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<p>This study examines the associations among institutional digital capacity, students’ AI literacy, academic motivation, and learning outcomes (cognitive learning and creative self-efficacy). Using survey data from 446 Chinese undergraduates, we tested a sequential mediation model integrating the Capability-Opportunity-Motivation-Behavior (COM-B) framework and Self-Determination Theory (SDT) via Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicate a full sequential pathway: institutional digital capacity (IDC) serves as a crucial environmental antecedent that positively relates to students’ AI literacy, which in turn is associated with their academic motivation, ultimately contributing to both cognitive outcomes and creative self-efficacy. Our findings highlight that effectively nurturing digital literacy and innovation requires a synergy of robust institutional resources, individual technical capability, and psychological engagement. By explicitly mapping contextual and psychological elements, this research provides practical insights for educators and policymakers aiming to design inclusive and motivating AI-enhanced higher education ecosystems.</p>

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Unpacking the role of AI literacy in cognitive outcomes and creative self-efficacy: A sequential mediation model of institutional digital capacity and academic motivation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月20日

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  • Chengqiu Dai,
  • Yueling Guo

摘要

This study examines the associations among institutional digital capacity, students’ AI literacy, academic motivation, and learning outcomes (cognitive learning and creative self-efficacy). Using survey data from 446 Chinese undergraduates, we tested a sequential mediation model integrating the Capability-Opportunity-Motivation-Behavior (COM-B) framework and Self-Determination Theory (SDT) via Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicate a full sequential pathway: institutional digital capacity (IDC) serves as a crucial environmental antecedent that positively relates to students’ AI literacy, which in turn is associated with their academic motivation, ultimately contributing to both cognitive outcomes and creative self-efficacy. Our findings highlight that effectively nurturing digital literacy and innovation requires a synergy of robust institutional resources, individual technical capability, and psychological engagement. By explicitly mapping contextual and psychological elements, this research provides practical insights for educators and policymakers aiming to design inclusive and motivating AI-enhanced higher education ecosystems.