Primary teachers’ acceptance and sustained adoption of AI powered learner corpora for writing instruction through TAM and ECM perspectives

Artificial intelligence (AI) offers significant potential to enhance writing instruction in primary education. However, its sustained adoption by English language teachers remains insufficiently understood. This study examines the factors influencing teachers' acceptance and continued use of AI-powered learner corpora, drawing on the Technology Acceptance Model (TAM) and Expectation Confirmation Model (ECM). A structured questionnaire was administered to 355 primary school English teachers in Malaysia to assess variables related to external support (e.g., facilitating conditions, teacher-learner interaction), individual characteristics (e.g., perceived self-efficacy, expectancy effects, growth mindset, interest) and technology perceptions (e.g., perceived ease of use, perceived usefulness, satisfaction, continuance intention). Data were analyzed using exploratory and confirmatory factor analysis, followed by Structural Equation Modeling. Results highlight perceived self-efficacy and interest as key predictors of perceived usefulness and continuance intention. They underscored the role of intrinsic motivation. In contrast, facilitating conditions did not significantly affect perceived ease of use, possibly due to increased digital familiarity among teachers. These findings validate the applicability of TAM and ECM in the primary education context and offer practical insights for designing AI tools that align with teachers' pedagogical needs, enhance user experience and support the sustainable integration of AI in writing instruction.