Are Teachers Addicted to AI? Analyzing Factors Influencing Dependence on Generative AI Through the I-PACE Model

The integration of generative artificial intelligence (AI) into education has revolutionized teaching practices, offering educators advanced tools for lesson planning, content creation, personalized learning, and administrative automation. While AI enhances efficiency and instructional effectiveness, concerns have emerged regarding teachers’ potential over-reliance on these technologies, leading to AI addiction. This study applies the I-PACE model (Interaction of Person-Affect-Cognition-Execution) to explore the psychological and behavioral mechanisms underlying teachers’ dependence on generative AI. Using survey data from 1750 teachers, the study examines factors such as self-efficacy, need for cognition, mood regulation, positive affect, perceived usefulness, and cognitive absorption in shaping AI addiction. 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. The results highlight the importance of fostering mindful AI integration in teaching to prevent habitual over-reliance. 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.