Generative AI · Education leadership

Actionable companion · Governance, equity and accountability

From evidence to district action: AI-supported case-based learning in medical education: a comprehensive 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:

Introduction Recent literature indicates that generative artificial intelligence (GenAI) is also being integrated into case-based learning (CBL) through activities such as clinical case generation, clinical reasoning support, and structured feedback. However, the evidence about GenAI’s role in CBL remains fragmented. Given the diverse nature of available GenAI-CBL studies, we conducted a comprehensive scoping review to map and synthesize the evidence in this area, highlighting key themes, outcomes, challenges, and limitations to inform future research, curriculum development, and policies. Method A comprehensive search was performed across multidisciplinary databases, including PubMed, ERIC, Scopus, Web of Science, and Google Scholar, covering publications from 2019 to 2025. Title and abstract screening, followed by full-text review and data extraction, were conducted independently by two reviewers using predefined eligibility criteria. The data synthesis involved thematic analysis to create an evidence map of GenAI-supported case-based and case-anchored learning in medical education. Results The findings were organized into six key themes that showcase the role of GenAI in enhancing case-based learning, covering areas such as clinical reasoning and contextual thinking; efficient and scalable case creation; learner engagement, motivation, and perceived usefulness; accuracy, reliability, and ethical issues; faculty adaptation and pedagogical integration; and hybrid and reflective learning methods. Conclusion Overall, the evidence indicates that GenAI can effectively support CBL in medical education, especially during early and intermediate stages. It also highlights the ongoing importance of faculty oversight and the need for further research to address advanced clinical judgment and ethical reasoning. Systematic review registration https://doi.org/10.17605/OSF.IO/28E3G, identifier (28E3G).

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. Convene a teacher, student, family and technology working group. Test one proposed use against a documented privacy, accessibility, bias and human-review checklist before authorizing a pilot. 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. Track unresolved risks, subgroup access, complaint resolution time and the proportion of outputs that require substantive human correction. 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

Syed Hani Abidi; Joseph Almazan; Olaoluwa Fabiyi; Fatin Zehra; Muhammad Tariq. AI-supported case-based learning in medical education: a comprehensive scoping review. Frontiers in Medicine (2026-03-25). https://doi.org/10.3389/fmed.2026.1798097

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.