| Sumario: | 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. Practitioner Notes What is currently known about the subject matter: ○Generative AI has transformed education by improving teaching efficiency, lesson planning and content creation.○Teachers may become overly reliant on AI, leading to reduced engagement in independent decision‐making and problem‐solving.○Previous studies on digital addiction have focused on gaming and social media, with limited research on AI dependence in professional settings.What this paper adds: ○Demonstrates that cognitive absorption is the strongest predictor of AI addiction among teachers.○Finds that perceived usefulness, self‐efficacy and positive affect indirectly contribute to AI reliance, while mood regulation and need for cognition do not.○Expands the I‐PACE model to include AI addiction in education, offering new insights into behavioural and psychological mechanisms.Implications of study findings for practitioners: ○Teachers should be trained to use AI as a supportive tool rather than becoming dependent on it for instructional tasks.○Institutions should implement policies that encourage balanced AI integration while preserving teachers' autonomy.○Policymakers and AI developers should design AI tools that foster active teacher engagement rather than passive reliance.
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