Beyond Adoption: Explaining Student Engagement with AI through an Extended TAM–UTAUT Framework

Abstract

Artificial Intelligence (AI) is increasingly integrated into higher education, yet existing studies mainly focus on students’ intention to use technology rather than their actual engagement with AI learning tools. This study examines the factors influencing student engagement with AI among Generation Z students in Malaysian private higher education institutions. The study extends the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating technological, social, and psychological factors to explain student engagement with AI. A quantitative cross-sectional design was adopted using an online survey questionnaire, with data collected from 304 students. Multiple regression analysis was conducted to examine the relationships between these factors and student engagement. The findings indicate that perceived usefulness, performance expectancy, social influence, perceived trust, and perceived risk significantly and positively influence student engagement, while perceived ease of use is insignificant. The positive relationship between perceived risk and engagement suggests that students may perceive the benefits of AI as outweighing potential risks. The study contributes to the technology acceptance literature by extending TAM and UTAUT beyond behavioural intention towards engagement outcomes and offers practical insights for higher education institutions implementing AI in learning environments.