Using participatory action research to develop an artificial intelligence-augmented, peer-driven, case-based, and simulation-enhanced curriculum for emergency medicine residents
Issued Date
2026-01-01
Resource Type
ISSN
14818035
eISSN
14818043
Scopus ID
2-s2.0-105030664644
Pubmed ID
41706282
Journal Title
Canadian Journal of Emergency Medicine
Rights Holder(s)
SCOPUS
Bibliographic Citation
Canadian Journal of Emergency Medicine (2026)
Suggested Citation
Eastwood K.W., Allali D., Leela-Amornsin S., Desbiens J.P., Szulewski A. Using participatory action research to develop an artificial intelligence-augmented, peer-driven, case-based, and simulation-enhanced curriculum for emergency medicine residents. Canadian Journal of Emergency Medicine (2026). doi:10.1007/s43678-026-01118-1 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115428
Title
Using participatory action research to develop an artificial intelligence-augmented, peer-driven, case-based, and simulation-enhanced curriculum for emergency medicine residents
Corresponding Author(s)
Other Contributor(s)
Abstract
This work describes the use of participatory action research to develop an artificial intelligence (AI)-augmented, peer-driven, case-based, and simulation-enhanced framework for senior emergency medicine trainees. It has been applied to enhance knowledge acquisition for small-group self-directed study in resuscitation medicine. Trainees engaged in structured learning cycles over 6 months, based on the principles of ‘desirable-difficulty’ and deliberate-practice. It incorporated peer-selected pre-reading, case-based discussions, high-fidelity simulations, and spaced-repetition flashcard review. A key innovation is the use of generative AI tools to supplement these activities, and follow evidence-based prompt engineering. The participants refined self-study methods through iterative evaluation. AI-generated questions facilitated retrieval-based learning, and flashcard integration enhanced knowledge retention. Simulation-based reinforcement contributed to the ‘desirable-difficulty’ through the clinical application of learned concepts. Self-reported recall improved over time. This structured, self-directed approach supports effective learning in resuscitation medicine. AI and peer-driven strategies augment knowledge retention. This methodology offers adaptability for broader medical education settings.