Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: AI in medical and dentistry education: perspectives from international students, educators and physicians

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education, Adult & professional learning. Health-professions learning must be distinguished from K–12 instruction.

Original authors’ abstract, reproduced under the linked source license:

Abstract Background Artificial intelligence (AI) is being integrated into higher education in an increasing rate, offering opportunities for personalized learning, efficiency, and enhanced engagement. However, most research focuses on single-institution or national contexts and primarily examines medical students through quantitative methods. Little is known about how international medical and dentistry students, educators, and practising physicians perceive and experience AI in medical and dentistry education. This study addresses this gap by capturing the perspectives of these diverse stakeholders, providing a comprehensive view of AI adoption, readiness, and ethical considerations across international contexts. Methods This cross-institutional mixed-methods study combined survey and interview data to explore perspectives on AI in medical and dentistry education. An online questionnaire was distributed to medical and dentistry students at the University of Pécs Medical School, Hungary and Oslo New University College, Norway. Semi-structured interviews were conducted with educators (n = 6) and physicians (n = 3) in October, 2024. Quantitative data were analyzed using descriptive statistics and inferential analyses to examine differences across participant groups and qualitative data were examined thematically and comparatively across educators and practising physicians. Results A total of 344 medical and dentistry students completed the survey. The majority of students (75.3%) reported that AI tools enhanced their learning and found chatbots beneficial for assignments (73.5%). AI was viewed as helpful for understanding complex concepts (65.1%) and managing study time (42.1%). However, concerns included data privacy (48.9%), reduced critical thinking (58.7%), and over-reliance on AI (74.7%). Comparative analysis revealed differences in emphasis across stakeholder groups, with students focusing on practical learning benefits and educators and practising physicians prioritizing long-term professional and ethical implications. Across groups, there was consensus that AI should supplement but not replace traditional and clinical teaching. Conclusions AI is perceived as a valuable supportive tool in medical and dentistry education, particularly by students, while educators and practising physicians adopt a more cautious stance emphasizing ethical, pedagogical, and clinical considerations. Effective integration of AI requires balanced implementation, including AI literacy for students, targeted faculty development, and clear institutional and ethical frameworks to safeguard critical thinking and professional competencies. Supplementary Information The online version contains supplementary material available at 10.1186/s12909-026-08886-5. Keywords: Artificial intelligence (AI), Medical education, Dentistry education, International medical and dentistry students, Chatbots, ChatGPT, Generative AI, Ethical guidelines, Curriculum development, Stakeholder perspectives

What kind of evidence is this?

Conversational & pedagogical agents · Qualitative / mixed methods

Stage: Higher education; Adult & professional learning. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

This study employed a cross-institutional, mixed-methods design conducted across two higher education institutions: the University of Pécs Medical School (UPMS), Hungary, and Oslo New University College (ONH), Norway. It integrated quantitative survey data, qualitative interview data, and insights from the existing literature. This mixed-methods designs enable triangulation, combining numerical trends with contextual insights to achieve both breadth and depth [12].

Limitations and uncertainty · source passage

These insights indicate consensus that AI should play a complementary rather than substitutive role in medical training. Both faculty and students valued its theoretical utility but emphasized that essential clinical competencies, empathy, communication, and judgment, must be developed through real-world experience.

Design the system before the pilot

Define whether the agent asks questions, gives hints, explains or assesses. Test hallucinated feedback, excessive praise, misleading confidence and responses when the learner is confused. A conversational interface alone does not establish autonomous agency.

  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Timea Németh; Shaya Irandoust; Anna Dávidovics. AI in medical and dentistry education: perspectives from international students, educators and physicians. BMC Medical Education (2026-02-25). https://doi.org/10.1186/s12909-026-08886-5

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.