Are Teachers Addicted to AI? Analysing Factors Influencing Dependence on Generative AI through the I-PACE Model
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| Title: | Are Teachers Addicted to AI? Analysing Factors Influencing Dependence on Generative AI through the I-PACE Model |
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| Language: | English |
| Authors: | Yiran Du (ORCID |
| Source: | Journal of Computer Assisted Learning. 2026 42(1). |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 16 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Artificial Intelligence, Technology Uses in Education, Addictive Behavior, Teacher Behavior, Self Efficacy, Emotional Response, Affective Behavior, Value Judgment, Cognitive Processes, Predictor Variables, Foreign Countries, Personality Traits |
| Geographic Terms: | China |
| DOI: | 10.1002/jcal.70174 |
| ISSN: | 0266-4909 1365-2729 |
| 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. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1495877 |
| Database: | ERIC |
| 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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| ISSN: | 0266-4909 1365-2729 |
| DOI: | 10.1002/jcal.70174 |
