Reconceptualizing Self-directed and Self-regulated Learning for Digital Learning Spaces: An Integrative Theoretical Framework

Abstract

The current situation that requires the redefinition of learner autonomy is accelerating change in digital learning settings in the period between 2022 and 2025 that is produced by generative artificial intelligence (GenAI), immersive technologies, and omnipresent learning analytics. The traditional demarcation between Self-Directed Learning (SDL) that is becoming more macro-level control of what learners which is to accomplish and Self-Regulated Learning (SRL) that is becoming more micro-level control of cognitive and metacognitive processes in the performance of tasks is becoming increasingly blurry in the condition that is facilitated by AI. The given paper presents an integrative theoretical analysis, that is, a synthesis of SDL and SRL into one model, which is called Digital Integrative Self-Directed and Regulated Learning (DI-SDRL). According to the exploration of the literature that has been published since 2022, the authors discover three structural transformations in contemporary learning: (i) the shift of individual regulation to distributed human-AI co-regulation; (ii) the extension of SDL to algorithmic navigation in AI-curated ecosystems; and (iii) the identification of digital well-being as the precondition of a sustainable regulation. DI-SDRL model assumes the digital learning spaces to be dynamic socio-technical agents, which are shaping motivation, volition, and metacognition. The article closes by giving pedagogic, curriculum and policy implications, which give the roadmap to the digital agents of learning being saturated by AI.