Multidimensional Engagement Effects of Generative AI Adoption among Minority Ethnic International Students in UK Higher Education

Generative artificial intelligence (GAI) has become a prevalent tool in higher education, yet little is known about how its use affects the learning experiences of minority-ethnic international students (MEIS). Drawing on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) and a four-strand engagement model, this quantitative study surveyed 273 MEIS across UK business schools and analysed the data with Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings suggest that performance expectancy, effort expectancy and social influence predicted intention, and facilitating conditions and intention were also positively associated with self-reported GAI use frequency. GAI frequency positively predicted cognitive, behavioural, emotional and, most strongly, agentic engagement. Multigroup analysis revealed that female students showed stronger effects of effort expectancy, performance expectancy, social influence and facilitating conditions on intention than males; low-experience students were more sensitive to effort expectancy than high-experience peers; and postgraduate students exhibited stronger facilitating conditions, performance expectancy and social influence effects than undergraduates. The study highlights both theoretical and practical implications by linking GAI adoption with multidimensional student engagement, with the strongest association observed for agentic engagement, and calls for equity-focused policy, curriculum interventions and institutional support targeted at individual access gaps among MEIS.