British Journal of Educational Technology 2026
AI support in self-regulated learning: A decade of technological evolution and meta-analysis
Corresponding author: Yonghe Wu
First published online: 2026-03-12.Issue publication: 2026-09.
Research summary
A meta-analysis of 35 empirical studies from 2013 to 2025 examines AI support within Zimmerman's cyclical model of self-regulated learning. It reports a moderate overall effect (g = 0.507), with different effects across learning phases and measurement approaches. The synthesis distinguishes immediate task performance from durable, transferable self-regulatory competence.
Study design.
Meta-analysis of 35 empirical studies, with SRL-phase, technology and contextual moderator analyses.
Abstract
This meta‐analysis systematically examines 35 empirical studies (2013–2025) investigating artificial intelligence applications within Zimmerman's cyclical model of self‐regulated learning (SRL). Three principal discoveries emerge: (1) Technological progression has evolved through three co‐existing paradigms: rule‐based architectures, data‐driven adaptive systems and generative AI ecosystems, that demonstrate increasingly sophisticated capabilities for human‐AI collaboration. (2) While AI‐supported SRL interventions yield a moderate overall effect size ( g = 0.507), their impact is uneven; AI is significantly more effective during the task performance phase ( g = 0.574) than in the preparatory forethought phase ( g = 0.401). Notably, generative AI shows markedly superior efficacy across all phases (e.g. g = 0.709 for forethought, g = 0.938 for performance), though high heterogeneity suggests these effects are heavily contingent on specific instructional designs. (3) Moderator analysis identifies optimal contexts in secondary education, natural science disciplines, fully online settings and interventions of medium duration (2–10 weeks), while also revealing that effects are substantially larger when measured by behavioural traces compared to self‐reports. Critically, these findings highlight a persistent performance‐competence divide, suggesting that AI's capacity to scaffold immediate task performance may outpace its current ability to cultivate durable, transferable self‐regulatory competence. The study discusses the implications of this divide and proposes a research agenda focused on designing AI systems that foster genuine learner autonomy.
Research context
How does AI support differ across the forethought, performance and reflection phases of self-regulated learning?
Use this synthesis for evidence on AI-supported SRL, phase-sensitive scaffolding and assessment design. Interpret pooled and subgroup effects in relation to study heterogeneity and instructional context.
self-regulated learning · meta-analysis · AI scaffolding · learning analytics
中文研究介绍
这项元分析综合2013至2025年的35项实证研究，按照Zimmerman自我调节学习循环模型分析AI支持，报告总体效应量g = 0.507，并比较不同学习阶段与测量方式的差异，强调即时任务表现与持久、可迁移自我调节能力的区分。
Read and cite
Xu, J., Luo, Y., Wang, C., Wang, M., & Wu, Y. (2026). AI support in self-regulated learning: A decade of technological evolution and meta-analysis. British Journal of Educational Technology, 57(5), 1191-1220. https://doi.org/10.1111/bjet.70058