Factors associated with the experience of AI tools for creating health education materials: cross-sectional study...

Artificial intelligence (AI) tools show great potential in the creation of health education materials, yet the factors influencing their adoption and user experience remain underexplored.

This study aims to investigate the factors associated with medical students' experience in using AI tools to create health education materials on the basis of an extended unified theory of acceptance and use of technology (UTAUT) model that incorporates content-related perceptions.

A cross-sectional survey was conducted among students at a medical university in Chongqing, China, from October 17 to 30, 2024. A total of 691 valid responses were analysed. The extended UTAUT model includes performance expectancy, effort expectancy, social influence, facilitating conditions, and four content perception variables: perceived scientificity, understandability, creativity, and misinformation of AI-generated content. Hierarchical logistic regression analysis was conducted, and predictors were entered into three blocks: (1) demographics, (2) core UTAUT constructs, and (3) extended content perceptions. Content analysis was used to explore thematic differences.

Among the 691 participants, 314 (45.4%) had experience using AI tools to create health education materials. Hierarchical regression revealed that clinical medicine majors had more than double the odds of experience (OR = 2.096, P < 0.001), as did paid AI tools (OR = 2.789, P < 0.001) in Model 1. The core UTAUT constructs significantly improved explanatory power, with social influence (OR = 1.268, P = 0.001) and facilitating conditions (OR = 1.561, P < 0.001) as key drivers in Model 2. In contrast, perceptions of generated content quality did not significantly predict usage experience, whereas a lower educational level was significantly associated with higher odds of AI tool use (OR = 0.732, P = 0.03) in Model 3. Content analysis showed that experienced users emphasized content verification and rational use, whereas nonusers expressed more caution and a stronger need for training. Both groups agreed that AI should serve as an assisting tool in creating health education materials.

Social influence and facilitating conditions may be more strongly associated with experience than with perceptions of content quality in this cohort. Enhancing facilitating conditions, social support and targeted training may promote more effective use of AI in health education.