Evaluating AI-Generated Geriatric Case Studies for Interprofessional Education: Systematic Analysis Across 5 Platforms
This study is the first to systematically evaluate and compare multiple generative AI platforms for case study generation using a validated assessment tool (SSET) and provides evidence-based guidance on selecting and using AI tools effectively. The findings offer practical direction for educators navigating available generative AI tools to enhance training for health care professionals, including specific strategies for prompt engineering that can improve the quality of SBL resources in interprofessional education. These insights enable educators to leverage AI capabilities while maintaining pedagogical rigor.
Authors
- Robert McKinney (ORCID: https://orcid.org/0000-0002-4363-1510)
- Uche Louisa Nwatu (ORCID: https://orcid.org/0000-0002-6907-808X)
- Hyunjin Noh (ORCID: https://orcid.org/0000-0002-6503-3597)
- Nicole Ruggiano (ORCID: https://orcid.org/0000-0002-2398-7077)
- Heather S. Cole (ORCID: https://orcid.org/0000-0001-9081-5681)
- Ellen L. Brown (ORCID: https://orcid.org/0000-0002-2418-3257)
- Suzanne S. Prevost (ORCID: https://orcid.org/0000-0002-2340-2809)
- Amie Brunson
- Sudikshya Sahoo
- C Victoria Framil Suarez
- Ava Brashear
Institutions
- University of Alabama (US)
- Florida International University (US)
Publication Details
- Journal
- JMIR Medical Education
- Published
- 2026-01-30
- DOI
- https://doi.org/10.2196/83085
- Primary Topic
- Simulation-Based Education in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00