Digital leadership as an environmental determinant of teachers’ cognitive and affective mechanisms in artificial intelligence adoption: an integrated UTAUT2–GETAMEL model

Introduction The integration of artificial intelligence (AI) in education depends on teachers’ cognitive, affective, and motivational dispositions toward complex digital tools, yet limited research has examined how school leadership shapes these mechanisms. This study tested an integrated model positioning digital leadership as an environmental antecedent and combining the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) with the General Extended Technology Acceptance Model for E-Learning (GETAMEL) to explain teachers’ behavioral intention to adopt AI-based educational technologies. Methods A cross-sectional survey was administered to 477 teachers working in public and private schools in Türkiye. The model included digital leadership, self-efficacy, anxiety, perceived enjoyment, subjective norm, experience, perceived usefulness, perceived ease of use, attitude, facilitating conditions, habit, price value, and behavioral intention. Measurement and structural models were evaluated using Structural Equation Modeling. Results The integrated model demonstrated good model fit and explained 61% of the variance in behavioral intention, outperforming standalone UTAUT2 and GETAMEL models. Digital leadership positively predicted self-efficacy, perceived enjoyment, subjective norm, experience, and behavioral intention, while negatively predicting technology-related anxiety. Self-efficacy, enjoyment, experience, subjective norm, and anxiety influenced perceived usefulness and perceived ease of use. Perceived usefulness, perceived ease of use, and attitude were strong predictors of behavioral intention, while facilitating conditions, habit, and price value also directly contributed to intention. Discussion Teachers’ AI adoption is shaped not only by perceived utility but also by emotional regulation, intrinsic motivation, social expectations, and routinization processes. The findings suggest that school leaders should provide hands-on AI training, school-based support structures, low-risk experimentation opportunities, and clear school-level guidance to strengthen teachers’ readiness for AI adoption.