A systematic review of UTAUT and UTAUT2 for AI adoption in education - Xue, Liangyong; Ghazali, Norliza; Mahat, Jazihan (Taylor and Francis, 2025)
Artificial Intelligence (AI) is transforming education, yet its adoption remains challenging. The Unified Theory of Acceptance and Use of Technology (UTAUT) and UTAUT2 offer structured frameworks for analyzing adoption, but their application in AI education lacks systematic review. This study examines UTAUT/UTAUT2 applications, hypothesis validation, and model extensions in AI adoption. Findings reveal a surge in research, led by China, with higher education dominating while K-12 remains underexplored. UTAUT is widely applied holistically, while UTAUT2 constructs are selectively implemented, showing greater hypothesis variability. Model extensions primarily introduce new Behavioral Intention predictors, with limited focus on moderators or outcomes. Individual and Technology Characteristics dominate, while Environmental factors receive less attention. To address conceptual redundancy and contextual misalignment, the Integrated AI-in-Education Acceptance Framework (IAEAF) is proposed. This study provides insights into UTAUT/UTAUT2’s application and extension in AI education and identifies areas for further theoretical and methodological refinement.