Large language models in emergency medicine education: opportunities, challenges, and implementation pathways

Emergency medicine education occurs in high-acuity, interruption-prone, and time-constrained environments, where learners must develop rapid clinical reasoning, effective communication, procedural competence, and reliable documentation skills. Large language models (LLMs) are increasingly being explored in health-professions education. This narrative review synthesizes emerging applications, major risks, and implementation pathways for LLMs in emergency medicine education.

A structured narrative review was conducted using PubMed, Web of Science Core Collection, China National Knowledge Infrastructure (CNKI), and Wanfang Data. The search period extended from January 1, 2023, to April 10, 2026. English and Chinese search blocks combined terms related to LLMs or generative artificial intelligence, emergency medicine or emergency care contexts, and education, training, simulation, assessment, communication, documentation, or implementation. After duplicate removal, title and abstract screening, and full-text review, 48 English-language studies and 5 Chinese-language studies were included. Eligible records addressed emergency medicine or emergency medical services education, simulation or virtual-patient applications, formative assessment and feedback, documentation or discharge communication, or governance issues relevant to educational use in emergency settings.

LLMs showed potential across multiple educational domains in emergency medicine, including just-in-time tutoring, resource generation, case drafting, simulation and virtual-patient rehearsal, formative feedback support, examination and competency-assessment support, documentation and discharge communication coaching, and educator workflow support. Potential benefits included more timely feedback, broader access to structured teaching resources, repeated rehearsal of low-frequency high-acuity scenarios, and greater consistency in communication training. Translation into routine educational practice remains constrained by hallucination, context mismatch with local protocols, automation bias, limited relational authenticity in AI-mediated interaction, privacy and cybersecurity concerns, multilingual inequity, uncertain validity of AI-assisted assessment, and uneven faculty readiness.

LLMs hold substantial promise for strengthening emergency medicine education, particularly in areas requiring rapid language-based support, structured feedback, scalable case generation, and communication rehearsal. Current evidence supports phased adoption, local grounding in institutional protocols, secure workflows, explicit faculty oversight, and evaluation of educational, operational, and governance outcomes. This review proposes a pragmatic framework for the integration of LLMs into emergency medicine education.