Research Article
Şeyma Yıldırım-Samuk, İsmail Fırat Altay
CONT ED TECHNOLOGY, Volume 18, Issue 3, Article No: ep666
ABSTRACT
Generative artificial intelligence (GenAI) has recently gained significant attention within educational contexts, particularly among university students. Regardless of its significance, there are still a limited number of empirical studies on graduate students’ intention to use this technology in higher education settings. Accordingly, this study investigated the acceptance of GenAI tools by master’s and doctoral students for academic purposes. The research adapted the unified theory of acceptance and use of technology 2 model and surveyed 145 graduate students from various universities through convenience sampling. Data collected through online surveys were analyzed using the partial least squares approach to structural equation modelling. Key findings revealed that factors, including habit (HB), performance expectancy, and hedonic motivation, had a significant effect on students’ behavioral intention (BI) to use GenAI tools. Additionally, the study indicated that the most important predictors for actual GenAI use were HB and BI. Notably, demographic variables, age, gender, and the level of study, showed no significant moderating influence on the relationships among the constructs. This study provides further insight into our understanding of how GenAI tools are accepted by graduate students for academic purposes and contribute to the literature on the factors affecting their intention to use these tools.
Keywords: generative artificial intelligence, UTAUT2, higher education, academic purposes
Research Article
Olga V. Sergeeva, Marina R. Zheltukhina, Tatyana Shoustikova, Leysan R. Tukhvatullina, Denis A. Dobrokhotov, Sergey V. Kondrashev
CONT ED TECHNOLOGY, Volume 17, Issue 2, Article No: ep571
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.
Keywords: UTAUT2, generative AI, higher education, adoption of AI, hedonic motivation, habit