Authors
Jiang-Tao Zhong, Zai-Zai Cao, Shui-Hong Zhou
Published in
JMIR medical education. Volume 12. Pages e91785. Oct 19, 2026. Epub Oct 19, 2026.
Abstract
Generative AI, including large language models (LLMs), may enable personalized learning in medical education. In otolaryngology-head and neck surgery (ear, nose, and throat [ENT]) education, AI may support knowledge retrieval, adaptive assessment, feedback, and scalable learning, but evidence remains fragmented.
This scoping review aimed to map generative AI research in otolaryngology education; summarize applications, performance, benefits, and risks; and identify directions for AI-enabled personalized learning.
We included English-language empirical studies, preprints, and technical or developmental evaluations published from 2017 through 2026 with direct relevance to professional ENT education; nonempirical publications and studies without a professional educational focus were excluded. PubMed, Embase, Web of Science, Scopus, ERIC, IEEE Xplore, and CINAHL were searched, supplemented by citation searching. The final search was conducted on July 6, 2026. Data were charted using a standardized form by 2 reviewers and synthesized through a 3-stage inductive thematic analysis, with third reviewer adjudication. No formal critical appraisal or meta-analysis was performed because of study heterogeneity.
A total of 42 studies published between 2023 and 2026 were included. Most were performance studies (n=33, 78.6%), followed by system development (n=3, 7.1%), educational intervention studies (n=3, 7.1%), surveys (n=2, 4.8%), and guideline development (n=1, 2.4%). Most studies evaluated question sets or model outputs rather than participants. Applications included knowledge retrieval, revision, board-style question practice, adaptive assessment, AI avatar-delivered instruction, and operative-planning support. Seven major thematic domains were identified: (1) educational support and learning enhancement; (2) learning outcomes, educational effectiveness, and pedagogical value; (3) model performance, accuracy, and variability; (4) assessment, examination readiness, and question-type sensitivity; (5) reliability, safety, and risks; (6) adoption, usability, and implementation barriers; and (7) advanced AI architectures and future educational design. Newer models performed strongly on selected structured knowledge tasks, but performance varied by task, difficulty, modality, and reasoning demands. Evidence remained heterogeneous and benchmarking focused, with only 3 educational interventions and limited evidence for sustained learning, clinical reasoning, or real-world outcomes. Hallucinations, unreliable sourcing, confidently incorrect outputs, and contextual and multimodal reasoning limitations supported supervised rather than autonomous use.
By moving beyond reviews centered on AI applications or model performance, this review provides an education-focused synthesis linking generative AI capabilities, learning outcomes, assessment, safety, implementation, and emerging architectures to the design of personalized learning in otolaryngology. Its contribution is a conceptual shift from evaluating standalone chatbots toward developing supervised, pedagogically governed, knowledge-grounded, and adaptive learning systems that preserve learner reasoning and expert oversight. These findings inform educators, curriculum designers, and developers when integrating generative AI into formative assessment, feedback, remediation, and personalized learning activities, while avoiding unsupported autonomous or high-stakes use. Future research should prioritize longitudinal educational outcomes, curriculum integration, higher-order reasoning, equitable implementation, and governance.
PMID:
42896157
Bibliographic data and abstract were imported from PubMed on 20 Oct 2026.
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