Is Higher Academic Workload Associated with AI Dependency? Investigating the Roles of Academic Anxiety,...

With the increasing integration of artificial intelligence (AI) technologies into higher education, concerns have emerged regarding students' growing dependency on AI tools. AI dependency refers to a maladaptive pattern of reliance on AI characterized by addiction-related symptoms. Grounded in the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, this study examines the associations between academic workload and AI dependency among undergraduate students by incorporating academic anxiety (affective), academic procrastination (executive), and satisfaction with AI (cognitive) into a moderated mediation framework.

Data were collected from 690 Chinese undergraduate students using validated self-report questionnaires with established psychometric properties. Structural equation modeling (SEM) with bootstrapping was used to construct latent variables and test the hypothesized model.

Results showed that academic workload was positively associated with AI dependency (total β = 0.35), with a remaining direct association (β = 0.14) and indirect associations through academic anxiety (β = 0.10), academic procrastination (β = 0.06), and their sequential pathway (β = 0.05). Satisfaction with AI moderated the association between academic procrastination and AI dependency (β = 0.13), with stronger effects at higher levels of satisfaction.

These findings extend the application of the I-PACE model by identifying affective and execution-related pathways associated with higher AI dependency. The pathway-specific moderation further suggests that positive appraisal of AI may amplify the link between procrastination and AI dependency. The findings highlight the need for educational strategies that address excessive academic demands, support students in managing anxiety and procrastination, and promote critical, balanced, and self-regulated AI use in higher education.