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<p>Generative artificial intelligence (GenAI) is rapidly reshaping higher education, yet evidence remains limited on how students’ AI dependency relates to cognitive load and learning-related outcomes. Drawing on Technology Dependency Theory and Cognitive Load Theory, this study examines the curvilinear associations between AI dependency, intrinsic, extraneous, and germane cognitive load, and two outcomes: higher-order thinking skills (self-reported) and academic enjoyment. An online survey of 951 undergraduate students from eight universities in Sichuan, China, instructed participants to respond with reference to typical coursework in which they use the Chinese GenAI tool DeepSeek for study-related activities, rather than a one-off experimental task. Confirmatory factor analysis and structural equation modeling assessed measurement quality and estimated direct associations, while polynomial regression and the Two-Lines procedure tested nonlinearity.Results show a dependency curve. Extraneous load is lowest at moderate AI dependency. Germane load, higher-order thinking skills, and academic enjoyment also peak at moderate AI dependency. Intrinsic load decreases as more processing is handled by GenAI. The study shifts attention from usage amount to AI dependency. It shows outcomes are clearer when considering extraneous and germane load instead of assuming linear effects. The findings support structure-first guidance that reduces coordination and checking costs while maintaining generative engagement.</p>

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Cognitive load pathways linking generative AI dependency to university students' higher-order thinking skills and academic enjoyment

  • 期刊论文

  • 发表日期：

    2026年5月12日

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  • Hang Zhang,
  • Rujia Jiang

摘要

Generative artificial intelligence (GenAI) is rapidly reshaping higher education, yet evidence remains limited on how students’ AI dependency relates to cognitive load and learning-related outcomes. Drawing on Technology Dependency Theory and Cognitive Load Theory, this study examines the curvilinear associations between AI dependency, intrinsic, extraneous, and germane cognitive load, and two outcomes: higher-order thinking skills (self-reported) and academic enjoyment. An online survey of 951 undergraduate students from eight universities in Sichuan, China, instructed participants to respond with reference to typical coursework in which they use the Chinese GenAI tool DeepSeek for study-related activities, rather than a one-off experimental task. Confirmatory factor analysis and structural equation modeling assessed measurement quality and estimated direct associations, while polynomial regression and the Two-Lines procedure tested nonlinearity.Results show a dependency curve. Extraneous load is lowest at moderate AI dependency. Germane load, higher-order thinking skills, and academic enjoyment also peak at moderate AI dependency. Intrinsic load decreases as more processing is handled by GenAI. The study shifts attention from usage amount to AI dependency. It shows outcomes are clearer when considering extraneous and germane load instead of assuming linear effects. The findings support structure-first guidance that reduces coordination and checking costs while maintaining generative engagement.