Factors Associated with Students’ Adoption of Artificial Intelligence Technology in Tertiary Education: A Meta-Analytic Review

This meta-analytic review investigates the individual, contextual, and technological factors associated with tertiary students’ adoption of artificial intelligence (AI) technology. Specifically, we synthesized 233 correlation coefficients from 31 studies and 32 samples (N = 16,977) in 24 countries, conducting a multilevel meta-analysis with the assumption of correlated effects. The meta-analytic correlations of the individual, contextual, and technological factors with AI adoption were moderate, positive, and ranged between r ̅s = .50-.56. Within the three broad categories of factors, the correlations exhibited large overall heterogeneity (I^2 above 98%), which could be partly explained by the diversity of constructs and the student samples’ experience with AI. Usage intentions, self-beliefs, and general attitudes towards AI were the strongest individual factors (r ̅s = .60-.64), whereas social norms and influences were the strongest contextual factors (r ̅ = .56). Perceptions of the usefulness, relevance, and impact of AI technologies were driving technological factors (r ̅ = .56). These results point to the importance of considering individual, contextual, and technological factors when examining students’ AI adoption and highlight the need to explore how to leverage social norms and enhance perceived usefulness to encourage AI engagement within student populations in tertiary education.