Ethical engagement with artificial intelligence in medical education.

Abstract

The integration of large language models (LLMs) in medical education offers both opportunities and challenges. While these artificial intelligence (AI)-driven tools can enhance access to information and support critical thinking, they also pose risks like potential overreliance and ethical concerns. To ensure ethical use, students and instructors must recognize the limitations of LLMs, maintain academic integrity, and handle data cautiously, and instructors should prioritize content quality over AI detection methods. LLMs can be used as supplementary aids rather than primary educational resources, with a focus on enhancing accessibility and equity and fostering a culture of feedback. Institutions should create guidelines that align with their unique educational values, providing clear frameworks that support responsible LLM usage while addressing risks associated with AI in education. Such guidelines should reflect the institution's pedagogical mission, whether centered on clinical practice, research, or a mix of both, and should be adaptable to evolving educational technologies.