Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Applications, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: Scoping Review

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

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Evidence and context

Health-professions education context: test transfer to K–12 rather than assuming it.

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

Abstract Background Nowadays, generative artificial intelligence (GAI) drives medical education toward enhanced intelligence, personalization, and interactivity. With its vast generative abilities and diverse applications, GAI redefines how educational resources are accessed, teaching methods are implemented, and assessments are conducted. Objective This study aimed to review the current applications of GAI in medical education; analyze its opportunities and challenges; identify its strengths and potential issues in educational methods, assessments, and resources; and capture GAI’s rapid evolution and multidimensional applications in medical education, thereby providing a theoretical foundation for future practice. Methods This scoping review used PubMed, Web of Science, and Scopus to analyze literature from January 2023 to October 2024, focusing on GAI applications in medical education. Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, 5991 articles were retrieved, with 1304 duplicates removed. The 2-stage screening (title or abstract and full-text review) excluded 4564 articles and a supplementary search included 8 articles, yielding 131 studies for final synthesis. We included (1) studies addressing GAI’s applications, challenges, or future directions in medical education, (2) empirical research, systematic reviews, and meta-analyses, and (3) English-language articles. We excluded commentaries, editorials, viewpoints, perspectives, short reports, or communications with low levels of evidence, non-GAI technologies, and studies centered on other fields of medical education (eg, nursing). We integrated quantitative analysis of publication trends and Human Development Index (HDI) with thematic analysis of applications, technical limitations, and ethical implications. Results Analysis of 131 articles revealed that 74.0% (n=97) originated from countries or regions with very high HDI, with the United States contributing the most (n=33); 14.5% (n=19) were from high HDI countries, 5.3% (n=7) from medium HDI countries, and 2.2% (n=3) from low HDI countries, with 3.8% (n=5) involving cross-HDI collaborations. ChatGPT was the most studied GAI model (n=119), followed by Gemini (n=22), Copilot (n=11), Claude (n=6), and LLaMA (n=4). Thematic analysis indicated that GAI applications in medical education mainly embody the diversification of educational methods, scientific evaluation of educational assessments, and dynamic optimization of educational resources. However, it also highlighted current limitations and potential future challenges, including insufficient scene adaptability, data quality and information bias, overreliance, and ethical controversies. Conclusion GAI application in medical education exhibits significant regional disparities in development, and model research statistics reflect researchers’ certain usage preferences. GAI holds potential for empowering medical education, but widespread adoption requires overcoming complex technical and ethical challenges. Grounded in symbiotic agency theory, we advocate establishing the resource-method-assessment tripartite model, developing specialized models and constructing an integrated system of general large language models incorporating specialized ones, promoting resource sharing, refining ethical governance, and building an educational ecosystem fostering human-machine symbiosis, enabling deep tech-humanism integration and advancing medical education toward greater efficiency and human-centeredness.

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. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. 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, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. 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

Yuhang Lin; Zhiheng Luo; Zicheng Ye; Nuoxi Zhong; Lijian Zhao; Long Zhang; Xiaolan Li; Zetao Chen; Yijia Chen. Applications, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: Scoping Review. JMIR Medical Education (2025-10-23). https://doi.org/10.2196/71125

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.