Students' behavioural intention to use content generative AI for learning and research: A UTAUT theoretical perspective

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.