Actionable companion · Motivation & engagement

Learning with agency: A Case-Based Framework for Teaching Artificial Intelligence Applications in Medical Education

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.

Read original paper Search paper agent CC BY 4.0

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. Health-professions learning must be distinguished from K–12 instruction.

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

Abstract Introduction Artificial intelligence (AI) and machine learning (ML)–based tools are emerging across many areas of medicine. The next generation of medical professionals must be prepared to critically assess the tools they will use, including how they work and the ethical or legal implications of their implementation. To address this need, we developed a generalizable case-writing framework that enables educators and learners to engage with clinical cases integrating AI/ML concepts, clinical reasoning, and ethical considerations. Methods We designed a case-writing template to compose clinical case scenarios that integrate clinical, technical, and ethical/legal learning objectives relating to AI applications in medicine. Medical students used this template to construct 3 cases. Student-authored cases were presented to a broader audience in 1-hour sessions: first as a pilot event, then as a class-wide lecture for 140 fourth-year medical students in a transition-into-residency course. Results After the sessions, student audiences had a statistically significant increase in familiarity with key AI terminology, a greater appreciation for the importance of ethical considerations of AI applications in health care, and an increased perceived importance of integrating AI-related topics into the medical curriculum. Discussion A case-based framework provides a scalable approach to integrating AI education into existing curricula by connecting clinical scenarios with technical concepts and the ethical considerations of AI adoption; cases offer a familiar format that helps learners understand AI applications in patient care while exploring implementation challenges. The case-writing template and 3 sample cases are provided as adaptable resources for educators seeking to incorporate AI literacy. Keywords: artificial intelligence, machine learning, case-based learning, computer vision, pathology, radiology, primary care, ethics/bioethics

What kind of evidence is this?

Supporting AI learning science · Design requires full-text appraisal

Stage: Higher education. 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

We developed a case-based instructional framework designed to help medical students critically evaluate AI applications within clinical contexts. This format enables integration of clinical learning points relevant to the medical curriculum with AI-specific technical and ethical learning points.

Limitations and uncertainty · source passage

The case-writing process itself provides a nuanced lens for studying how AI is being applied in health care. For instance, as authors of this case series and article, the process of building case-based content toward AI applications in medicine helped us appreciate a key distinction among the tools we explored: screening models, such as those used in diabetic retinopathy detection, are designed to make referral recommendations without producing outputs intended for clinical interpretation. This contrasts with clinical decision support tools, such as algorithms for tumor grading, which assist clinicians through image annotation intended to aid in interpretation. Viewing these technologies through a case-based lens made this distinction more tangible to us and emphasized how context shapes the design and intended use of AI systems in clinical care.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

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

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. 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.
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. 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

Rafael C Schulman; Ali Shervin Razavi; Céline Hardy; Prithi Chakrapani; Abdel Rhman Daoud; Mitchell Mazza; Ankita Saxena; Jason Grullon; Philip J Gary; Frank Middleton; Sriram Narsipur. A Case-Based Framework for Teaching Artificial Intelligence Applications in Medical Education. MedEdPORTAL : the Journal of Teaching and Learning Resources (2026-10-27). https://doi.org/10.15766/mep_2374-8265.11644

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.