ORIGINAL RESEARCH article
Front. Psychol.
Sec. Educational Psychology
Determinants of Acceptance and Usage of Generative AI (GenAI) Among Chinese Medical Students: A UTAUT-Based Empirical Investigation
Provisionally accepted
- 1School of Stomatology, Xuzhou Medical University, Xuzhou, China
- 2School of Public Health, Xuzhou Medical University, Xuzhou, China
- 3Student Affairs Office, Xuzhou Medical University, Xuzhou, China
- 4Shenyang Heping District Center for Disease Control and Prevention, Shenyang, China
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Background: Generative artificial intelligence (GenAI) is rapidly transforming higher education, yet empirical evidence remains limited on the factors associated with its acceptance and usage among medical students, especially in non-Western, high-stakes educational contexts like China. This study applied the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate the relationships between core UTAUT constructs, and behavioral intention (BI) and actual usage (AU) of GenAI, among Chinese medical students. Methods: A cross-sectional online survey was administered to students at a public medical university in China from October 2024 to January 2025, yielding 1781 valid responses. Validated scales were used to measure core UTAUT constructs: performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FCs), BI, and AU. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to test the hypothesized relationships. Results: The model explained 67.6% of the variance in BI and 66.3% in AU. PE (β = 0.377, P < 0.001), FCs (β = 0.333, P < 0.001) and SI (β = 0.212, P < 0.001) were positively associated with BI. EE showed no significant direct association with BI (β = 0.038, P = 0.209) but had a weak yet significant direct association with AU (β = 0.057, P = 0.045). BI mediated the relationships between PE, SI, FCs, and AU (all P < 0.001) but failed to mediate the association between EE and AU (P = 0.219). Age was the only significant moderator for the path from EE to BI (β = 0.071, P = 0.043) and the path from BI to AU (β = 0.024, P = 0.022); gender, major, and academic level showed no moderating effects. Conclusions: This study empirically validates and extends the UTAUT framework within Chinese medical education, highlighting the important roles of PE, FCs and SI, the context-dependent role of EE, and identifying the moderating role of age. Strategic interventions, including demonstrating utility, improving infrastructure, leveraging social advocacy, and tailoring for age—are recommended to support the responsible integration of GenAI into medical training, ultimately preparing future healthcare professionals for an AI-driven healthcare ecosystem.
Keywords: Chinese medical students, Generative Artificial Intelligence (GenAI), higher education, medicaleducation, technology acceptance, UTAUT
Received: 12 Nov 2025; Accepted: 15 Jan 2026.
Copyright: © 2026 Jiang, Xue, Yuan and Tong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xue Jiang
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