Actionable companion · Assessment & reasoning

Learning with agency: Advancements in artificial intelligence transforming medical education: a comprehensive overview

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): Age / stage not established. 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 revolutionizing medical education by introducing innovative tools and reshaping traditional teaching and learning methods. AI technologies such as virtual and augmented reality, adaptive learning platforms, and AI-driven assessments are increasingly recognized for their potential to enhance diagnostic precision, clinical decision-making, and personalized learning experiences. Objective: This narrative review explores the current trends, challenges, and innovations associated with the integration of AI in medical education. It aims to critically examine how AI transforms teaching and learning processes while addressing ethical concerns and practical barriers. Methods: We performed a systematic literature search across three major databases (PubMed, Scopus, and Web of Science) for publications dated 2010–2024. Our search strategy employed key terms including ‘artificial intelligence,’ ‘medical education,’ and ‘AI-based learning platforms’ to identify relevant peer-reviewed articles, review papers, and case studies. After screening and selection, 67 studies met our inclusion criteria for final analysis. Results: AII technologies improve learning outcomes by creating personalized, immersive, and interactive environments. They support clinical decision-making and procedural skills training while addressing diverse learner needs. However, ethical issues like data privacy, algorithmic biases, and equitable access, coupled with challenges like faculty resistance and technological infrastructure gaps, limit broader adoption. Conclusion: AI is an important tool in medical education, offering significant opportunities to enhance learning outcomes and bridge educational gaps. However, its successful integration requires ethical frameworks, faculty training, and equitable resource allocation. A balanced approach that combines technological innovation with human-centered pedagogy is essential to preserve empathy and ethical care in healthcare. KEYWORDS: Artificial intelligence, medical education, personalized learning, virtual reality, ethical considerations, adaptive learning platforms

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Evidence synthesis

Stage: Age / stage not established. 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 systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines to evaluate the integration of artificial intelligence (AI) in medical education. The research questions were iteratively refined during the review process to ensure alignment with emerging evidence. The Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) checklist was used to guide the development of the review protocol, ensuring transparency and completeness in planning.

Limitations and uncertainty · source passage

This study incorporates a diverse range of academic sources to ensure a comprehensive analysis. While the majority of the references consist of peer-reviewed journal articles, which provide rigorous and validated insights, a limited number of conference papers have also been included. Conference articles were selectively utilized to incorporate emerging trends, preliminary findings, or niche perspectives that have not yet been published in full-length journal formats. However, greater emphasis has been placed on established scholarly literature to maintain the robustness and reliability of the research.

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

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.
Co-design accommodations with learners and specialists. Check access and error burden across groups without inferring subgroup effectiveness from small or unreported samples.

A 90-day district pilot

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.
Days 16–30 · Design. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. Name an accountable owner and set the baseline, success threshold and budget before implementation.
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.
Days 61–90 · Decide. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. 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

Aliasghar Khakpaki. Advancements in artificial intelligence transforming medical education: a comprehensive overview. Medical Education Online (2025-08-12). https://doi.org/10.1080/10872981.2025.2542807

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.