Work

Multidimensional engagement effects of generative AI adoption among minority ethnic international students in UK higher education

Year: 2026

Type: article

Abstract: 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. Practitioner notes What is already known about this topic? Generative AI (GAI) technologies are increasingly used by students in higher education for learning support. The Unified Theory of Acceptance and Use of Technology (UTAUT) has been widely applied to examine technology adoption in educational contexts. What this paper adds? It extends UTAUT to an under‐researched population (MEIS), who often face heightened academic, technological and cultural challenges in UK higher education. GAI use by MEIS is strongly associated with cognitive, behavioural, emotional and especially agentic engagement. Implications for practice and/or policy Institutions should consider embedding GAI literacy and access support into orientation and academic skills programmes for MEIS. Educators who integrate GAI into teaching may consider structured questioning, verification and reflection activities while evaluating rather than assuming their effects on engagement. Policymakers should develop equity‐oriented strategies that support the digital and cultural integration of disadvantaged student groups.

Source: British Journal of Educational Technology

DOI: 10.1111/bjet.70093

Authors: Olatunji David Adekoya, Hakeem Adeniyi Ajonbadi, Chima Mordi

Institutions: Sheffield Hallam University, University of Doha for Science and Technology

Language: English

Topic: Artificial Intelligence in Education

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDGs: Quality education, Reduced inequalities

Open Access status: hybrid

Estimated APC paid: $4,250