โก Quick Summary
This study explored the factors influencing medical students’ experiences with AI tools for creating health education materials, revealing that social influence and facilitating conditions play a crucial role in their adoption. The findings suggest that enhancing these factors could lead to more effective use of AI in health education.
๐ Key Details
- ๐ Participants: 691 medical students from Chongqing, China
- ๐งฉ Methodology: Cross-sectional survey using an extended UTAUT model
- โ๏ธ Constructs analyzed: Performance expectancy, effort expectancy, social influence, facilitating conditions, and content perceptions
- ๐ Key findings: 45.4% of participants had experience using AI tools
๐ Key Takeaways
- ๐ Clinical medicine majors had over double the odds of using AI tools (OR = 2.096, P < 0.001).
- ๐ฐ Paid AI tools significantly increased usage odds (OR = 2.789, P < 0.001).
- ๐ฅ Social influence was a key driver for AI tool adoption (OR = 1.268, P = 0.001).
- ๐ง Facilitating conditions also played a significant role (OR = 1.561, P < 0.001).
- ๐ Lower educational levels were associated with higher odds of AI tool use (OR = 0.732, P = 0.03).
- ๐ Experienced users emphasized the importance of content verification.
- โ ๏ธ Nonusers expressed caution and a need for training in AI tool usage.
- ๐ค Both groups agreed that AI should assist in creating health education materials.
๐ Background
The integration of artificial intelligence (AI) in healthcare is rapidly evolving, particularly in the realm of health education. However, understanding the factors that influence the adoption and user experience of AI tools remains a critical area of research. This study aims to fill that gap by examining medical students’ experiences with AI tools for creating educational materials.
๐๏ธ Study
Conducted between October 17 and 30, 2024, this cross-sectional survey involved 691 medical students from a university in Chongqing, China. The researchers employed an extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT) model, which included additional content-related perceptions to better understand the factors influencing AI tool usage.
๐ Results
Out of the 691 participants, 314 (45.4%) reported having experience using AI tools for health education. The hierarchical regression analysis indicated that clinical medicine majors and the use of paid AI tools significantly increased the likelihood of AI tool adoption. Notably, social influence and facilitating conditions emerged as critical factors in enhancing user experience, while perceptions of content quality did not significantly predict usage.
๐ Impact and Implications
The findings of this study have important implications for the future of AI in health education. By focusing on enhancing social support and facilitating conditions, educational institutions can promote more effective use of AI tools. This could lead to improved health education materials, ultimately benefiting both students and patients alike.
๐ฎ Conclusion
This study highlights the significant role of social influence and facilitating conditions in the adoption of AI tools for health education. By addressing these factors, we can foster a more supportive environment for the integration of AI in educational settings. Continued research and targeted training will be essential in maximizing the potential of AI technologies in healthcare education.
๐ฌ Your comments
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Factors associated with the experience of AI tools for creating health education materials: cross-sectional study using an extended UTAUT model.
Abstract
BACKGROUND: 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.
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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 (ORโ=โ2.096, Pโ<โ0.001), as did paid AI tools (ORโ=โ2.789, Pโ<โ0.001) in Model 1. The core UTAUT constructs significantly improved explanatory power, with social influence (ORโ=โ1.268, Pโ=โ0.001) and facilitating conditions (ORโ=โ1.561, Pโ<โ0.001) 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 (ORโ=โ0.732, Pโ=โ0.03) 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.
CONCLUSION: 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.
Author: [‘Zheng C’, ‘Zhang Y’, ‘Luo T’, ‘Li F’, ‘Qin Z’, ‘Xiong S’, ‘Zhang J’, ‘Lei E’, ‘Lu L’, ‘Zhang L’, ‘Rong H’, ‘Chen JA’]
Journal: BMC Med Educ
Citation: Zheng C, et al. Factors associated with the experience of AI tools for creating health education materials: cross-sectional study using an extended UTAUT model. Factors associated with the experience of AI tools for creating health education materials: cross-sectional study using an extended UTAUT model. 2026; (unknown volume):(unknown pages). doi: 10.1186/s12909-025-08499-4