How Medical Students Use and Perceive Generative Artificial Intelligence for Learning and Assessment: A Cross-Sectional Study at a Regional Australian Medical School
Aug 2026· International Medical Education· Vol 5, pp. 89· 0 citations· 41 references
TL;DR
Although uptake was high, trust was moderate, and students expressed concerns about professionalism and critical thinking, medical schools should provide explicit guidance on acceptable use, incorporate artificial intelligence literacy training and ethical use guidelines, and redesign assessment to protect the skills students perceive to be most at risk.
Abstract
Generative artificial intelligence has been rapidly adopted by university students, yet there is limited evidence describing how and why medical students use it, particularly in regional settings. This cross-sectional study examined the use of generative artificial intelligence for learning and assessment among medical students at James Cook University, a regional Australian medical school with three North Queensland campuses. All enrolled students (Years 1–6) were invited to complete a 24-item online survey; closed-ended items were analysed using frequency and bivariate analyses by year level and gender, with correction for multiple comparisons, and open-ended responses were analysed using qualitative content analysis. In total, 438 students responded. Eighty percent reported using artificial intelligence for their studies or assignments, and 95% supported its use in medical education in some capacity. The most common uses were explaining concepts (56%) and answering medical content questions (54%). Pre-clinical students reported greater study-related use, whereas clinical year students reported greater assessment-related use. Male students also reported higher levels of use, willingness to pay, and trust in AI tools. Although uptake was high, trust was moderate, and students expressed concerns about professionalism and critical thinking. Medical schools should provide explicit guidance on acceptable use, incorporate artificial intelligence literacy training and ethical use guidelines, and redesign assessment to protect the skills students perceive to be most at risk.
ABSTRACT Introduction: Artificial Intelligence (AI) has been progressively incorporated into medical education, modifying how knowledge is accessed and utilized. Objective: To analyze medical students’ perceptions regarding the use of Artificial Intelligence in the learning process. Methods: A descriptive, cross-sectional study with a mixed-methods approach was conducted with 108 medical students. Data were collected through an online structured questionnaire and analyzed using descriptive statistics and thematic content analysis. Results: Most participants (97.2%) reported using AI tools, with ChatGPT being the most frequently used (95.4%). The main applications included tutoring (84.3%), academic activities (65.7%), and basic disciplines, particularly anatomy/morphofunctional sciences (50.9%) and physiology (41.7%). Reported benefits included improved understanding of complex content (85.2%), time optimization (78.7%), and rapid access to information (63%). Concerns were identified regarding information reliability, algorithmic bias, and technological dependence. Most students (79.6%) showed a preference for hybrid learning methods. Conclusion: Artificial Intelligence is widely integrated into students’ academic routines and is perceived as a complementary tool whose use requires a critical approach.
Isabel Mitsu Brito Kanashiro, Naudia da Silva Dias, Ana Cecília Perotes Albuquerque et al.· Revista Brasileira de Educaç...· 0 citations
Although a growing body of research has examined students’ attitudes toward generative artificial intelligence (GenAI) in higher education, few studies have compared perceptions across contrasting institutional contexts or explored how students’ reported uses of GenAI relate to broader learning practices. This study addresses that gap by examining university students’ perceptions, self-reported competence, and use of GenAI at two Swedish universities with different academic profiles: a technology-oriented institution and a broader multidisciplinary institution. The study is based on an exploratory questionnaire survey administered to all enrolled students at both universities, yielding 1,097 responses (University A response rate: 11.27%, University B response rate: 14.06%) from students across diverse disciplines, including engineering, nursing, and criminology. Quantitative data were analyzed using reliability analysis, exploratory factor analysis, and non-parametric group comparisons, supplemented by thematic analysis of qualitative responses. The analysis identified three reliable constructs: perceived learning benefit, perceived institutional support and integration, and self-reported technical knowledge and competence. Across both institutions, students reported generally positive attitudes toward GenAI and described using it primarily for information retrieval, text refinement, and text analysis, but also as a discussion partner or personal tutor in ways that suggest both surface-level and more dialogic forms of engagement. Comparisons between the two universities showed broad similarity across most measures, with the only statistically significant difference relating to perceived institutional support and integration, which was rated higher by students at the technology-oriented university. Students at both institutions also viewed GenAI primarily as a complement to, rather than a replacement for, traditional teaching, while reporting only moderate trust in AI-generated outputs. These findings suggest that GenAI is already embedded in students’ study practices, but that its use is largely self-directed rather than strongly shaped by institutional context. The study thus contributes comparative empirical evidence on student engagement with GenAI across contrasting higher education settings and highlights the need for pedagogical and institutional strategies that support critical, reflective, and responsible use.
Å. Nygren, Anna-Li Eriksson, Jeanette Sjöberg et al.· Education and Information Te...· 0 citations
The normalisation of АІ in academic practice indicates its role as a cognitive extension in medical education, which necessitates the development of structured educational strategies and methodological guidelines for its responsible use in medical training.
Inna I. Kucherenko, A. O. Burdeinyi, L. Lymar et al.· Polski merkuriusz lekarski :...· 0 citations
Although baseline knowledge of AI among medical students and faculty members was limited, both groups demonstrated strong positive attitudes and a clear demand for further training, highlighting the importance of integrating structured AI education into medical curricula to support the responsible and effective use of emerging technologies.
Ay Sıla Çaloğlu, Halid Durna, Zeynep Naz Ergen et al.· Journal of Medical Education...· 0 citations
BACKGROUND
Artificial intelligence (AI) has emerged as one of the most rapidly evolving technologies in recent years and is increasingly being integrated into healthcare, education, and everyday life. Examining university students' attitudes toward this technology is important for understanding their future professional orientations and adaptation to technological change. This study aimed to identify medical students' attitudes toward artificial intelligence and the determinants of these attitudes, and to develop educational implications for medical training based on the findings.
METHODOLOGY
This descriptive cross-sectional study was conducted between January 22 and May 28, 2025, among 198 final-year medical students at Pamukkale University Faculty of Medicine in Denizli, Türkiye. Data were collected using a Descriptive Information Form, which assessed students' sociodemographic characteristics and their knowledge and experiences regarding AI, and the General Attitudes toward Artificial Intelligence Scale (GAAIS, Turkish version). Since the negative subscale is reverse-coded, higher scores in both indicate more positive attitudes toward AI. Data were analyzed using SPSS v25 with descriptive statistics, Mann-Whitney U, Kruskal-Wallis, and multiple linear regression analyses.
RESULTS
The mean age of participants was 24.47 ± 0.98 years; 56.1% were female. A total of 74.2% of the participants reported general knowledge about AI, and 76.3% reported experience using AI in daily life. A total of 63.1% viewed AI developments positively, 69.7% believed AI changes work and daily life, and 52.0% felt emotionally unaffected by it. The mean positive attitude score was 45.01 ± 9.17, and the negative attitude score was 26.41 ± 6.22. Multiple linear regression analysis showed that positive attitudes toward artificial intelligence were significantly associated with father's education level (university vs. primary school), having an interest in technology, perceptions regarding developments in artificial intelligence, and the belief that AI has an emotional impact (p < 0.05). For the negative attitude subscale, only perceptions regarding developments in artificial intelligence were found to be significantly associated (p < 0.05).
CONCLUSION
Medical students demonstrated generally positive attitudes toward AI. Higher paternal education, technological interest, perceiving AI as emotionally influential, and evaluating AI developments positively were predictors of favorable attitudes. In addition, more positive evaluations of AI-related developments were also associated with higher scores on the reverse-coded negative attitude subscale, indicating lower negative attitudes toward AI. These findings suggest that students' attitudes toward AI are shaped not only by technological interest but also by perceptual factors related to AI. Therefore, integrating clinically oriented AI content and awareness-building activities into medical education may support the development of more balanced and informed attitudes toward AI.
Batuhan Horasan, A. Ergin, Eda Şenarabacı· BMC Medical Education· 0 citations
To explore how nursing graduate students in China experience and navigate generative artificial intelligence use in their research practice, including the conditions under which such use becomes dependency, a differentiated, stage-sensitive artificial intelligence governance frameworks tailored to nursing graduate education is advocated.
Furong Chen, Jingjing Cai, Shaoxue Li et al.· International Journal of Nur...· 0 citations