The relationship between inert thinking and ChatGPT dependence: An I-PACE model perspective

ChatGPT, as an example of generative artificial intelligence, possesses high-level conversational and problem-solving capabilities supported by powerful computational models and big data. However, the powerful performance of ChatGPT might enhance learner dependency. Although it has not yet been confirmed, many teachers and scholars are also concerned about this issue. Therefore, it is necessary to investigate this topic further. This study’s objective is to explore the association between inert thinking, positive experiences with ChatGPT, avoidance learning motivation, and ChatGPT dependence, based on the Interaction of Person-Affect-Cognition-Execution (I-PACE) model. Employing a cross-sectional design, we conducted an online survey with 870 Taiwanese university students, who had an average age of 22.81 years. The study found that inert thinking was positively associated with both positive experiences with ChatGPT and ChatGPT dependence. Furthermore, a significant association was found between inert thinking and avoidance learning motivation. Positive experience with ChatGPT was also positively related to avoidance learning motivation and ChatGPT dependence. Due to the scarcity of empirical research on generative artificial intelligence, the issues that people worry about when discussing AI were confirmed in this study. Moreover, avoidance learning motivation was positively correlated with ChatGPT dependence. Based on these findings, this study calls for educators to help students overcome inert thinking and avoidance learning motivation to prevent dependency on emerging technologies.