Modelling Pre-Service English Teachers' Readiness for AI Integration: A TPACK–TAM Mixed-Methods Study
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Artificial Intelligence (AI), particularly large language models such as ChatGPT, has advanced rapidly recently, revolutionizing English Language Teaching (ELT); nonetheless, its pedagogically meaningful integration remains uneven and contingent on teacher preparation. Emerging research indicates that AI adoption is shaped more by teachers’ professional knowledge and acceptance views than by technological hurdles. However, empirical information on their interaction, particularly in underexplored contexts, remains scarce. Using an integrated Technological Pedagogical Content Knowledge (TPACK) and Technology Acceptance Model (TAM) framework, this study investigates pre-service English teachers' preparedness for AI integration, conceptualizing readiness as competence-informed acceptance, a novel construct that differs from traditional readiness frameworks by emphasizing the cognitive professional interplay between knowledge and beliefs rather than mere willingness or attitude. An explanatory sequential mixed-methods single-case design was utilized, with survey data (n = 78) analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and qualitative responses examined through reflexive thematic analysis. The results demonstrated that pedagogical knowledge was the strongest predictor of reported usefulness (β = 0.607, p < 0.001) and perceived ease of use (β = 0.546, p < 0.001). Prior AI experience directly predicted intention (β = 0.208, p < 0.001) and moderated the usefulness–intention link (β = 0.061, p = .044), although perceived ease of use had a greater impact on planned future use (β = 0.299, p < 0.001) than perceived usefulness (β = 0.192, p = 0.003). The qualitative results identified the importance of pedagogical rationale and context limitations. The research contributes to the theory, as it combines TPACK and TAM and offers context-related evidence in the MENA region, which supports the preparation of AI in ELT with pedagogy as a priority. Qualitative findings highlighted the role of pedagogical reasoning and contextual constraints. The study advances theory by integrating TPACK and TAM, demonstrating that professional knowledge operates indirectly through acceptance beliefs, and provides context-sensitive evidence from the Middle East and North Africa (MENA) region, supporting pedagogy-first AI preparation in ELT.
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