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Evaluating AI-Generated Geriatric Case Studies for Interprofessional Education: Systematic Analysis Across 5
Nicole Ruggiano1, Sudikshya Sahoo1, Ava Brashear2
1School of Social Work, University of Alabama, Tuscaloosa, AL, United States.
JMIR Medical Education
|January 30, 2026
Summary
Generative AI platforms show varied performance in creating geriatric simulation case studies for health professions education. ChatGPT performed best, while Grok scored lowest, highlighting the need for careful AI tool selection and prompt engineering.
Area of Science:
- Medical Education Technology
- Artificial Intelligence in Healthcare
- Simulation-Based Learning
Background:
- Simulation-based learning (SBL) is crucial for healthcare professional training.
- Developing diverse SBL scenarios is time-consuming for educators.
- Generative AI offers potential for efficient case study creation.
Purpose of the Study:
- To generate geriatric case scenarios using five AI platforms.
- To systematically evaluate AI-generated cases for quality, accuracy, and bias.
- To compare the performance of different AI platforms in creating interprofessional education materials.
Main Methods:
- Ten geriatric case studies were generated per AI platform (N=50 total).
- Platforms included ChatGPT, Claude, Copilot, Gemini, and Grok.
- Cases were evaluated using the Simulation Scenario Evaluation Tool (SSET) by experienced SBL educators.
Main Results:
- Case quality varied significantly across and within AI platforms.
- ChatGPT yielded the highest overall scores (mean 3.27), Grok the lowest (mean 1.61).
- AI platforms excelled at generating learning objectives but struggled with supplies/materials descriptions.
Conclusions:
- This study provides the first systematic evaluation of generative AI for SBL case generation.
- Findings offer evidence-based guidance for educators selecting and using AI tools.
- Effective prompt engineering is key to enhancing AI-generated SBL resources for interprofessional education.
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