Self-regulated learning and engagement as serial mediators between AI-driven adaptive learning platform...

The rapid integration of artificial intelligence in educational technology has transformed learning environments, yet the psychological mechanisms through which AI-driven adaptive learning platforms are associated with enhanced educational outcomes remain insufficiently understood. This study investigates the complex mediational pathways linking adaptive learning platform characteristics to educational quality enhancement, examining the sequential relationships between self-regulated learning and learning engagement from a psychological perspective. Drawing on cognitive and motivational theories of learning, we hypothesized that platform features would be associated with educational quality both directly and through the serial mediation of self-regulated learning and engagement. Employing structural equation modeling with data from 625 learners using AI-driven adaptive learning platforms (including Knewton, ALEKS, and Squirrel AI), this research reveals both significant direct relationships between platform characteristics and educational quality and substantial indirect effects through serial mediation. The findings demonstrate that platform features show strong associations with self-regulated learning, which sequentially relates to learning engagement and educational quality. These results advance our understanding of the cognitive and motivational processes through which technological affordances are associated with enhanced learning outcomes. The study provides important insights into the psychological mechanisms underlying effective digital learning, offering evidence-based guidance for designing adaptive learning environments that are related to learner autonomy and engagement. This research contributes to educational psychology by elucidating how AI-driven personalization features correlate with fundamental psychological processes associated with successful learning experiences.