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This empirical study applies UTAUT2 to examine adoption drivers among Pyatigorsk State University students, analyzed via structural equation modeling. Results highlight habit and performance expectancy as key predictors, with hedonic motivation, social influence, and price value supporting intention, while facilitating conditions negatively affect it.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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\nUnderstanding higher education students’ adoption of generative AI technologies: An empirical investigation using UTAUT2  \nOlga V. Sergeeva 1  \n 0000-0002-9950-000X  \nMarina R. Zheltukhina 2*  \n 0000-0001-7680-4003  \nTatyana Shoustikova 3  \n 0000-0002-0404-6693  \nLeysan R. Tukhvatullina 4  \n 0009-0004-9133-9567  \nDenis A. Dobrokhotov 5  \n 0000-0002-6069-4978  \nSergey V. Kondrashev 5  \n 0000-0002-6547-865X  \n1 Kuban State University, Krasnodar, RUSSIA  \n2 Scientific and Educational Center «Person in Communication», Pyatigorsk State University, Pyatigorsk, RUSSIA  \n3 Peoples’ Friendship University of Russia named after Patrice Lumumba, Moscow, RUSSIA  \n4 Kazan (Volga region) Federal University, Kazan, RUSSIA  \n5 I. M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, RUSSIA  \n* Corresponding author: [zzmr@mail. ru](zzmr@mail. ru)  \nCitation: Sergeeva, O. V., Zheltukhina, M. R., Shoustikova, T., Tukhvatullina, L. R., Dobrokhotov, D. A., & Kondrashev, S. V.(2025) . Understanding higher education students’ adoption of generative AI technologies: An empirical investigation using UTAUT2. Contemporary Educational Technology, 17(2), ep571. [https://doi.org/10.30935/cedtech/16039](https://doi.org/10.30935/cedtech/16039)  \nARTICLE INFO ABSTRACT  \nReceived: 9 Dec 2024 Generative artificial intelligence (GAI) technologies are gaining traction in higher education, Accepted: 13 Feb 2025 offering potential benefits such as personalized learning support and enhanced productivity.  \nHowever, 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.  \nKeywords: UTAUT2, generative AI, higher education, adoption of AI, hedonic motivation, habit  \nINTRODUCTION  \nGenerative artificial intelligence (GAI) refers to a class of machine learning models and algorithms capable of generating new data, such as text, images, audio, or video, based on the patterns learned from training data (Alasadi & Baiz, 2023; Kohnke et al., 2023; Ofem & Chukwujama, 2024. These models use deep learning techniques, like variational autoencoders, generative adversarial networks, and large language models, to capture the underlying structure and patterns in the training data, allowing them to create novel and realistic outputs (Ramesh et al., 2022; Zastudil et al., 2023; Zhang & Aslan, 2021) . Notable examples of GAI systems include image generators like DALL-E, St","cbCaio8YWMdFVEmo","https://ap.wps.com/l/cbCaio8YWMdFVEmo","pdf",383451,21,"English","# Introduction\n## Generative AI in higher education\n## Purpose of the study\n## Benefits of AI implementation","[{\"question\":\"What is the focus of the study on GAI in higher education?\",\"answer\":\"The study examines the factors influencing higher education students’ adoption and usage of generative AI technologies, using the UTAUT2 framework to interpret behavioral intentions.\"},{\"question\":\"Which factors most strongly predict students’ adoption of generative AI?\",\"answer\":\"Habit (HB) is identified as the most influential predictor, followed by performance expectancy.\"},{\"question\":\"How do facilitating conditions affect students’ behavioral intention to use GAI?\",\"answer\":\"Facilitating conditions (FCs) show a negative effect on behavioral intention, suggesting potential gaps in support systems.\"}]","Understanding higher education students’ adoption of generative AI technologies: An empirical investigation using UTAUT2 | PDF",1792090736,53]