ArticleBMC medical education2026
Factors associated with the experience of AI tools for creating health education materials: cross-sectional study using an extended UTAUT model.
Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundArtificial intelligence (AI) tools show great potential in the creation of health education materials, yet the factors influencing their adoption and user experience remain underexplored.
objectiveThis 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.
methodsA 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.
resultsAmong 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.
conclusionSocial 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.