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Expert Consensus on Artificial Intelligence Proficiency for Medical Educators(2025 Edition).
Hui Pan1, Meng-Chun Gong2, Jing-Hui Lu2
1Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China.
Summary
This consensus introduces an AI proficiency framework for medical educators to navigate generative artificial intelligence (AI) in education. It outlines core competencies for effective AI integration and professional development in medical training.
Area of Science:
- Medical Education
- Artificial Intelligence
- Digital Health
Background:
- Generative artificial intelligence (AI) presents significant challenges and opportunities within medical education.
- There is a need for a structured framework to guide medical educators in developing AI proficiency.
Purpose of the Study:
- To propose a comprehensive AI proficiency framework for medical educators (CAIP-ME).
- To establish core competency items and assessment standards for AI proficiency in medical educators.
Main Methods:
- Systematic literature review.
- Preliminary framework construction.
- Multiple rounds of expert pre-study.
- Structured Delphi method with 60 interdisciplinary experts.
Main Results:
- A framework with five core dimensions and 25 specific competency items was developed.
- Competencies are categorized into 11 foundational and 14 developmental items.
- Each item includes definitions, behavioral manifestations, and assessment indicators.
Conclusions:
- The CAIP-ME framework provides a scientific basis for medical educator professional development.
- It supports faculty building and serves as a reference for digital transformation in medical education.
- This framework aims to enhance AI competency among medical educators for improved teaching and learning.
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