Structural path analysis.

Objective
The adoption of Generative AI in education presents both opportunities and challenges, particularly regarding its potential to foster student dependency. However, the psychological drivers of this dependency remain unclear. This study addresses this gap by applying the Interaction of Person-Affect-Cognition-Execution (I-PACE) model and Basic Psychological Needs (BPN) theory to explore how specific personality traits—neuroticism, self-critical perfectionism, and impulsivity—contribute to AI dependency through needs frustration, negative academic emotions, and reinforced performance beliefs.
Method
Data were collected from 958 university students (Mage = 21.67) across various disciplines. Structural equation modeling (SEM) was used to analyze the relationships among the variables.
Results
Neuroticism, self-critical perfectionism, and impulsivity were found to be significantly associated with increase needs frustration and negative academic emotions, which in turn reinforced students’ positive beliefs about performance of AI tools, deepening their dependency. The study also uncovered complex serial mediation effects, highlighting intricate psychological pathways that drive maladaptive AI use.
Conclusions
This research provides a critical insight into the interplay between personality traits and technology use, shedding light on the nuanced ways in which individual differences influence dependency on generative AI. The findings offer practical strategies for educators to promote balanced AI use and support student well-being in educational settings.