Disabilities and Generative Artificial Intelligence Education in Rheumatology Fellowship: Educational Module and Simulation Exercise
Authors' conclusion
Our educational materials teach FITs to incorporate genAI into writing letters for accommodation and addressing disparities from medical disabilities.
Study context
- Population
- Rheumatology fellows-in-training18 completed the Likert scales
- Participants
- 23 participants
- Exposure
- FITs' confidence before vs after an online module and simulation activity writing letters for accommodation using generative artificial intelligence (genAI)
- FITsn = Twenty-three
- Outcome
- FITs' confidence implementing disability advocacy and genAI skills (retrospective pre-post Likert scales)
Methodological detailsonline module and simulation activity, retrospective pre-post Likert scales, Wilcoxon signed rank tests
- Online module and simulation activity
- Retrospective pre-post Likert scales
- Wilcoxon signed rank tests
Main finding
As reported in the abstract
Confidence (retrospective pre-post Likert scales)
- FITs' confidence significantly improved across all objectives, most notably in engineering genAI prompts and finalizing letters drafted with genAI
- P < 0.0001
Observational data: associations, not effects.
Further findings
| Outcome | Values |
|---|---|
| Skills scored by educators against a rubric | FITs average 83.83% · Engineering genAI prompts (strongest skill) 95.45% · Differentiating scope of practice between rheumatology providers and disabilities professionals (lowest skill) 74.55% |
Original publication
Disabilities and Generative Artificial Intelligence Education in Rheumatology Fellowship: Educational Module and Simulation Exercise. Arthritis Care Res (Hoboken). Published 26 February 2026. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →