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Practical Applications of Generative Artificial Intelligence for Clinical Teaching in the Emergency Department.

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Title: Practical Applications of Generative Artificial Intelligence for Clinical Teaching in the Emergency Department.
Authors: Shaw, Jazmyn J.1 (AUTHOR) jazmyn.shaw@uc.edu, Mand, Simanjit K.2 (AUTHOR), Williams, Sarah R.3 (AUTHOR), Cico, Stephen J.4 (AUTHOR), Bailes, Carrie5 (AUTHOR), Alvarez, Al'ai3 (AUTHOR), Promes, Susan B.6 (AUTHOR), Wagner, Jason7 (AUTHOR), Schnapp, Benjamin H.2 (AUTHOR)
Source: AEM Education & Training. Apr2026, Vol. 10 Issue 2, p1-10. 10p.
Subject Terms: *Generative artificial intelligence, *Self-regulated learning, *Educational technology, *Clinical education, *Medical education, Language models, Artificial intelligence & ethics, Hospital emergency services
Abstract: Introduction: Clinical teaching in the emergency department (ED) continues to be an essential component of medical education but is increasingly constrained by time pressure, competing clinical demands, and variability in educator experience. Generative artificial intelligence (AI), particularly large language models (LLMs), offers new opportunities to support learner‐centered clinical education; AI can assist educators with the preparation, organization, and provision of feedback during an educational encounter while preserving the central human elements of teaching. Approach: This article reflects best practices as outlined by both the literature and the expert perspectives of academic emergency medicine (EM) physicians from multiple institutions who facilitated the "Be the Best Teacher: Clinical Teaching Educational Bootcamp" at the 2025 Society for Academic Emergency Medicine (SAEM) pre‐conference. Drawing on their collective experience as clinical educators, the authors review current applications of generative AI in medical education and present practical, clinically grounded examples of how LLMs can be incorporated into everyday teaching activities in the ED. Educational Uses: Three key domains were identified using the Self‐Regulated Learning theory, in which LLMs may support time‐constrained educators on a clinical shift with learners. (1) LLMs can help both educators and learners with Forethought ("before" learning) for the clinical shift by generating learner‐specific goals and objectives. (2) LLMs can assist educators in preparing learners to Perform ("during" learning) clinical duties by guiding procedural preparation and augmenting efficiency with patient‐facing tasks. (3) Educators can use LLMs to help shape Self‐Reflection ("after" learning) by guiding effective feedback between educators and learners. This paper provides a practical, learner‐centered, theory‐based framework for incorporating AI into clinical teaching while upholding the principles of evidence‐based, humanistic medical education. Considerations and Challenges: AI works best as a tool to supplement self‐directed learning rather than a replacement for clinical preparation, observation, or teaching. Important limitations remain with the integration of AI in the medical education setting, including data privacy concerns, potential biases, inaccurate or incomplete outputs, and the risk of overreliance that may undermine clinical reasoning or educator engagement. Conclusion: When integrated thoughtfully in the Self‐Directed Learning framework, generative AI tools (such as LLMs) can help EM educators overcome long‐standing barriers to clinical teaching by enhancing the structure and delivery of educational sessions while maintaining the primacy of human judgment and interaction. Educators play a critical role in modeling ethical, transparent, and reflective use of AI tools for learners who are already encountering them in clinical environments. [ABSTRACT FROM AUTHOR]
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Database: Education Research Complete
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Abstract:Introduction: Clinical teaching in the emergency department (ED) continues to be an essential component of medical education but is increasingly constrained by time pressure, competing clinical demands, and variability in educator experience. Generative artificial intelligence (AI), particularly large language models (LLMs), offers new opportunities to support learner‐centered clinical education; AI can assist educators with the preparation, organization, and provision of feedback during an educational encounter while preserving the central human elements of teaching. Approach: This article reflects best practices as outlined by both the literature and the expert perspectives of academic emergency medicine (EM) physicians from multiple institutions who facilitated the "Be the Best Teacher: Clinical Teaching Educational Bootcamp" at the 2025 Society for Academic Emergency Medicine (SAEM) pre‐conference. Drawing on their collective experience as clinical educators, the authors review current applications of generative AI in medical education and present practical, clinically grounded examples of how LLMs can be incorporated into everyday teaching activities in the ED. Educational Uses: Three key domains were identified using the Self‐Regulated Learning theory, in which LLMs may support time‐constrained educators on a clinical shift with learners. (1) LLMs can help both educators and learners with Forethought ("before" learning) for the clinical shift by generating learner‐specific goals and objectives. (2) LLMs can assist educators in preparing learners to Perform ("during" learning) clinical duties by guiding procedural preparation and augmenting efficiency with patient‐facing tasks. (3) Educators can use LLMs to help shape Self‐Reflection ("after" learning) by guiding effective feedback between educators and learners. This paper provides a practical, learner‐centered, theory‐based framework for incorporating AI into clinical teaching while upholding the principles of evidence‐based, humanistic medical education. Considerations and Challenges: AI works best as a tool to supplement self‐directed learning rather than a replacement for clinical preparation, observation, or teaching. Important limitations remain with the integration of AI in the medical education setting, including data privacy concerns, potential biases, inaccurate or incomplete outputs, and the risk of overreliance that may undermine clinical reasoning or educator engagement. Conclusion: When integrated thoughtfully in the Self‐Directed Learning framework, generative AI tools (such as LLMs) can help EM educators overcome long‐standing barriers to clinical teaching by enhancing the structure and delivery of educational sessions while maintaining the primacy of human judgment and interaction. Educators play a critical role in modeling ethical, transparent, and reflective use of AI tools for learners who are already encountering them in clinical environments. [ABSTRACT FROM AUTHOR]
ISSN:24725390
DOI:10.1002/aet2.70146