Understanding Students’ Acceptance of Generative AI in Higher Education: The Roles of Perceived Usefulness, Trust, and Digital Competence

Generative artificial intelligence (GenAI) has rapidly entered higher education, creating
new opportunities for academic support while raising concerns about reliability, privacy,
academic integrity, and responsible use. Student acceptance is therefore unlikely to be
determined by perceived usefulness alone. This paper synthesizes evidence on three central
determinants of acceptance—perceived usefulness, trust, and digital competence—and
examines how these constructs interact within a coherent explanatory framework. The
synthesis focuses on university students’ perceptions, technology-acceptance models, AI
literacy, trust, ethical concerns, and the educational use of generative AI and conversational
systems. The findings indicate that perceived usefulness is a consistent driver of behavioral
intention, while trust is influenced by perceived accuracy, transparency, privacy, reliability,
and responsible use. Digital competence functions as an enabling condition that helps
students understand AI behaviour, evaluate outputs, recognize limitations, and use GenAI
critically and effectively. The synthesis suggests that acceptance can be understood as a
pathway in which digital competence shapes perceptions of usefulness and trust, which
subsequently influence attitudes and behavioral intentions, while ethical and institutional
conditions affect the transition from intention to actual use. Based on these findings, the
paper proposes an integrated conceptual model for understanding GenAI acceptance in
higher education. The findings frame acceptance not as a single attitude, but as an
interaction among perceived value, trust, competence, and contextual governance,
providing practical implications for responsible and learner-centred GenAI adoption.