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PEPCAL: A Heuristic Framework for Teaching Generative AI to Continuing Professional Education Providers and Faculty
Heather MacNeill1, Ken Masters, Jennifer Benjamin
1Dr. MacNeill: Assistant Dean, Clinical Faculty Development, Toronto Metropolitan University, School of Medicine, Toronto, Ontario, Canada, and Professor, Temerty Faculty of Medicine, Department of Medicine, University of Toronto, Toronto, Ontario, Canada. Dr. Masters: Professor of Medical Informatics, Medical Education and Informatics Department, College of Medicine and Health Sciences, Sultan Qaboos University, Sultanate of Oman. Dr. Benjamin: Assistant Dean of CPD, Associate Professor, Pediatrics, Department of Pediatrics, Huffington Department of Education Innovation and Technology, Baylor College of Medicine, and Co-Director for Faculty College Texas Children's Hospital, Houston, TX. Dr. Mehta: Professor of Medicine and Associate Dean for Curricular Affairs, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University School of Medicine, and The Jones Day Endowed, Chair in Medical Education, Cleveland Clinic, Cleveland, OH, and Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH. Dr. Agrawal: Associate Professor in Internal Medicine and Pediatrics, Program Director for the Combined Internal Medicine and Pediatrics Residency Program, Baylor College Medicine, Houston, TX. Dr. Valanci-Aroesty: Consultant for Royal College of Physicians and Surgeons of Canada, Ottawa, Canada.
The PEPCAL framework offers guidance for integrating Generative AI (GenAI) into continuing professional development (CPD) and faculty development (FD). It helps educators use GenAI effectively and ethically, moving beyond tool-based adoption to education-first strategies.
Area of Science:
- Medical Education
- Artificial Intelligence
- Professional Development
Background:
- Generative AI (GenAI) is rapidly advancing, impacting continuing professional development (CPD) and faculty development (FD).
- Educators require practical, theory-informed guidance for integrating GenAI into teaching and learning.
- Existing frameworks may not fully address the unique challenges and opportunities of GenAI in professional education.
Purpose of the Study:
- To introduce the Pedagogical-Ethical-Preparation-Creative-Administrative-Large Data (PEPCAL) framework.
- To provide a structured approach for CPD/FD leaders to teach about and utilize GenAI.
- To support the responsible and effective integration of GenAI in health professions education.
Main Methods:
- Development of the PEPCAL framework based on international teaching experience, literature review, and inductive feedback.
- Creation of a heuristic matrix with intersecting axes: teaching GenAI and using GenAI in teaching.
- Application of the framework in training CPD/FD providers for GenAI implementation.
Main Results:
- PEPCAL offers a matrix guiding GenAI integration through Pedagogical Design, Ethical Considerations, Preparation, Creative Applications, Administrative Tasks, and Large Data Insights.
- The framework identifies opportunities for GenAI to enhance learning, curriculum design, feedback, and data analysis.
- It emphasizes ethical considerations, environmental impact, pedagogical design, and preventing deskilling and burnout.
Conclusions:
- PEPCAL complements existing CPD/FD frameworks and learning theories.
- It encourages an education-first adoption of GenAI, adapting to diverse educational contexts.
- The framework facilitates responsible implementation, evaluation, and refinement of GenAI-enabled CPD/FD, fostering dialogue on sociotechnical issues.
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