ArticleBMC psychology2026
Task complexity and AI dependency among college students: the mediating roles of cognitive load, future anxiety, and task motivation.
Article in BMC psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed.
- Is It Mine or Not Mine? Cognitive Offloading to Generative AI and Concern over the Erosion of Cognitive Ownership.Journal of Intelligence · 2026Article
- The impact of artificial intelligence literacy on learning engagement among university students: the mediating role of psychological capital and the moderating role of professional identity.Frontiers in psychology · 2026Article
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Authors and funding
3 authors.
Funding
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Abstract
purposeThis study aims to clarify the associations between task complexity and Artificial Intelligence (AI) dependency among university students and to examine the roles of cognitive load, future anxiety, and task motivation in these associations.
methodsA structured questionnaire survey was conducted with 442 college students, collecting self-reported data on task complexity, AI dependency, cognitive load, future anxiety, and task motivation. Confirmatory factor analysis was used to assess the adequacy of the proposed framework, and structural equation modeling was then employed to examine the associations among the variables.
resultsTask complexity was significantly and positively associated with AI dependency. Further analyses indicated that students facing more complex tasks tended to report higher cognitive load and stronger future anxiety, which were linked to greater AI dependency. In contrast, students with higher task motivation were less likely to over-rely on AI even when dealing with complex tasks. No significant gender differences were found in AI dependency or related psychological variables.
conclusionThis study advances a task-focused perspective on AI dependency by integrating Cognitive Load Theory and Self-Determination Theory and by specifying cognitive load, future anxiety, and task motivation as correlates linking task complexity with AI dependency. Practically, the results suggest actionable directions for course and assessment design (e.g., calibrating task complexity, scaffolding task decomposition and staged feedback, and supporting motivation and emotion management) to help students balance AI use with autonomous learning.
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