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Integrating Generative AI Into Clinical Reasoning Education for Medical Students: Mixed Methods Study
Cheng-Heng Liu1,2, Yu-Ting Chen3,4, Chiun Hsu5
1Department of Medical Education, National Taiwan University Hospital, No. 7, Zhongshan South Road, Zhongzheng District, Taipei City, 100, Taiwan, 886 223123456 ext 61426.
JMIR Medical Education
|August 13, 2026
Summary
Medical students showed improved AI literacy and collaboration skills after a workshop integrating Generative AI (GenAI) with clinical reasoning. The intervention focused on prompt engineering and verification, enhancing readiness for AI in healthcare.
Area of Science:
- Medical Education
- Artificial Intelligence in Healthcare
- Clinical Reasoning
Background:
- Generative AI (GenAI) integration in clinical settings is rising.
- Medical students often lack essential GenAI competencies like prompt design and output verification.
- Limited educational strategies exist for integrating GenAI with clinical reasoning.
Purpose of the Study:
- To evaluate a workshop designed to enhance medical students' AI literacy and collaborative attitudes.
- To assess the impact of GenAI and clinical reasoning integration on self-perceived AI literacy.
- To determine if patient-centered orientation changes after intensive AI exposure.
Main Methods:
- A single-group, pre-post mixed-methods study with fifth-year medical students.
- A 3-hour workshop included modules on clinical reasoning, bias awareness, and prompt engineering using ChatGPT.
- Quantitative data from AI-literacy, patient-centered orientation, and collaborative learning scales were collected, alongside qualitative interviews and reflective narratives.
Main Results:
- Significant improvements in self-perceived AI literacy across all domains (Cohen d=0.69-0.93).
- Substantial increase in collaborative learning attitudes (d=0.94).
- No significant change in patient-centered orientation, though this finding is inconclusive due to measurement and power limitations.
Conclusions:
- A brief, theory-informed intervention significantly improved AI literacy and collaborative attitudes in medical students.
- Embedding AI verification within clinical reasoning offers a scalable approach for responsible human-AI collaboration.
- Further research with comparative designs and performance-based assessments is recommended.