Large language models and medical education: a paradigm shift in educator roles

Abstract

Abstract This article meticulously examines the transformation of educator roles in medical education against the backdrop of emerging large language models (LLMs). Traditionally, educators have played a crucial role in transmitting knowledge, training skills, and evaluating educational outcomes. However, the advent of LLMs such as Chat Generative Pre-trained Transformer-4 has expanded and enriched these traditional roles by leveraging opportunities to enhance teaching efficiency, foster personalised learning, and optimise resource allocation. This has imbued traditional medical educator roles with new connotations. Concurrently, LLMs present challenges to medical education, such as ensuring the accuracy of information, reducing bias, minimizing student over-reliance, preventing patient privacy exposure and safeguarding data security, enhancing the cultivation of empathy, and maintaining academic integrity. In response, educators are called to adopt new roles including experts of information management, navigators of learning, guardians of academic integrity, and defenders of clinical practice. The article emphasises the enriched connotations and attributes of the medical teacher's role, underscoring their irreplaceable value in the AI-driven evolution of medical education. Educators are portrayed not just as users of advanced technology, but also as custodians of the essence of medical education.

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