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

In brief: Teo, Scherer, Fung, and Fung meta-analyze 233 correlations from 32 studies (N = 16,977) to synthesize the factors shaping post-secondary students' adoption of AI. A three-level model yields moderate positive correlations for individual (r = 0.57), contextual (r = 0.53), and technological (r = 0.50) factors — no single category dominates — with usage intentions the strongest specific predictor (r = 0.64). The review argues the field over-relies on traditional technology acceptance models (TAM, UTAUT) and neglects AI-specific factors such as anthropomorphism and Ethics.

This meta-analysis addresses the scattered evidence on why tertiary students adopt AI for learning. Synthesizing 233 correlations from 32 studies across 16,977 participants, the authors group predictors into individual, contextual, and technological factors and use a three-level correlated-effects model. The result is a balanced picture: individual (r = 0.57), contextual (r = 0.53), and technological (r = 0.50) factors all matter moderately, with usage intentions the single strongest predictor (r = 0.64). Notably, perceived risks and Trust showed weaker associations than expected. Large heterogeneity (I² > 98%) was partly explained by construct diversity and students' prior experience with AI.

The review's theoretical contribution is its critique of the field's reliance on traditional technology acceptance models (TAM, UTAUT) that predate modern intelligent systems. It argues these frameworks miss AI-specific factors — such as anthropomorphism, ethics, and the adaptive/human-like nature of modern AI — and that addressing this gap matters for advancing theory and evidence-based policy. For practice, it recommends targeting specific constructs (learning objectives, intentions) and adapting integration to students' experience levels.

Key Findings

• 233 correlations from 32 studies (N = 16,977) synthesized via a three-level correlated-effects meta-analysis.
• Moderate positive correlations for individual (r = 0.57), contextual (r = 0.53), and technological (r = 0.50) factors — no single category dominates.
• Usage intentions are the strongest predictor (r = 0.64); perceived risks and trust showed weaker associations.
• Large heterogeneity (I² > 98%), partly explained by construct diversity and students' AI experience.
• The field over-relies on TAM/UTAUT and neglects AI-specific factors (anthropomorphism, ethics).
• Practice implication: target specific constructs and adapt integration to students' experience levels.

Connected Concepts

• Technology Acceptance Model — the frameworks the review critiques and builds on
• Higher Ed — the tertiary education context
• Student Experience — the adoption perspective
• AI Technologies — the tools being adopted
• Generative AI — a modern driver of adoption

Connected Articles

• ai-adoption — related adoption research
• Tam Critical Use GenAI Engineering 2026 — TAM applied to critical GenAI use in engineering
• Will Skill Not Tool Chinese University Students Acceptance Of Generative AI For — GenAI acceptance among Chinese university students
• Amponsah AI Acceptance Science Teachers 2026 — AI acceptance among science teachers
• Saihi Ahmed GenAI Adoption Personas Higher Ed 2026 — GenAI adoption personas in higher ed

Citation

Teo, T., Scherer, R., Fung, A. S. K., & Fung, C. S. L. (2026). Factors associated with students' adoption of artificial intelligence technology in tertiary education: A meta-analytic review. Educational Research Review, 52, 100804.