Synopsis: This study examines students’ engagement with Gen‑AI and the influences shaping their views on its academic use, using a semi‑structured questionnaire. Initial findings reveal strong social adoption of AI tools but notable reluctance to use them in formal assessments.
Background:
Generative artificial intelligence (Gen-AI) tools have introduced a novel approach for students studying biomedical science courses to interact with academic material, create academic content and aid in research and writing assignments. Nonetheless, the impact of these Gen-AI tools in student education remains ambiguous. AI tools, as education aids, offer substantial advantages to students, but they also pose a risk of (a) impeding development of vital academic skills, (b) academic misconduct, (c) unoriginal and potentially biased work, (d) inaccuracies in output responses, and (e) unfair advantages for students with access to the technology (Iwasawa et al., 2023; Mortlock and Lucas, 2024). In fact, it challenges the established norms of academic integrity. Though many higher education institutions are setting integrity guidelines and policies for Gen-AI usage, there is a mixed attitude toward their use amongst students (Lim et al., 2023; Su and Yang, 2023). Beyond formal education, Gen-AI is increasingly embedded within social media platforms and everyday digital interactions, blurring boundaries between academic and social use. Normalisation of Gen-AI within daily digital life further complicates students’ perceptions of appropriate academic use, as tools experienced as benign or beneficial in social contexts are re-encountered as contested technologies within assessment settings.
In biomedical/medical education, this tension is particularly consequential, as the development of professional competencies relies not only on knowledge acquisition but also on critical thinking, clinical reasoning, and ethical judgement. Overreliance on Gen-AI for tasks such as summarising complex biomedical concepts, drafting laboratory reports, or generating clinical case responses may inadvertently limit students’ deeper cognitive engagement and hinder the formation of essential diagnostic and decision-making skills (Gram et al., 2025; Khakpakiz, 2025). For academics, this creates increasing challenges in designing authentic assessments that both harness the pedagogic potential of GenAI and safeguard academic standards, while also requiring continuous adaptation of teaching strategies, assessment literacy, and policy enforcement (Saroha, 2025). At professional level, these dynamics raise broader concerns regarding the preparedness of future biomedical scientists and healthcare practitioners, particularly in relation to accountability. transparency, and the ethical use of AI-assisted research outputs, clinical documentation, and patient communication.
Consequently, the misalignment between everyday Gen-AI use and academic expectations risks not only undermining academic integrity but also shaping emerging professional norms in ways that may have long-term implications for trust, safety, and ethical practice within the biomedical and healthcare professions. Consequently, understanding how UK-domiciled and international students perceive, negotiate, and operationalise Gen-AI use is critical for developing equitable, culturally responsive institutional policies that balance innovation, learning support, to maintain academic integrity.
Aims and Objectives
The study aims to examine how biomedical science students use GenAI for learning and assessment and how cultural, educational, and social factors shape their engagement and perceptions of appropriate academic use. This presentation will cover key findings from our survey response comparing different group of students to a) extent of Gen-AI use in social activities and in assessment; b) perceptions of Gen-AI use and its benefits and drawbacks for learning; c) does usage of Gen-AI change the way they approach assessments and social interactions; and d) type of support they have received from the department in the use of Gen-AI with integrity, upholding medical ethics
Methodology
Following University of Derby’s Research Ethics Committee approval (ETH2324-5132), students enrolled in biomedical science course were recruited and surveyed on Gen-AI use, in their (a) social interactions, and (b) academic learning and assessments. Comparative, descriptive and Pearson correlation were used to analyse semi-structured question data.
Results
Out of 30 respondents, 73% were females and 27% males. There were 53%-home and 47%-international students. Of these, 63% completed most of their education in UK, compared to 37% who studied outside of UK. About 87% of this cohort overwhelmingly claimed not using Gen-AI tools in assessments prior to university (Home-53%, International-34%) with increased usage post-enrolment (Home-37%, International-20%). While 80% of participants were familiar with various chatbots and AI-generated text used in social contexts (Home – 53%, International – 27%), they were more evenly divided in their likelihood of using such tools themselves (Home – with 56% identifying as non-users and 44% as frequent users). When asked, ‘How much they trust AI tools?` 70% of participants said they trust with caution, compared to 20% who distrust and 10% were neutral. Surprisingly, 67% of them said that Gen-AI improves their engagement with academic sessions, but a similar percentage of students said that it hinders their ability to critically think. The data suggests that institutional context, peer practices, and perceived Academic pressures may normalise AI-assisted work in ways that are not always aligned with integrity policies. Students may not initially perceive Gen-AI use as misconduct, particularly when such tools are already embedded in their everyday social and digital lives, yet their application in assessments raises concerns about authorship, transparency, and fairness.
Discussion and Conclusions:
This is a survey-based study to understand student perception of using GenAI. Although there is a vast array of similar studies being carried out, authors believe in the importance of subject -specific context. From a medical ethics perspective, these findings are particularly significant. Biomedical and healthcare education is grounded in principles such as honesty, accountability, and professional responsibility. These values underpin patient trust and safe clinical practice. The observation that 67% of students believe Gen-AI enhances engagement, while a similar proportion report diminished critical thinking, signals a potential erosion of reflective judgement and independent reasoning, both of which are essential for clinical decision-making and evidence-based practice. Furthermore, the cautious trust expressed by 70% of participants reflects an awareness of potential risks yet may also indicate an overreliance on tools whose outputs are not always accurate or verifiable, raising parallels with issues of misinformation and uncritical acceptance of evidence in clinical contexts. Further analysis is currently being carried out which will provide valuable insights into how diverse student populations undertaking biomedical science education understand, utilise and are impacted by AI tools in academic and social environments. The insights from the study would help us to devise fair, transparent and more effective assessment methods in biomedical science to minimise misuse of GenAI.
References:
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