Understanding Teachers’ Adoption of AI: Insights from Innovation Diffusion and Social Cognitive Theories

Aim/Purpose: This study investigates the personal and contextual factors that influence teachers’ behavioral adoption of artificial intelligence (AI) in educational settings by integrating innovation diffusion theory (IDT) and social cognitive theory (SCT). Specifically, the study investigates how teachers’ perceptions of AI (relative advantage, compatibility, trialability/observability), cognitive beliefs, and environmental support shape their intention and behavior toward adopting AI tools. The study also explores the moderating role of environmental support and the mediating role of cognitive beliefs within this adoption process. Background: AI technologies offer significant potential to transform education, but their successful adoption in education depends on teachers’ intention and willingness to utilize them. This study provides an understanding of teachers’ AI adoption behaviors in the UAE, a country that aims for AI-driven educational transformation. Methodology: Using a quantitative cross-sectional design, data were collected from 249 teachers across multiple educational levels. A validated survey was used, and data analysis involved hierarchical multiple regression, structural equation modeling (SEM), and moderation and mediation analyses using SPSS and SmartPLS. Contribution: Unlike prior studies, this research uniquely integrates IDT and SCT in the context of K–12 education in the UAE. These results highlight the need for multidimensional strategies that combine institutional support, experiential learning opportunities, and cognitive engagement to promote effective AI integration in education. The study contributes to theory by demonstrating the value of an integrated IDT-SCT framework and offers actionable insights for educational leaders, policymakers, and professional development designers. Findings: The findings indicate that relative advantage and trialability/observability are the strongest predictors of behavioral adoption, while cognitive beliefs and environmental support also play significant and complementary roles. Furthermore, environmental support moderates the influence of compatibility and trialability/observability on adoption, and cognitive beliefs mediate the effects of relative advantage and compatibility. Recommendations for Practitioners: Educational leaders should provide strong institutional support, hands-on opportunities to trial AI tools, and training that fosters positive cognitive beliefs to facilitate effective AI adoption. Recommendation for Researchers: Educational leaders should provide strong institutional support, hands-on opportunities to trial AI tools, and training that fosters positive cognitive beliefs to facilitate effective AI adoption. Impact on Society: This study fills a critical gap by integrating IDT and SCT to explain teachers’ adoption of AI in K–12 education in the UAE, a context that has received limited prior attention. The findings inform efforts to promote equitable and effective integration of AI in education, aligned with sustainable development goals and national innovation strategies. This contributes to the development of future-ready education systems that prepare students for the demands of a digital and AI-driven world. Future Research: Future research should investigate how teachers’ adoption of AI changes longitudinally and across different educational levels and contexts. Studies could explore the impact of AI literacy initiatives, leadership styles, and institutional culture on adoption behaviors.