Research Article
Activity Theory as a Lens on Teachers' Adoption of AI Technologies
Synthesis: Lee & Granziera (2026) test activity theory as an explanatory framework for why some educators adopt AI tools while others remain reluctant, using factor analysis and structural equation modeling (SEM) on 557 primary, secondary, and pre-service teachers. Conceptualizing each of activity theory's six components (Individual/Subject, Objectives, Tools, Community, Rules/Regulations, Division of Labor) as measurable constructs, they find that Individual and Community components significantly explain teachers' Intention to adopt AI-driven tools. Objectives and Division of Labor were directly linked to Individual (and indirectly to Intention); Community linked to Rules/Regulations and Government, which were indirectly associated with Intention. The results suggest career stage and teaching level shape adoption, offering activity theory as an alternative to technology-acceptance framings of AI uptake in education.
Key Findings
- AT components are reliably measurable and explanatory. Factor analysis and SEM demonstrated that the six activity-theory components can be operationalized as latent constructs with explanatory power over teachers' intention to use AI tools (N = 557).
- Individual and Community drive intention. In the full sample, both the Individual (Subject) and Community components played significant roles in explaining teachers' Intention to adopt AI tools — pointing to both personal and collective/social drivers.
- Indirect pathways through Objectives and Division of Labor. 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.
- Stage and level matter. Primary teachers showed notable roles in intention, and pre-service teachers' results resembled — but were not identical to — in-service patterns, indicating adoption mechanisms differ across career stage and teaching level.
Implications for AI in Education
The study positions activity theory as a structural lens on teacher adoption of AI, complementing individual-belief models like TAM by foregrounding the community, rules, and division of labor that shape a teacher's decision to use AI. For practice, it implies that professional development and institutional adoption strategies should address not only individual attitudes but the collective activity system — community norms, regulations, and role divisions — in which teachers work. It connects to the knowledge base's Teacher Role and K 12 concepts and to the broader question of how teacher professional development should be designed for the AI era.
Connected Concepts
Connected Articles
- Lee Anson K12 Teachers AI Activity Theory — K-12 teachers' perspectives on AI use through activity theory
Citation
Lee, J., & Granziera, H. (2026). Activity theory as a lens on teachers' adoption of AI technologies: A structural equation modeling. Computers and Education Open, 10, 100349.