Artificial intelligence (AI) has emerged as a prominent research focus in higher education. Despite increased interest, significant gaps remain in theory and knowledge, particularly regarding emerging factors such as self-efficacy (SE) and perceived trust (PT), and these gaps are especially pronounced among demographics like Gen Z. This study examines the adoption of AI technology among Gen Z university students, focusing on the influence of SE and PT. It also explores how gender, course major, and experience may moderate AI adoption. A total of 349 students were sampled, and the study employed an extended UTAUT, structural equation modelling, and multigroup analysis. This study has provided an improved and tested theoretical framework to enhance understanding of AI adoption in the field, with an R² value of 0.751. Behavioural Intention (BI) is influenced by SE, PT, and Perceived Expectancy (PE), with a diminishing role of Social Influence (SI), suggesting a more individualistic viewpoint among Gen Z, besides emphasising efficacy and trust in technology. Experience influences all the predictive factors, but its impact diminishes as experience increases, while gender has no impact on the adoption of AI. Additionally, the course major also influences the predictive effect. The findings imply that for the successful adoption of AI among Gen Z university students, universities, policymakers, and Big Tech should minimise the burden on efficacy and foster elements of trust, such as data security and privacy. However, the findings may also reflect Gen Z’s intention to adopt technology beyond the academic sphere.
Rosli et al. (Tue,) studied this question.