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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.

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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.

Keywords:
Artificial intelligenceCurriculum developmentEducation

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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.