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Cited by 131Technology Adoption and User BehaviourArtificial Intelligence in EducationAI in Service Interactions

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

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Sergeeva, Olga V., Zheltukhina, Marina R., Shoustikova, Tatyana, Tukhvatullina, Leysan R., Dobrokhotov, Denis A., Kondrashev, Sergey V.·2025·Contemporary Educational Technology
DOI 10.30935/cedtech/16039
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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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  • Generative AI integration in higher education enhances academic engagement and originality, although it also presents ethical and pedagogical dilemmas related to plagiarism, authorship and data bias.

    “Studies demonstrate that the integration of generative AI in higher education enhances academic engagement and originality, although it also presents ethical and pedagogical dilemmas related to plagiarism, authorship and data bias (Sergeeva et al., 2025).”

    From <b>Beyond Access: Socioeconomic, Academic and Disciplinary Determinants of Generative AI Use in Higher Education</b> (2026) · cites this paper as Sergeeva et al., 2025

    The Benefits of AI Implementation section describes the potential for AI tools to enhance engagement, while Challenges and Limitations identifies algorithmic bias; the Introduction also reports academic-integrity concerns. But the paper does not demonstrate increased academic engagement or originality, and the Introduction instead notes worries about loss of creativity. The passages do not specifically address plagiarism or AI-related authorship. (LLM, conf=0.88)

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Cited by

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Authors
Sergeeva, O. V., Zheltukhina, M. R., Shoustikova, T., Tukhvatullina, L. R., Dobrokhotov, D. A., & Kondrashev, S. V.
Affiliations
  • Kuban State University (RU)
  • Pyatigorsk State University (RU)
  • Peoples' Friendship University of Russia (RU)
  • Kazan Federal University (RU)
  • Sechenov University (RU)
Corresponding author
Zheltukhina, Marina R.
ORCID
Venue
Contemporary Educational Technology
Publisher
Bastas Publications
Published
2025-04-01
Volume
17
Issue
2
Pages
ep571
ISSN
1309-517X (ISSN-L)
DOI
10.30935/cedtech/16039
OpenAlex
W4407871395
Cited by
131
References
23
Citations by year
2026: 104 · 2025: 27
FWCI
86.43
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
Contemporary Educational Technology (publishedVersion, cc-by)
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
Keywords
generative AI, technology adoption, university students, UTAUT2, habit, performance expectancy, hedonic motivation, social influence, price value, facilitating conditions, behavioral intention, gender differences

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