Psychological Mechanisms of Generative AI Discontinuance Intention among Chinese K-12 Teachers
For educational technology researchers and policymakers, this work identifies psychological mechanisms driving teacher discontinuance of generative AI, though it is an incremental application of existing frameworks to a specific context.
This study investigated why Chinese K-12 teachers discontinue using generative AI, finding that privacy concerns, algorithmic opacity, and information hallucination increase AI anxiety and discontinuance intention, while perceived intelligence, personalization, and interactivity enhance satisfaction and reduce discontinuance intention.
This study examines the psychological mechanisms underlying Chinese K-12 teachers' discontinuance intention toward generative AI. Drawing on the Cognition-Affect-Conation framework, the study investigates how cognitive evaluations of generative AI shape affective responses and subsequently influence behavioural intention. Survey data from 256 Chinese K-12 teachers were analysed using structural equation modelling and fuzzy-set qualitative comparative analysis. The results showed that privacy concern, algorithmic opacity, and information hallucination increased AI anxiety, which in turn strengthened discontinuance intention. Conversely, perceived intelligence, perceived personalisation, and perceived interactivity enhanced satisfaction, which reduced discontinuance intention. The configurational analysis further identified multiple pathways leading to high discontinuance intention, highlighting the combined roles of technological risks, AI anxiety, weak affordance perceptions, and low satisfaction. These findings extend research on post-adoption generative AI use in education and suggest that sustainable integration requires both reducing technological uncertainty and enhancing teachers' positive user experiences.