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<p>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&#xa0;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.</p>

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Using participatory action research to develop an artificial intelligence-augmented, peer-driven, case-based, and simulation-enhanced curriculum for emergency medicine residents

  • 期刊论文

  • 发表日期：

    2026年2月18日

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  • Kyle W. Eastwood,
  • Daniah Allali,
  • Sittichok Leela-Amornsin,
  • Jean-Philippe Desbiens,
  • Adam Szulewski

摘要

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.