ArticleJMIR formative research2026
Generative AI-Assisted Microlearning for Erectile Dysfunction Myth Reduction: Single-Center Pre-Post Quasi-Experimental Study.
Article in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Background: Erectile dysfunction (ED) is strongly influenced by persistent misconceptions that delay help-seeking and limit engagement with effective care. Patient-centered digital strategies, including generative artificial intelligence (AI) microlearning, may improve sexual-health knowledge; however, real-world evidence in urological practice remains sparse. Objective: This study aimed to evaluate whether a clinician-supervised generative AI microlearning video improves ED-related knowledge in adult men attending routine outpatient care. Methods: This single-center pre-post quasi-experimental study included 200 adult men in a university urology clinic. Participants completed an 8-item ED myth questionnaire immediately before and after watching a 3-minute educational video. The narration script was drafted using a large language model (ChatGPT) and iteratively reviewed by urologists for accuracy and cultural appropriateness. The primary outcome was the within-participant change in total correct responses (0-8). Subgroup analyses assessed effects across age (<40 years vs ≥40 years), education level, and self-reported ED. Paired analyses and multivariable logistic regression were used (α=.05). Results: All participants completed the intervention (mean age 44.0, SD 11.6 years). Total mean correct responses increased from 3.77 to 6.56 (mean difference 2.79; P<.001), indicating a large effect (Cohen d=1.52). Knowledge gains were consistent across subgroups, with greater improvements among those with lower education. Self-reported ED was independently associated with lower odds of achieving ≥2-point improvement (odds ratio 0.46, 95% CI 0.26-0.81; P=.01). No adverse events or technical difficulties occurred. Conclusions: A brief clinician-supervised generative AI microlearning video was associated with substantial short-term improvements in ED myth-related knowledge in routine outpatient care. AI-assisted microlearning may represent a scalable adjunct to patient education during urological consultations. Future studies should evaluate long-term retention and behavioral outcomes.
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