Actionable companion · Assessment and evidence of learning

From evidence to district action: Large language models in emergency medicine education: opportunities, challenges, and implementation pathways

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

Evidence and context

Health-professions education context: test transfer to K–12 rather than assuming it.

Original authors’ abstract, reproduced under the linked source license:

Background 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. Methods 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. Results 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. Conclusion 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.

A 90-day district pilot

1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
2. Days 16–30 · Design. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. Name an accountable owner and set the baseline, success threshold and budget before implementation.
3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
4. Days 61–90 · Decide. Blind-score reasoning, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Tingting Fan; Tianle Gao; Wan Tang; Qian Xu; Xingyou Wang; Qiaoli Su; Qingguo Lyu. Large language models in emergency medicine education: opportunities, challenges, and implementation pathways. Frontiers in Public Health (2026-08-05). https://doi.org/10.3389/fpubh.2026.1899528

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.