Articlehybrid2025

Understanding higher education students’ adoption of generative AI technologies: An empirical investigation using UTAUT2

Olga Victorovna Sergeeva · Marina Rostislavovna ZHELTUKHINA · Tatyana V. Shoustikova · Leysan R. Tukhvatullina · Denis A. Dobrokhotov · Sergey V. Kondrashev

85 citations0/0 files storedContemporary Educational Technology

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Technology Adoption and User Behaviour Artificial Intelligence in Education AI in Service Interactions

Abstract

Generative artificial intelligence (GAI) technologies are gaining traction in higher education, offering potential benefits such as personalized learning support and enhanced productivity. However, successful integration requires understanding the factors influencing students’ adoption of these emerging tools. This study investigates the determinants shaping higher education students’ adoption of GAI through the lens of the unified theory of acceptance and use of technology 2 framework. Data was collected from Pyatigorsk State University students and analyzed using structural equation modeling. The findings reveal habit (HB) as the most influential predictor of GAI adoption among students, followed by performance expectancy. Hedonic motivation, social influence (SI), and price value positively influenced behavioral intention (BI) to use these technologies. Surprisingly, facilitating conditions (FCs) exhibited a negative effect on BI, suggesting potential gaps in support systems. The study identifies no significant gender differences in the underlying factors driving adoption. Based on the results, recommendations are provided to foster HB formation, communicate benefits, enhance hedonic appeal, leverage SI, address price concerns, and strengthen FCs. Potential limitations include the cross-sectional nature of the data, geographic constraints, reliance on self-reported measures, and the lack of consideration for individual differences as moderators. This research contributes to the growing body of knowledge on GAI adoption in educational contexts, offering insights to guide higher education institutions in responsibly integrating these innovative tools while addressing student needs and promoting improved learning outcomes.

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1991The theory of planned behavior 1989Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology 2003User Acceptance of Information Technology: Toward A Unified View1 2018When to use and how to report the results of PLS-SEM 2012Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology1 2021Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R 2011Predicting and Changing Behavior 1985Disentangling behavioral intention and behavioral expectation

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