Are Teachers Addicted to <scp>AI</scp> ? Analysing Factors Influencing Dependence on Generative <scp>AI</scp> Through the I‐ <scp>PACE</scp> Model

Abstract

ABSTRACT Background The integration of generative artificial intelligence (AI) into education has revolutionised teaching practices, offering educators advanced tools for lesson planning, content creation, personalised learning and administrative automation. While AI enhances efficiency and instructional effectiveness, concerns have emerged regarding teachers' potential overreliance on these technologies, leading to AI addiction. Objectives This study applies the I‐PACE model (Interaction of Person‐Affect‐Cognition‐Execution) to explore the psychological and behavioural mechanisms underlying teachers' dependence on generative AI. Methods Using survey data from 1750 teachers in Huanghua, China, the study examines factors such as self‐efficacy, need for cognition, mood regulation, positive affect, perceived usefulness and cognitive absorption in shaping AI addiction. Results Findings indicate that cognitive absorption is the strongest predictor of AI dependence, while perceived usefulness, self‐efficacy and positive affect contribute indirectly through reinforcement mechanisms. Notably, mood regulation and need for cognition do not significantly influence AI addiction, suggesting that AI engagement in education is driven more by functional efficiency than emotional dependence. Conclusions The results highlight the importance of fostering mindful AI integration in teaching to prevent habitual overreliance. This study provides theoretical contributions by extending the I‐PACE model to the context of AI addiction in education and offers practical insights for educators, institutions and policymakers in promoting responsible AI use while maintaining teachers' professional autonomy and cognitive engagement.

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