Psychological Mechanisms of Generative AI Discontinuance Intention among Chinese K-12 Teachers
Yiran Du, Qian Chen, Huimin He

TL;DR
This study explores psychological factors influencing Chinese K-12 teachers' intention to discontinue using generative AI, highlighting the roles of AI anxiety, satisfaction, and technological perceptions.
Contribution
It applies the Cognition-Affect-Conation framework with structural equation modeling and fuzzy-set analysis to identify pathways affecting discontinuance intention.
Findings
AI anxiety increases discontinuance intention due to privacy, opacity, and hallucination concerns.
Perceived intelligence, personalization, and interactivity reduce discontinuance by boosting satisfaction.
Multiple pathways to high discontinuance involve technological risks, low satisfaction, and weak affordances.
Abstract
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,…
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