AI-assisted learning tools and student learning outcomes: A cognitive load theory perspective

Abstract

Artificial intelligence (AI) is transforming higher education, yet its impact on students' learning outcomes depends on how effectively these tools are used, perceived, and supported within learning environments. Guided by Cognitive Load Theory (CLT), this study examines how AI-Assisted Learning Tools Usage (AIALTU) and the Perceived Usefulness of AI in Education (PUAIE) influence university students’ Learning Outcomes (LO), with Student Engagement (SE) as a mediator and Digital Readiness (DR) as a moderator. Data were collected from 400 students across top Saudi universities using a validated bilingual survey instrument and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results show that both AIALTU and PUAIE significantly enhance learning outcomes, and SE partially mediates both relationships, highlighting engagement as a key cognitive mechanism through which AI tools contribute to academic success. DR significantly strengthens the effect of perceived usefulness on learning outcomes but does not alter the influence of actual tool usage, suggesting that intuitive AI design reduces dependence on digital skills. These findings extend CLT by demonstrating how technological, cognitive, and learner-readiness factors jointly shape learning effectiveness in AI-supported settings. The study offers practical insights for universities aiming to align AI integration with Vision 2030 goals by prioritizing student engagement, intuitive tool design, and digital readiness development. Overall, the results underscore the importance of combining advanced AI technologies with supportive learning strategies to achieve meaningful improvements in student learning outcomes.

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