Date
Publisher
arXiv
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.
Study at a glance
- Question: What psychological factors and combinations lead Chinese K–12 teachers to consider discontinuing generative AI use?
- Setting and data: Online survey of 256 Chinese K–12 teachers with prior generative AI experience across primary, junior secondary, and senior secondary schools.
- Method: Cross-sectional survey analysed using structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA).
Why it matters
Teachers may abandon generative AI when perceived risks outweigh benefits. Little research examines why K–12 teachers intend to stop using these tools after initial experimentation, despite balancing instructional innovation with professional responsibilities and content accuracy.
Key findings
- AI anxiety was positively associated with discontinuance intention, whereas user satisfaction was negatively associated with teachers' intention to discontinue generative AI.
- Concerns about information hallucination, algorithmic opacity, and privacy positively predicted AI anxiety, with hallucination showing the strongest association.
- Perceived intelligence, personalisation, and interactivity enhanced satisfaction, while configurational analysis identified three combinations of technological risks, anxiety, and low satisfaction leading to discontinuance.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Why use AI?
Study design
