article · Education and Information Technologies
Generative artificial intelligence offers significant potential to enhance student learning and academic research, yet the drivers behind student adoption remain insufficiently understood in Nigerian higher education institutions. An assessment of 289 computer science students at a state university in northern Nigeria evaluated six behavioural factors based on the unified theory of acceptance and use of technology. Structural equation modelling revealed that performance expectancy, effort expectancy, and social influence serve as significant determinants shaping student intentions to use content generative artificial intelligence tools. Conversely, factors including facilitating conditions, perceived risks, and general attitudes towards technology did not demonstrate a significant effect on adoption intentions. Understanding these specific influences provides university administrators and policymakers with clearer empirical evidence to shape strategies, support mechanisms, and institutional policies that guide students towards productive educational uses of generative artificial intelligence systems.
Higher education administrators and policymakers need empirical evidence to manage the integration of generative artificial intelligence into academic environments. By clarifying that perceived usefulness, ease of use, and peer influence drive student adoption more than perceived risks or institutional conditions, institutional leaders can design targeted training and supportive policies that align directly with actual student motivations for learning and research.
This research provides early-stage behavioural insight for educational technology developers, university administrators, and policy planners seeking to introduce artificial intelligence tools into higher education. Product developers can tailor onboarding, user interfaces, and collaborative features around ease of use and perceived academic utility. Because the findings derive from an empirical survey rather than a tested product intervention, application remains at an early diagnostic stage focused on informing implementation strategies.
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Abstract Generative Artificial Intelligence tools have the potential to impact students learning significantly and positively in several ways. However, the factors responsible for student’s behavioural intentions to use these tools are still not fully understood, especially in the context of Nigerian higher education institutions (HEIs). To support students use of Content Generative - Artificial Intelligence (CG-AI) tools for learning and research purposes, it is important that HEI administrators and policy makers understand these factors. Therefore, the purpose of this study is to examine the factors that influence Nigerian students’ behavioural intentions to use CG-AI tools for learning and research. Based on structural equation modelling technique, this study uses the unified theory of acceptance and use of technology (UTAUT) to examine the relationship between six constructs and students’ behavioural intentions to use CG-AI. Employing a paper-based survey, responses from 289 students in the Department of Computer Science were obtained from a State University in northern Nigeria. A two-step approach (Confirmatory Factor Analysis and Path Analysis) was used to analyse the relationships between both observed and latent variables. The findings showed that three of the factors, performance expectancy (α = 0.551, p < 0.001), effort expectancy (α = 0.466, p < 0.001), and social influence (α = 0.507, p < 0.001) were observed to be determinants of behavioural intentions to use CG-AI tools. Facilitating conditions, perceived risks, and attitude towards technology, on the other hand, showed no significant impact on students’ behavioural intention to use CG-AI tools.
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DOI: 10.1007/s10639-025-13441-8
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