Agentic education · Learning science

Actionable companion · Assessment & reasoning

From AI evidence to responsible practice: Integrating Generative AI Into Clinical Reasoning Education for Medical Students: Mixed Methods Study

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not findings or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

Read original paperSearch paper agentCC 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 education findings do not establish effectiveness in K–12 classrooms.

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

Abstract Background Generative AI (GenAI) is increasingly integrated into clinical learning and practice. However, medical students often lack the competencies required for safe and critical use, including prompt design, output verification, and recognition of limitations. Educational interventions that integrate GenAI with clinical reasoning frameworks remain limited. Objective This study evaluated a structured, theory-informed workshop integrating GenAI, prompt engineering, and clinical reasoning education to enhance medical students’ self-perceived AI literacy and collaborative learning attitudes, and to assess whether patient-centered orientation changed following intensive AI exposure. Methods We conducted a single-group, pre-post, explanatory sequential mixed methods study with fifth-year medical students enrolled at an academic medical center between April 2024 and May 2025. The 3-hour workshop comprised 6 modules integrating clinical reasoning, cognitive-bias awareness, verification-oriented GenAI use, and hands-on prompt engineering and centered on ChatGPT (OpenAI). Quantitative outcomes were self-reported measures from a 20-item self-report AI-literacy questionnaire adapted from the Meta AI Literacy Scale and were examined with exploratory and confirmatory factor analysis, the 6-item Patient-Practitioner Orientation Scale-Short, and a modified Collaborative Learning Attitude Scale (CLAS). Pre-post change was assessed using 2-tailed paired t tests with Benjamini-Hochberg correction and Cohen d; the Patient-Practitioner Orientation Scale-Short and CLAS were available for a subsample (n=46). Qualitative data from 6 interviews and 17 reflective narratives were analyzed using reflexive thematic analysis and integrated with the quantitative findings. Results Among 150 eligible students, 139 (92.7%) completed paired AI-literacy assessments. Self-perceived AI literacy improved across all domains (Cohen d=0.69‐0.93, all P<.001; all remaining significant after false discovery rate correction), and collaborative learning attitudes increased substantially (d=0.94, P<.001). Patient-centered orientation showed no significant change (d=0.03); however, baseline scores were concentrated at the favorable end of the scale (a floor/restricted-range effect), and the subsample analysis was underpowered (minimum detectable dz=0.42), so this null result is inconclusive rather than evidence of unchanged orientation. Gains did not differ by sex or across the sequential cohorts. Reflexive thematic analysis (interviews: n=6; reflections: n=17) identified five themes and one emergent theme describing a shift toward verification-oriented GenAI use: (1) understanding GenAI capabilities and limitations, (2) prompt-engineering skill development, (3) calibrated trust through verification, (4) GenAI-supported communication and collaboration, and (5) ethical considerations, with emerging reconceptualization of professional identity. Conclusions A brief, theory-informed educational intervention integrating GenAI with clinical reasoning was associated with medium-to-large improvements in self-perceived AI literacy and collaborative attitudes. No detectable change in patient-centered orientation was observed; however, this finding should be interpreted as inconclusive, given measurement and power constraints. Embedding verification practices within clinical reasoning frameworks may offer a scalable approach for preparing physicians for responsible human-AI collaboration. Future studies should incorporate comparative designs, performance-based assessments, and longitudinal follow-up.

What kind of evidence is this?

Supporting AI learning science · Qualitative / mixed methods

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 conducted a mixed methods educational evaluation using an explanatory sequential design (also termed sequential explanatory), in which a quantitative pre-post outcome phase was followed by a qualitative phase intended to help explain and contextualize the quantitative findings. The quantitative outcome data were collected immediately before and after the workshop, assessing changes in self-perceived AI literacy, collaborative learning attitudes, and patient-centered orientation; the qualitative interviews and reflective narratives were then collected in a subsequent phase to examine how students described their reasoning processes, trust calibration, and professional adaptation when using GenAI. The 2 strands were integrated at the interpretation stage.

Limitations and uncertainty · source passage

As GenAI tools continue to evolve and permeate clinical environments, medical education should move beyond passive exposure toward structured, theory-informed curricula that help future physicians harness AI’s potential responsibly. By interweaving technical skills with clinical reasoning and humanistic principles, medical education can prepare physicians who not only use AI but also guide its responsible implementation in patient care. This study indicates that a concise, theory-informed workshop was associated with improvements in self-perceived AI literacy and collaborative attitudes while explicitly emphasizing critical thinking and humanistic values. The remaining challenge lies in scaling such interventions while maintaining educational quality and addressing the ethical complexities associated with AI in medicine.

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.

  • 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

  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. 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.
  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 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

Cheng-Heng Liu; Yu-Ting Chen; Chiun Hsu; Huey-Ling Chen; Chih-Wei Yang. Integrating Generative AI Into Clinical Reasoning Education for Medical Students: Mixed Methods Study. JMIR Medical Education (2026-08-13). https://doi.org/10.2196/94951

Companion prepared 2026-11-01. 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.