From the Self-Learning of Machine to the Self-Regulated Learning of Students: An Affordance Actualization Perspective

AI-enabled learning applications (AILAs) are increasingly used for autonomous learning, yet how they enhance student performance remains unclear. Prior research on AI affordances often assumes affordance perception occurs without examining its technological preconditions, while also overlooking self-regulated learning (SRL) as the key mechanism linking affordance perception to learning outcomes. Drawing on affordance actualization theory, we address these gaps by modeling the full pathway from an AILA’s core technological capability to learning performance. We frame an AILA’s self-learning capability as a precondition for affordance perception and SRL as the affordance actualization mechanism. Based on survey data from 626 student AILA users, our results confirm this model. Enabled by the AILA’s self-learning capability, perceptions of four key affordances (pattern recognition, strategy formulation, feedback generation, and content personalization) significantly enhance SRL and subsequent learning outcomes. This study contributes a comprehensive framework that explains AILA effectiveness, advancing both affordance theory and educational technology research.