Integrating UTAUT-2 and protection motivation theory to explain pre-service teachers’ adoption of AI-supported game-based learning

Abstract

Despite growing interest in artificial intelligence (AI)–supported game-based learning (GBL), adoption among pre-service teachers remains uneven. Drawing on survey data from 864 pre-service teachers enrolled in public universities in Turkey, this study integrates the Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2) and Protection Motivation Theory (PMT) to examine motivational and psychological determinants of adoption intention. Structural equation modeling revealed that habit, hedonic motivation, performance expectancy, self-efficacy, and response efficacy positively predicted behavioral intention, whereas perceived response cost acted as a significant barrier. The integrated model explained 74% of the variance in behavioral intention, outperforming standalone UTAUT-2 and PMT models. These findings indicate that adoption decisions are shaped by both approach-oriented motivations and risk-related coping appraisals. Practically, teacher-education programs should prioritize repeated hands-on exposure to AI-supported GBL, strengthen pre-service teachers’ efficacy beliefs, and reduce perceived implementation costs through pedagogically grounded training and institutional support. As the sample was limited to public universities in Turkey, the findings may not generalize to private institutions, in-service teachers, or countries with differing teacher-education structures.