Actionable companion · Governance, equity and accountability

From evidence to district action: How to Teach Generative Artificial Intelligence in Undergraduate Medical Education

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 Generative artificial intelligence (AI) refers to computational systems capable of analysing data, recognising patterns and generating outputs that may support decisions. In healthcare, AI has the potential to improve diagnostic accuracy and provide clinical decision support. As AI becomes ubiquitous in clinical workflows, clinical teachers must be prepared not only to use AI tools but also to interpret, appraise and oversee their outputs safely and effectively. However, evidence indicates that medical curricula have not kept pace with technological adoption; structured AI education remains sparse or inconsistent across institutions. To address this gap, educators must define what medical students should learn about AI and how to teach it. Whereas existing literature defines what learners should know about AI, our work provides a pragmatic framework for how they should learn to use it in practice. By integrating verification, critical appraisal and ethical reflection into everyday clinical teaching, our workflow offers a scalable and adaptable model for preparing future clinicians to engage safely and responsibly with generative AI. Keywords: artificial intelligence, learning modules, undergraduate medical curriculum

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. 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.
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. 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

Eva Feigerlova. How to Teach Generative Artificial Intelligence in Undergraduate Medical Education. The Clinical Teacher (2026-06-01). https://doi.org/10.1111/tct.70420

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.