Supporting Self-Regulated Learning in the Age of Artificial Intelligence: A Comparative Systematic Review of Earlier AI and Generative AI

Abstract

Generative AI (GenAI) has rapidly emerged as a promising tool for supporting self-regulated learning (SRL). However, little is known about how its mechanisms compare with, or extend beyond, earlier artificial intelligence (AI) approaches. Using Winne and Hadwin’s COPES (Conditions, Operations, Products, Evaluations and Standards) architecture as an organizing framework, this comparative systematic review synthesized 70 articles from 2015 to 2025 to examine how AI and GenAI were technologically and pedagogically implemented to scaffold SRL. Technically, SRL systems predominantly employed knowledge-based systems, machine learning, natural language processing, and, more recently, customized large language models, with limited integration of external knowledge bases. Pedagogically, interventions in the reviewed studies concentrated on Operations and Evaluations during task enactment, with less attention to task definition, goal setting, and adaptation. In terms of operational distinctions between AI and GenAI, AI tended to instantiate analytics-driven scaffolding (data-based adaptivity, progress monitoring, and standards setting). In contrast, GenAI more often enabled dialogic, context-adaptive cognitive scaffolding and content generation. Building on these findings, this review proposes a framework for comprehensive SRL scaffolding.