Addicted to AI? Prevalence and psychological drivers of generative AI dependency among pre-service teachers.

INTRODUCTION

Generative artificial intelligence (GenAI) has become embedded in higher education, raising concerns about students' behavioral dependency on these tools. Focusing on pre-service teachers-who will shape how a future generation learns to use AI-we asked how prevalent GenAI dependency is, what drives it, and whether perceived AI literacy protects against it.

METHODS

In a mixed-methods design, 565 education-major students in Chinese teacher-preparation programs completed an online survey, and five were interviewed.

RESULTS

Dependency markers were already widespread: 79% called AI indispensable to their learning, 78% felt unaccustomed without it, 54% "asked AI first," and 31% used its answers without verification. In the structural model (R 2 = 0.60), the strongest drivers were impulsivity (β = 0.40) and anthropomorphic attachment (β = 0.28), with a smaller contribution from AI anxiety (β = 0.15); trust was non-significant. Necessary condition analysis identified social norms as a necessary-in-degree condition. Perceived AI literacy was modestly protective (β = -0.19), stronger in the full model than its zero-order correlation (r = -0.14)-a suppression effect, because more AI-literate students also trusted AI more and sat within more AI-reliant peer groups. Interviews converged with the survey model, most strikingly on anthropomorphic attachment, while qualifying the smaller anxiety pathway.

DISCUSSION: Dependency is driven chiefly by dispositional impulsivity and affective attachment rather than by low competence; interventions-for policymakers, teacher educators, and pre-service teachers-should prioritize self-regulation and awareness of parasocial engagement, treating AI-literacy training as a secondary lever.