A Capability–Decision Model of teacher readiness for AI integration in teaching
Résumé
This conceptual article develops the Capability–Decision Model for AI Integration Readiness (CDM-AIR), a theoretically grounded framework for explaining teacher readiness to integrate artificial intelligence (AI) in teaching. The model addresses a recurring weakness in studies that combine Technological Pedagogical Content Knowledge (TPACK) with belief-based adoption frameworks by treating knowledge and belief constructs as parallel predictors of intention and use, an approach that produces construct overlap, ambiguous causal ordering, and limited intervention guidance. Engaging recent AI-adapted TPACK scholarship, CDM-AIR positions AI-TPACK as an upstream capability system that informs attitude and perceived behavioural control, while the Theory of Planned Behavior (TPB) constitutes the proximal decision pathway through which intention and behaviour are formed. Contextual facilitating conditions are modelled as antecedents of subjective norm and perceived behavioural control and as moderators of the perceived control–behaviour link. The article specifies thirteen hypotheses, declares the model's boundary conditions regarding AI co-agency, states the empirical patterns that would disconfirm the model, provides a worked operationalisation of performance-based capability measurement with explicit discriminant-validity criteria, and outlines an empirical testing strategy. By separating competence from belief and treating context as both antecedent and boundary condition, CDM-AIR offers a disciplined, falsifiable basis for diagnosing readiness barriers and designing context-responsive interventions in teacher education and policy.
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