Aug 2026· Journal of Medical Education and Family Medicine· Vol 3, pp. 44-53· 0 citations· 18 references
TL;DR
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.
Abstract
AbstractObjective: The integration of artificial intelligence (AI) into medical education is rapidly increasing; however, gaps remain in terms of learners’ knowledge and ethical awareness. This study aimed to evaluate baseline perceptions of AI among medical students and faculty members and to assess the impact of a structured educational intervention on students’ knowledge, attitudes, and ethical awareness.Method: This study employed a single-group pretest–posttest design. Initially, a 24-item questionnaire was developed on the basis of a comprehensive literature review and administered to medical students (n = 123) and faculty members (n = 17) to assess baseline perceptions. Following an expert review involving medical education specialists from three universities, the instrument was refined to a 12-item artificial intelligence awareness questionnaire. The finalized questionnaire was administered as both a pretest and posttest to 143 medical students. After the educational intervention, 123 students completed both assessments and were included in the paired analyses. Data were analyzed using paired samples t tests, and effect sizes were calculated using Cohen’s d.Results: At baseline, both students (mean = 3.55 ± 0.58) and faculty members (mean = 3.66 ± 0.53) demonstrated moderate levels of AI awareness. Knowledge of fundamental AI concepts was relatively limited, while attitudes toward the future importance and educational utility of AI were highly positive. Following the intervention, statistically significant improvements were observed across all knowledge and awareness domains (P < .001). The greatest gains were observed in understanding AI concepts, applications in medical education, and ethical considerations. The overall questionnaire score significantly increased (mean difference = -1.01, t(122) = -11.745, P < .001), indicating a strong positive effect of the intervention. However, the participants’ perceived need for further AI training remained high and did not significantly change (P = .060).Conclusion: 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. The educational intervention significantly improved AI-related knowledge and ethical awareness. These findings highlight the importance of integrating structured AI education into medical curricula to support the responsible and effective use of emerging technologies.
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
Artificial intelligence (AI), treated in this study as an umbrella term for AI-enabled clinical and educational technologies rather than as a single platform, is reshaping medical education, including how diagnostic skills, treatment planning, and patient care are taught. This study examines AI integration in medical education through the perceptions and readiness of clinical educators. Guided by the Unified Theory of Acceptance and Use of Technology, the study explores factors influencing AI adoption in medical training, including performance expectancy, effort expectancy, social influence, and facilitating conditions. In this exploratory study, semi-structured interviews were conducted with 15 clinical educators in the south-central United States who supervise third-year medical students. Findings suggested six recurring themes: the technological learning curve, the need for hands-on learning, institutional support, mentorship, preservation of human elements, and generational differences in comfort with AI. While some AI-enabled applications may support adaptive and personalized learning, educators expressed concerns about maintaining empathy, patient interaction, and human-centered care. The findings suggest that effective AI integration may require strategic institutional support, ongoing training, and pedagogical change. This study provides insight into developing AI-ready medical education models that balance technical competence with humanistic values.
T. Murphy, Ginger Vaughn, Rob E. Carpenter et al.· International Medical Educat...· 0 citations
Undergraduate medical students show moderate awareness of AI in healthcare but lack formal training and in-depth understanding, highlighting the need for structured AI education within medical curricula and further research on its long-term impact.
Sonali Sharma, Smriti Kayat, N. Saboo et al.· Nigerian Medical Journal· 0 citations
Artificial intelligence (AI) is increasingly implemented in medical education, adding new opportunities to improve learning in complex subjects like anatomy. This study assessed the perceived effectiveness of AI-powered tools in undergraduate anatomy education by evaluating the content validity of AI-generated materials, the student-perceived quality of generated questions, and students' perceptions of AI use. A cross-sectional, multi-phase mixed-methods study was conducted at the College of Medicine and Health Sciences, Sultan Qaboos University, Oman (September to December 2025). Three aims were addressed: (1) expert content validity review of AI-generated heart anatomy materials; (2) students' evaluation of AI-generated MCQs across clarity, difficulty, scientific accuracy, and educational utility; (3) a structured survey examined students' perceptions, usage patterns, and attitudes toward AI tools in anatomy education. Findings were interpreted using three complementary frameworks. These linked the study aims to technical quality, psychometric assessment, educational value, ethical considerations, and human-involved review process. AI-generated anatomy content showed favorable, small-panel-dependent validity (CVR = 0.933; 90% above threshold). Students rated the AI-generated MCQs positively, especially for clarity and scientific accuracy, while difficulty scored lowest. AI was widely used for concept clarification and self-testing. Students found it helpful and time-saving, but reported only moderate confidence in its accuracy, limited AI competence, and concerns about ethical use and long-term retention. When mapped to the three frameworks, the findings indicate favorable expert-rated content validity (Roveta), positive learner responses, and indirect learning gains (Kirkpatrick Levels 1-2). They also highlight the need for a human-in-the-loop workflow (QUEST-AI). AI-powered tools can support anatomy education by improving efficiency, engagement, and self-directed learning. However, they should complement rather than replace traditional teaching. Structured guidance and expert validation of generated content are essential to ensure safe and effective integration into medical curricula.
Ayisha Almamari, Mohamed Al-Farsi, Abdul-Rahman Almamari et al.· Clinical anatomy (New York,...· 0 citations
The potential and challenges of using Generative artificial intelligence (GAI) in medical education are widely discussed, yet its use by medical students and faculty remains under-researched in the UK context. This exploratory sequential mixed-methods pilot study empirically investigates and compares student and faculty experiences and perceptions of using GAI in medical education. A questionnaire was developed based on focus groups and administered to undergraduate medical students and faculty within a UK medical school. Descriptive analysis and comparative analysis were used to quantify and compare student (n = 29) and faculty (n = 32) experiences and perceptions of GAI. Reflexive thematic analysis of open-ended question responses was undertaken to complement quantitative results. We found that GAI is being used for a variety of purposes by students and faculty in learning/teaching/assessment. However, both of them generally have relatively low self-confidence in GAI-related knowledge and skills. Faculty members tend to have stronger concerns of GAI limitations and its ethical challenges. Males are more confident in using GAI and have more positive attitudes towards GAI than females. The findings also provide preliminary evidence for the importance of open communication of GAI between students and faculty and the need for schools’, universities’, and national regulators’ strategic support to ensure faculty and student ethical and effective use of GAI and equitable access to GAI technology between subgroups. Our findings and the pilot tools can inform future research investigating the use of GAI in medical education in other institutional context or at a larger scale.
Hui-Ming Ding, M. Homer· Discover Education· 0 citations
BACKGROUND
The application of artificial intelligence (AI) in simulating detailed patient-doctor interactions for objective structured clinical examinations (OSCEs) remains emerging. This study aimed to evaluate an AI virtual patient (AIVP) innovation designed to support medical education through interactive patient simulations and feedback.
METHOD
This prospective mixed-methods pilot recruited final-year medical students during their critical care term. Two cohorts were examined: a volunteer AIVP group (n = 43) and an educational-support medical education enhancement training (MEET) group (n = 8). Participants completed pre- and postintervention OSCEs scored across communication, history and examination, management, and overall outcome domains. Students accessed nine emergency medicine scenarios featuring AI-driven dialogue, speech recognition, avatar interaction, and immediate feedback. Quantitative outcomes were analyzed using the Wilcoxon signed-rank test. Postintervention surveys (Likert scale) and focus groups provided qualitative data, which were analyzed thematically.
RESULTS
In the AIVP cohort, descriptive improvements were observed across domains; however, Wilcoxon testing identified no statistically significant changes. MEET students demonstrated significant improvement in overall OSCE outcomes (median 37 [IQR: 30-67] vs 67 [IQR: 47-75]; P = 0.03), with positive trends in all domains. Survey responses (n = 25) showed high agreement that AIVP enhanced confidence in history-taking (83%), differential diagnosis (78%), and management planning (78%). Thematic analysis from 10 students demonstrated that AIVP was experienced as a structured, exam-focused, and confidence-building learning tool that addresses gaps in workplace-based learning.
CONCLUSION
AIVP was feasible and well received, providing practice aligned with university OSCE standards. While the volunteer cohort showed modest gains, the MEET group demonstrated significant improvement, suggesting benefit for students needing structured support. Tailored feedback and alignment with OSCE standards highlight AIVP as a promising adjunct for training.
Betty S Chan, J. Marjot, Tim Dodds et al.· Proceedings· 1 citation