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Updated: Feb 20, 2026
06:48
Emergency Undocking in Robotic Surgery: A Simulation Curriculum
Published on: May 20, 2018
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Using participatory action research to develop an artificial intelligence-augmented, peer-driven, case-based, and
Kyle W Eastwood1, Daniah Allali2, Sittichok Leela-Amornsin3
1Department of Emergency Medicine, Dalhousie University, Halifax, NS, Canada. ky675200@dal.ca.
CJEM
|February 18, 2026
Summary
Senior emergency medicine trainees improved resuscitation medicine knowledge using an AI-augmented, peer-driven framework. This approach combined case studies, simulations, and AI tools for enhanced learning and knowledge retention over six months.
Area of Science:
- Medical Education
- Artificial Intelligence in Medicine
- Emergency Medicine Training
Background:
- Traditional medical education methods may not fully optimize knowledge acquisition for complex fields like resuscitation medicine.
- Senior trainees require advanced learning strategies that integrate theoretical knowledge with practical application.
- The integration of technology and peer collaboration offers potential for enhanced self-directed learning.
Purpose of the Study:
- To develop and evaluate an artificial intelligence (AI)-augmented, peer-driven framework for senior emergency medicine trainees.
- To enhance knowledge acquisition and retention in resuscitation medicine through a structured, self-directed learning approach.
- To assess the impact of incorporating generative AI tools and simulation-based learning on trainee performance.
Main Methods:
- Participatory action research was employed to develop a framework integrating peer-selected readings, case-based discussions, high-fidelity simulations, and spaced-repetition flashcards.
- Trainees engaged in six-month structured learning cycles based on 'desirable-difficulty' and deliberate practice principles.
- Generative AI tools were utilized with evidence-based prompt engineering to create AI-generated questions and supplement learning activities.
Main Results:
- The AI-augmented, peer-driven framework facilitated structured learning cycles, enhancing knowledge acquisition in resuscitation medicine.
- AI-generated questions supported retrieval-based learning, and flashcard integration improved knowledge retention.
- Simulation-based reinforcement provided 'desirable-difficulty' through clinical application, leading to improved self-reported recall over time.
Conclusions:
- The developed framework effectively supports self-directed learning in resuscitation medicine for senior emergency medicine trainees.
- The combination of AI augmentation and peer-driven strategies significantly enhances knowledge retention.
- This adaptable methodology holds potential for application across various medical education settings.
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