ORIGINAL RESEARCH article
Front. Educ.
Sec. STEM Education
Predicting STEM Students' Adoption of Generative AI in Academic Contexts: An Application of the UTAUT Model
Provisionally accepted
- 1Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
- 2Prince Sattam bin Abdulaziz University Deanship of Scientific Research, Al Kharj, Saudi Arabia
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This study examined the factors influencing STEM students' behavioral intention to adopt generative AI using the Unified Theory of Acceptance and Use of Technology (UTAUT). A survey of 464 students from Prince Sattam Bin Abdulaziz University in Saudi Arabia was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), which showed good model fit (χ²/df = 2.94, GFI = 0.92, AGFI = 0.89, RMSEA = 0.056, NFI = 0.91, CFI = 0.94). Results revealed that performance expectancy (β = 0.491, p < 0.001), effort expectancy (β = 0.130, p < 0.001), social influence (β = 0.239, p < 0.001), and facilitating conditions (β = 0.213, p < 0.001) significantly predicted behavioral intention. Subgroup analysis indicated that female students, those with beginner-level computer experience, and students majoring in Engineering and Computer Science reported higher intention to adopt AI. These findings highlight the importance of perceived usefulness, usability, social norms, and institutional support in promoting AI adoption in STEM education. The study recommends enhancing infrastructure, offering targeted training, and supporting peer-led initiatives to facilitate adoption, while future research should explore longitudinal and cross-cultural trends.
Keywords: Behavioral Intention, Generative AI, higher education, stem, Technology Adoption, UTAUT
Received: 22 Jul 2025; Accepted: 31 Oct 2025.
Copyright: © 2025 BinJwair. 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: Amani Abdullah BinJwair, dr.aasj2007@gmail.com
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