Payal Guha, Oscar L Oglina, Celia A Brown, Courtney Krstic, Chukwuma Uduku, Layla Thurston, Risheka Walls, Amir H Sam
{"title":"Teaching on demand: Exploring the role of AI in medical education podcasts.","authors":"Payal Guha, Oscar L Oglina, Celia A Brown, Courtney Krstic, Chukwuma Uduku, Layla Thurston, Risheka Walls, Amir H Sam","doi":"10.1080/0142159X.2026.2733139","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Artificial intelligence (AI)-generated educational content holds promise for scalable medical education, but evidence comparing AI and clinician-produced podcasts remains limited. We investigated whether AI-generated podcasts achieve comparable student satisfaction to clinician-recorded podcasts, enabling broader curriculum coverage without overburdening clinical educators, and explored students' attitudes towards AI-generated podcasts.</p><p><strong>Methods: </strong>Medical students were randomised into two groups, with group 1 receiving clinician-produced podcasts on type 1 diabetes (T1DM) and AI-generated podcasts on type 2 diabetes (T2DM). Group 2 received AI-generated podcasts on T1DM, and clinician-produced podcasts on T2DM. Students were blinded to the podcast types. The outcome measures included students' satisfaction (measured using the Student Satisfaction with Educational Podcasts Questionnaire (SSEQ)) and students' attitudes to AI using an open-ended survey. A Bonferroni correction was applied (α = 0.025).</p><p><strong>Results: </strong>One hundred and seventy-eight students participated in this study (Group 1 <i>n</i> = 93, Group 2 <i>n</i> = 85). Mean SSEQ scores (out of 50, reported as mean ± SD) showed no significant difference between clinician and AI podcasts in T1DM or T2DM (T1DM: 35.8 ± 6.80 vs 34.6 ± 7.19, <i>p</i> = 0.3025, T2DM: 33.2 ± 7.50 vs 35.5 ± 6.88, <i>p</i> = 0.0513). In the attitude survey (<i>n</i> = 165), 56% reported production method mattered, citing the sound of the voice, engagement of speaker, and ethical concerns as major issues with AI podcasts.</p><p><strong>Discussion: </strong>AI-generated podcasts achieved non-inferior satisfaction to clinician-recorded podcasts. However, students' psychological preference for human teaching and negative perceptions of AI pose a barrier to scalable benefits, necessitating efforts to build trust and acceptability.</p>","PeriodicalId":18643,"journal":{"name":"Medical Teacher","volume":" ","pages":"1-7"},"PeriodicalIF":4.0000,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical Teacher","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1080/0142159X.2026.2733139","RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION, SCIENTIFIC DISCIPLINES","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: Artificial intelligence (AI)-generated educational content holds promise for scalable medical education, but evidence comparing AI and clinician-produced podcasts remains limited. We investigated whether AI-generated podcasts achieve comparable student satisfaction to clinician-recorded podcasts, enabling broader curriculum coverage without overburdening clinical educators, and explored students' attitudes towards AI-generated podcasts.
Methods: Medical students were randomised into two groups, with group 1 receiving clinician-produced podcasts on type 1 diabetes (T1DM) and AI-generated podcasts on type 2 diabetes (T2DM). Group 2 received AI-generated podcasts on T1DM, and clinician-produced podcasts on T2DM. Students were blinded to the podcast types. The outcome measures included students' satisfaction (measured using the Student Satisfaction with Educational Podcasts Questionnaire (SSEQ)) and students' attitudes to AI using an open-ended survey. A Bonferroni correction was applied (α = 0.025).
Results: One hundred and seventy-eight students participated in this study (Group 1 n = 93, Group 2 n = 85). Mean SSEQ scores (out of 50, reported as mean ± SD) showed no significant difference between clinician and AI podcasts in T1DM or T2DM (T1DM: 35.8 ± 6.80 vs 34.6 ± 7.19, p = 0.3025, T2DM: 33.2 ± 7.50 vs 35.5 ± 6.88, p = 0.0513). In the attitude survey (n = 165), 56% reported production method mattered, citing the sound of the voice, engagement of speaker, and ethical concerns as major issues with AI podcasts.
Discussion: AI-generated podcasts achieved non-inferior satisfaction to clinician-recorded podcasts. However, students' psychological preference for human teaching and negative perceptions of AI pose a barrier to scalable benefits, necessitating efforts to build trust and acceptability.
期刊介绍：
Medical Teacher provides accounts of new teaching methods, guidance on structuring courses and assessing achievement, and serves as a forum for communication between medical teachers and those involved in general education. In particular, the journal recognizes the problems teachers have in keeping up-to-date with the developments in educational methods that lead to more effective teaching and learning at a time when the content of the curriculum—from medical procedures to policy changes in health care provision—is also changing. The journal features reports of innovation and research in medical education, case studies, survey articles, practical guidelines, reviews of current literature and book reviews. All articles are peer reviewed.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
