Psychological Mechanisms of Generative AI Discontinuance Intention among Chinese K-12 Teachers

2026-05-15

AI Summary

This study investigates the psychological mechanisms underlying Chinese K–12 teachers’ discontinuance intention toward generative AI, with a focus on the interplay among cognition, affect, and behavioral intention. Grounded in the cognition–affect–conation framework, the research integrates structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to demonstrate that perceived technological risks—such as privacy concerns, algorithmic opacity, and AI hallucinations—heighten AI-induced anxiety, whereas perceived value dimensions—including intelligence, personalization, and interactivity—enhance user satisfaction; both pathways jointly shape discontinuance intention. The analysis further uncovers multiple configurational pathways leading to high discontinuance intention, offering theoretical and practical insights into educators’ technology abandonment behaviors.

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, 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.

Problem

• generative AI
• discontinuance intention
• K-12 teachers
• psychological mechanisms
• AI anxiety

Innovation

• Cognition-Affect-Conation framework
• generative AI discontinuance
• fuzzy-set qualitative comparative analysis
• AI anxiety
• teacher technology adoption