共识-平台-实训-案例:人工智能赋能医学教育的CPTC范式与协和实践

Consensus-Platform-Training-Case: The CPTC Framework and Peking Union Medical College Practice in AI-Empowered Medical Education

  • 摘要: 生成式人工智能正在改变医学知识的获取、组织与应用,医学教育的紧迫课题已由“是否使用”转向“如何在使用中保护专业能力、重塑教师作用并形成可信治理”。现有研究显示,人工智能可扩展练习、模拟与反馈,但模型表现和短期学习结果无法替代对教师能力及医学生临床胜任力的真实情境评价,也不能替代对患者安全及健康结局的长期检验。基于国际进展及北京协和医学院在共识、组织、教师实训与案例孵化方面的连续探索,本文阐释共识(Consensus)—平台(Platform)—实训(Training)—案例(Case)的CPTC可标准化实践范式。该范式以能力保护型精准教育为核心,坚持“以人为本、师生一体、一人一策、人机共治”,将平台界定为连接知识、课程、师生与治理的组织能力,强调教师的质量责任和案例的证据功能。北京协和医学院的实践探索提示,医学院校可通过制定生成式人工智能教学应用与伦理规范、开展教师人工智能素养和教学设计培训、将真实临床与教学问题转化为分层案例,并建立过程审核、学习评价和案例复用机制,逐步形成可监督、可评价、可迁移的教育能力。CPTC的效果尚需多中心实践以及长期评估加以验证,其价值在于从中国问题出发,把技术优势转化为育人能力,并为全球医学教育提供可比较、可共建的实践方案。

     

    Abstract: Generative artificial intelligence( AI) is transforming the acquisition, organization, and application of medical knowledge. The urgent challenge in medical education has shifted from “whether to use” to “how to use it while safeguarding professional competence, redefining the role of educators, and establishing trustworthy governance”. Existing research indicates that while AI can expand opportunities for practice, simulation, and feedback, model performance and short-term learning outcomes cannot substitute for authentic, contextualized evaluation of faculty competence and medical students' clinical proficiency, nor can they replace long-term validation against patient safety and health outcomes.Drawing on international developments and the ongoing explorations of Peking Union Medical College(PUMC) in consensus-building, organizational structuring, faculty training, and case incubation, this paper articulates a standardized and practicable CPTC paradigm—Consensus, Platform, Training, and Case. Grounded in competencepreserving precision education, this paradigm adheres to the principles of “human-centeredness, teacher-student integration, individualized tailoring, and human-machine co-governance”. It defines the platform as an organizational capability that connects knowledge, curricula, faculty, students, and governance, while emphasizing the quality-related responsibilities of teachers and the evidentiary function of cases.The practical explorations at PUMC suggest that medical schools can progressively develop a superviseable, evaluable, and transferable educational capacity by: formulating guidelines for the pedagogical application and ethical governance of generative AI; delivering training programs to enhance faculty AI literacy and instructional design skills; transforming real-world clinical and pedagogical challenges into tiered cases; and establishing mechanisms for process review, learning assessment, and case reuse. The effectiveness of the CPTC paradigm warrants further validation through multi-center practice and long-term evaluation. Its value lies in its origin in the Chinese context—transforming technological advantages into educational capabilities—while offering a comparable and collaboratively developable practical framework for global medical education.

     

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