Generative AI-Assisted Microlearning for Erectile Dysfunction Myth Reduction: Single-Center Pre-Post...

Area of Science:

• Urology
• Digital Health
• Medical Education

Background:

• Persistent misconceptions about erectile dysfunction (ED) hinder patient help-seeking and effective care.
• Digital strategies, including generative AI microlearning, show potential for improving sexual health knowledge.
• Real-world evidence on AI in urological practice for ED education is limited.

Purpose of the Study:

• To evaluate the effectiveness of a clinician-supervised generative AI microlearning video in enhancing ED-related knowledge.
• To assess improvements in knowledge among adult men in a routine outpatient urology setting.

Main Methods:

• A single-center, pre-post quasi-experimental study involving 200 adult men.
• Participants completed an 8-item ED myth questionnaire before and after a 3-minute AI-generated educational video.
• Analysis included paired t-tests and multivariable logistic regression to assess knowledge changes and influencing factors.

Main Results:

• Mean correct responses on the ED myth questionnaire significantly increased from 3.77 to 6.56 (P<.001), indicating a large effect size (Cohen d=1.52).
• Knowledge gains were consistent across age, education, and self-reported ED subgroups, with greater improvements in those with lower education.
• Lower odds of significant knowledge improvement were associated with self-reported ED (OR 0.46, P=.01).

Conclusions:

• A short, AI-generated microlearning video effectively improved ED knowledge in a urology clinic setting.
• AI-assisted microlearning can serve as a scalable tool to supplement patient education during urological consultations.
• Further research is needed to assess long-term knowledge retention and behavioral changes.