Generative Artificial Intelligence-Driven Audio Learning Materials for Clinician-Educators: An Exploratory Feasibility Study of Radio- and Narrative-Style Podcasts Using NotebookLM.

INTRODUCTION: Clinician-educators must keep pace with rapidly evolving evidence while improving their teaching skills. However, time constraints often limit their participation in traditional faculty development, thus increasing the demand for portable medical education resources. To address this gap, we conducted an exploratory feasibility study to develop and evaluate a source-grounded approach for producing Japanese audio learning materials using generative artificial intelligence (AI).

METHODS: Peer-reviewed health professions education papers in portable document format (PDF) served as primary sources. Gemini 2.5 Pro generated two supplemental script formats: fictional listener letters describing teaching dilemmas for a radio dialogue and metaphorical fables for a narrative story. We uploaded the scripts and PDF documents to NotebookLM and prompted the AI hosts to respond while adhering to the sources. Audio Overviews were generated in Japanese (short setting). A convenience sample of six practicing physicians-three medical education specialists and three clinician-educators-independently evaluated both formats using a modified Revised Medical Education Translational Resources: Impact and Quality scale (0-3 per item). Reviewers also provided open-ended feedback.

RESULTS: Structure and production (Item 3) and clinical utility (Item 6) consistently received high ratings across evaluators for both formats (scores: 2-3). Clinician-educators found the podcasts extremely helpful for those teaching between busy clinical duties, whereas specialists cautioned against potential oversimplification. Transparency of the editorial and prepublication review processes (Item 5) consistently received the lowest scores (0-1). Evaluators found the synthesized voices natural but noted technical limitations, primarily occasional mispronunciations of specialized Japanese terms. They recommended including clear transparency statements outside the podcast directing learners to the original PDF documents.

CONCLUSIONS: Preliminary evidence suggests that this approach can rapidly generate structured, source-grounded medical education podcasts for busy clinician-educators. However, human curation at multiple stages, transparent reporting, and learner-based evaluation are required to ensure accuracy, trustworthiness, and educational impact.