Predicting STEM students’ adoption of generative AI in academic contexts: an application of the UTAUT model

Abstract

Introduction: The rapid advancement of generative artificial intelligence (AI) has created new opportunities and challenges in higher education, particularly in STEM disciplines. Understanding the factors that influence students' behavioral intention to adopt generative AI is essential for effective integration into learning environments. This study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine these factors among STEM students. Methods: A cross-sectional survey was conducted among 464 STEM students at Prince Sattam Bin Abdulaziz University in Saudi Arabia. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed model. Model fit indices indicated good fit (χ 2 /df = 2.94, GFI = 0.92, AGFI = 0.89, RMSEA = 0.056, NFI = 0.91, CFI = 0.94). Results: 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 to adopt generative AI. Subgroup analyses revealed higher adoption intentions among female students, those with beginner-level computer experience, and students majoring in Engineering and Computer Science. Discussion: The findings highlight the crucial role of perceived usefulness, ease of use, social norms, and institutional support in influencing AI adoption among STEM students. To enhance adoption, the study recommends improving digital infrastructure, providing targeted AI training, and promoting peer-led initiatives. Future research should investigate longitudinal and cross-cultural dynamics of AI adoption in education.