Activity theory as a lens on teachers’ adoption of AI technologies: A structural equation modeling

• Teachers’ adoption of AI tools is driven by individual choices and practices • All six Activity Theory components are linked to teachers’ AI adoption patterns • Adoption patterns differ between primary and secondary school teachers Despite the growing use of Artificial Intelligence (AI) technologies in education, the underlying mechanisms that account for why some educators are inclined to adopt AI while others remain reluctant to do so remain unclear. The present study examined primary, secondary, and pre-service teachers’ intentions to use AI tools in K-12 educational settings. Drawing from Activity Theory, we tested six hypotheses illustrating the complex interrelationships among AT-related components linked to teachers’ intentions. Our factor analysis and structural equation modeling (SEM) demonstrated that AT components can be reliably measured and have explanatory power regarding teachers’ intentions to adopt AI-driven tools. In the full sample analysis (N = 557), both Individual and Community components played significant roles in explaining teachers’ Intention. Objectives and Division of Labor were directly linked to Individual and indirectly associated with Intention. Community was linked to Rules/Regulations and Government , which were indirectly associated with Intention . We also found notable differences when comparing secondary and primary school teachers. Division of Labor and Community had stronger effects among secondary teachers. On the other hand, Objectives and Individual played more prominent roles among primary teachers in their intention to use AI tools. The results for pre-service teachers resembled those of primary school teachers. Overall, the present study highlights the complex mechanisms through which teachers at different career stages and teaching levels may adopt AI-driven tools. Theoretical and practical implications are discussed in conclusion.