Assessing attitudes and impact of AI integration in higher education

Purpose This study aims to examine the integration of AI in student engagement and its benefits in the learning environment. Design/methodology/approach The study employed a quantitative research method, analyzing data from a sample of 720 students. The econometric data analysis used the structural equation modeling (SEM) technique. Findings The results show that facilitating conditions and performance expectations positively affect students’ attitudes toward AI, whereas the negative impact of perceived risk is statistically weak and only marginally significant at the 90% level. On the other hand, the main findings are that attitudes significantly influence the use of AI, which consequently increases students’ productivity, performance, and self-efficacy. Research limitations/implications Through the integration of new variables in the TAM and UTAUT models, steps are suggested for institutions to take to increase the acceptance and efficiency of AI. Originality/value This study introduces a novel approach to AI integration within higher education, presenting an innovative model that significantly enhances the discourse on AI’s tangible impacts on educational processes.