI for academic assignments.\u0026rdquo; (T3)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCategory 2: Reconstruction of student evaluation mechanisms\u003c/h2\u003e \u003cp\u003eParticipants argued that optimizing student evaluation mechanisms could mitigate academic inequities caused by \u003cem\u003eGen\u003c/em\u003eAI. In the semi-structured interview, one teacher said,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Reverting to traditional student evaluation methods could be a good way to uphold educational integrity. I think replacing take-home essays with closed-book exams and incorporating in-class performance into final assessments could effectively reduce GenAI\u003c/em\u003e\u0026rsquo;\u003cem\u003es influence on grading outcomes.\u0026rdquo; (T7)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eOne student similarly reported that\u003c/em\u003e,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;I think teachers should grade us in more diverse ways. Maybe cutting down on assignments that can be done with GenAI would help.\u0026rdquo; (S2)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFurthermore, both teachers and students suggested that implementing AI detection tools could help ensure appropriate GenAI use in academic assignments. This was supported by a student as follows,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;I believe detection shouldn\u003c/em\u003e\u0026rsquo;\u003cem\u003et rely solely on teachers\u003c/em\u003e\u0026rsquo; \u003cem\u003ejudgment. It should integrate AI writing detection tools to identify AI-generated content.\u0026rdquo; (S2)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eAlso, one student said\u003c/em\u003e,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;I believe that plagiarism detection software can be employed to identify traces generated by AI, essentially using technology to counteract technology.\u0026rdquo; (T3)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCategory 3: Development of students\u0026rsquo; GenAl competencies\u003c/h2\u003e \u003cp\u003eParticipants proposed that universities should offer courses to help students improve their GenAI usage competency. They also emphasized that integrating academic integrity education was essential to maintain fairness and educational integrity. As explained by one teacher,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;I think we should start teaching students how to work with GenAI the right way. Instead of just telling them not to use it, we could show them how to use tools like GenAI properly for their assignments.\u0026rdquo; (T1)\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAnother teacher similarly said\u003c/em\u003e,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Academic integrity has always been at the heart of education. We should make students recognize its critical importance by integrating GenAI literacy and academic integrity courses for students.\u0026rdquo; (T6)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFurthermore, one teacher proposed utilising GenAI as a dynamic interactive tool to transform traditional learning paradigms into human-AI collaborative frameworks. As illustrated by that teacher in the interview,\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;If students can learn to use GenAI properly by using it as a tool for inquiry, counter-questioning, and interactive dialogue, I honestly think GenAI could help them learn knowledge more efficiently. We need to learn how to use AI effectively. For instance, we can first conceptualize ideas and let AI help refine them.\u0026rdquo; (T6)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe conducted in-depth interviews with 11 undergraduate medical students and 8 teachers to explore their perspectives on the ethical challenges and coping strategies of using GenAI for academic assignments. Guided by the Responsible Innovation framework, we identified three themes and eight categories, which focused on students\u0026rsquo; and teachers\u0026rsquo; perspectives on using GenAI for academic assignments. Their descriptions specifically elaborated on the shift of students\u0026rsquo; learning paradigm and motivation, anxiety triggered by GenAI, reconceptualisation of teacher-student relationships and roles, over-reliance on GenAI, threats to academic integrity, and coping strategies for the usage of GenAI.\u003c/p\u003e \u003cp\u003eThe theoretical framework is based on the premise that emerging techonologies have not only generated understanding and knowledge, but also raised questions, dilemmas, and unintended consequences. In our study, students reported that GenAI, while reshaping traditional learning processes, might erode their intrinsic motivation to pursue in-depth knowledge. According to Bloom\u0026rsquo;s Taxonomy in educational theory, learning progresses from remembering and understanding to applying, analysing, evaluating, and creating [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Students derive satisfaction and intrinsic motivation by mastering each stage [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, with GenAI, they can now reach the applying stage without necessarily going through the stages of remembering and understanding, which consequently weakens their learning motivation [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This shift in learning motivation may cause students to bypass crucial cognitive exploration during the learning process, thereby affecting the comprehensive development of their academic competencies. This perspective aligns with scholarly findings that when GenAI reduces essential learning experiences to formulaic assignments, students often cease to actively engage in knowledge exploration and deep comprehension [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, this study revealed a concern that over-reliance on GenAI might lead to superficial learning among students. This technological dependence may create an illusion of competence, masking students\u0026rsquo; actual skill deficiencies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Stirling et al. suggest that issues such as technological dependence and lock-in, may arise during the process of utilizing technology. Over-reliance on GenAI could lead students into technological lock-in, making it difficult for them to break free from AI dependence. Over time, students may develop the misconception that relying solely on such tools suffices for completing academic assignments. Over-reliance on GenAI could diminish learning motivation derived from students\u0026rsquo; anxiety about knowledge gaps, which is a crucial motivational driver for the professional development of students. Ultimately, this may impede the professional growth of medical students [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study also found that GenAI has challenged the teaching relationship between students and teachers, shifting from traditional, single-scenario, and teacher-dominated modes to hybrid and interactive learning modes. As indicated by both students and teachers in the semi-structured interviews, the conventional binary teaching structure of teacher-student has inevitably supplanted by a ternary structure of teacher-AI-student, thereby forging a novel paradigm in educational practice. Likewise, the application of GenAI was found to risk eroding the social and moral value of learning. Although both teachers and students expressed that they have continuously adapted and strived to better integrate into this novel pedagogical paradigm, a set of universal guidelines has not been established for addressing role transformation and responsibility allocation in technology integration. This qualitative finding aligns with Li\u0026rsquo;s research perspective [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. There is a need for more anticipatory discussions on GenAI\u0026rsquo;s educational applications, so that teachers and students can proactively shape how GenAI integrates with education [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Teachers should persistently question and examine the educational values and potential risks behind the use of GenAI technology, ensuring the essence of education amid technological changes.\u003c/p\u003e \u003cp\u003eAdditionally, in the semi-structured interviews, students and teachers extensively discussed whether using GenAI for academic assignments would threaten academic integrity. Academic integrity establishes a foundation of fairness, and ethical responsibility in education, ensuring that academic achievements reflect genuine learning and effort [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The majority of students in this study believed that using GenAI for academic assignments would have a negative impact on academic integrity. The students\u0026rsquo; concerns about GenAI\u0026rsquo;s threat to academic integrity align with broader debates about technology-mediated learning [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Their skepticism primarily stemmed from erosion of authentic learning and unfair advantage dilemma.\u003c/p\u003e \u003cp\u003eMoreover, participants believed that whether GenAI constitutes academic misconduct depends on the extent of its utilisation. As illustrated in the semi-structured interviews, the judicious application of GenAI reflects essential digital literacy in the modern era, whereas its misuse crosses ethical boundaries. This divergence in perception among our participants reveals two underlying tensions. Firstly, students lack clarity regarding the permissible scope of GenAI usage in academic assignments [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Secondly, universities have yet to reach a consensus on integrity policies concerning this emerging technology. Currently, university regulations on GenAI-assisted assignments predominantly remain confined to binary \u0026ldquo;prohibition or permission\u0026rdquo; approaches. Some universities currently impose a blanket ban on using GenAI for assignments, treating violations as academic misconduct [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Universities like the Massachusetts Institute of Technology and Harvard University in the United States, as well as Fudan University in China, have explicitly permitted student use of GenAI under specified conditions [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Therefore, a multi-stakeholder consensus is needed to establish clear boundaries for GenAI use, optimizing its educational benefits while protecting academic integrity.\u003c/p\u003e \u003cp\u003eIn the Responsible Innovation framework, responsiveness involves responding to feedback and constructing strategic policies and technological standards. Therefore, in this study, participants proposed several recommendations for regulating the use of GenAI. Firstly, regarding academic integrity management of GenAI usage, beyond providing students with clear guidelines defining the permissible scope of GenAI-assisted assignments, universities should also establish oversight mechanisms and require students to explicitly label Al-generated content to address academic misconduct caused by GenAI. Secondly, concerning the reconstruction of student evaluation mechanisms, teachers are proposed to reduce dependence on traditional written assignments by designing diverse assessment formats. Interactive and application-oriented methods, such as hands-on tasks, oral presentations, and group discussions, that are resistant to direct AI completion should be integrated. Formative assessments tracking student learning processes, including submissions of draft work, revision records, and task completion workflows, should be emphasized. AI detection tools are also proposed to develop to assist teachers in identifying AI-generated content in student work. Thirdly, for the development of students\u0026rsquo; GenAI competencies, teachers should provide students with GenAI courses that simultaneously foster effective human-AI collaboration skills and cultivate critical independent thinking. Additionally, academic integrity training should also be integrated into standard curricula to promote ethical GenAI usage [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eA major strength of this study lies in the originality of the exploration of GenAI\u0026rsquo;s ethical challenges and coping strategies in academic assignments through qualitative interviews with both undergraduate medical students and teachers. However, the study also has limitations. Firstly, although this study involved teachers and students from multiple specialties, the sample size within each specialty was limited, and all participants were from one university. This may restrict the generalizability and representativeness of the findings. Secondly, this study only involved frontline teachers but not institutional administrators, which may result in the research findings overlooking the dimension of institutional management-level response strategies. Finally, it is essential to recognize that GenAI, as an emerging technology, has continually evolving capabilities; consequently, a general understanding of it may remain incomplete. Future research might lean on experimental approaches and surveys to thoroughly investigate GenAI\u0026rsquo;s ethical norms and long-term governance mechanisms in the medical educational context.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, the research team identified three core themes through the lens of the Responsible Innovation framework, which are (1) Subversion posed by GenAI, (2) Limitations and potential risks of using GenAI, and (3) Coping strategies in response to the utilisation of GenAI. These findings provide critical insights to help university policymakers and teachers refine educational practices and implement institutional and pedagogical strategies for addressing ethical challenges in GenAI-assisted academic assignments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all undergraduate medical students and teachers who participated in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. D.L.L. led the conceptualization of the research framework, secured funding, established the methodology, and oversaw project administration. K.Y.H. contributed to conceptual development and methodological design, and participated in manuscript review and editing. W.T.X. and G.A. were responsible for data collection. W.T.X. and K.L.L. performed data analysis and prepared the initial manuscript draft. J.Y. provided critical feedback on the initial draft and contributed to manuscript editing. J.D.P., T.L., H.C., S.F.Z., and Z.J.Z. engaged in analytical discussions of emerging themes during data interpretation and participated in manuscript editing. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003cstrong\u003eunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Educational Science Planning Project of Guangzhou Medical University. The project number is 2024-41.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe qualitative data collected in this study are available from the corresponding author (D.L.L) upon reasonable request, provided the request meets research ethics and governance criteria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial numbe\u003c/strong\u003er\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Ethics Committee of The Second Affiliated Hospital, Guangzhou Medical University (LYZX-2025-044-01). Informed consent was obtained from each participant prior to the interviews.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent, which explicitly authorized the publication of anonymized verbatim quotations extracted from interview transcripts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLee D, Yoon SN. s and challenges. Int J Environ Res Public Health. 2021;18(1):271.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeuerriegel S, Hartmann J, Janiesch C, Zschech P, Generative AI. 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Health Promot Pract. 2025:15248399251320989.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eApplication of artificial. intelligence-based technologies in the healthcare industry: Opportunitie.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Generative artificial intelligence, education, ethical challenges, academic integrity, qualitative research","lastPublishedDoi":"10.21203/rs.3.rs-6803964/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6803964/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGrowing ethical concerns have emerged regarding the misuse of generative artificial intelligence (GenAI) for academic assignments among undergraduate medical students. Given that the quality of medical education directly shapes students\u0026rsquo; professional competencies, exploring both medical students\u0026rsquo; and teachers\u0026rsquo; perspectives on GenAI use for academic assignments carries significant implications for medical education.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo explore medical students\u0026rsquo; and teachers\u0026rsquo; perspectives on ethical challenges and coping strategies of using GenAI for academic assignments.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study employed a descriptive phenomenological approach using semi-structured in-depth interviews. Purposive sampling was used to recruit undergraduate medical students and their teachers from one medical university between January and April 2025 in Guangzhou, China. Data were analyzed through Colaizzi\u0026rsquo;s phenomenological method, supplemented by deductive analysis guided by the Responsible Innovation framework to identify key themes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 19 participants were interviewed, including 11 undergraduate medical students and 8 teachers. The participants expressed a consensus on the ethical challenges arising from the use of GenAI for academic assignments. Following a thematic analysis, three themes were identified: (1) Subversion posed by GenAI, (2) Limitations and potential risks of using GenAI, and (3) Coping strategies in response to utilisation of GenAI.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe integration of GenAI in education has raised significant academic and ethical concerns. However, existing regulatory policies and student evaluation mechanisms have yet to adapt to the ethical challenges posed by GenAI. Therefore, strategies such as implementing academic integrity policies for GenAI-assisted assignments, establishing transparent oversight mechanisms, and promoting GenAI literacy education should be adopted to address these issues.\u003c/p\u003e","manuscriptTitle":"Undergraduate medical students’ and teachers’ perspectives on ethical challenges and coping strategies of using generative artificial intelligence for academic assignments: A qualitative study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 08:17:17","doi":"10.21203/rs.3.rs-6803964/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7942e149-564e-4625-8e48-1146d601d3f7","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-12T18:09:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 08:17:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6803964","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6803964","identity":"rs-6803964","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}