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TRUVACE RECORD VERSION
record: TRV-2026-2816
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:28:15.294625Z
status: published
lens: trace
sector: policy
headline: AI support in self‐regulated learning: A decade of technological evolution and meta‐analysis
dek: 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 i…
gain_title: A meta-analysis of 35 studies (2013–2025) found AI-supported self-regulated learning produced a moderate overall effect (g = 0.507), with generative AI strongest (g = 0.938 in the task performance phase).
problem_title: A meta-analysis of 35 studies on AI-supported self-regulated learning suggests AI's ability to scaffold immediate task performance may outpace its ability to build durable, transferable self-regulatory skills.
trace_subject: AI support for self-regulated learning
gain_reading: A meta-analysis of 35 studies (2013–2025) found AI-supported self-regulated learning produced a moderate overall effect (g = 0.507), with generative AI strongest (g = 0.938 in the task performance phase).
gain_evidence: yield a moderate overall effect size ( g = 0.507) | g = 0.938 for performance
problem_reading: A meta-analysis of 35 studies on AI-supported self-regulated learning suggests AI's ability to scaffold immediate task performance may outpace its ability to build durable, transferable self-regulatory skills.
problem_evidence: AI's capacity to scaffold immediate task performance may outpace its current ability to cultivate durable, transferable self‐regulatory competence
quick_read: A meta-analysis of 35 empirical studies published between 2013 and 2025 examined AI applications within Zimmerman's cyclical model of self-regulated learning. It found a moderate overall effect of g = 0.507 for AI-supported interventions.

The effect varied by learning phase: g = 0.574 during task performance, g = 0.401 in forethought and g = 0.464 in self-reflection. Generative AI outperformed other approaches in every phase, though high heterogeneity suggests results depend heavily on instructional design.

The authors describe a performance-competence divide. AI's effect on observable behaviours (g = 0.751) was more than double its effect on self-reported perceptions (g = 0.369), and they say immediate task support may outpace lasting skill building.
limitation: The authors report high heterogeneity, suggesting the effects of generative AI are heavily contingent on specific instructional designs.
tag: Dual reading
key_points: The analysis covered 35 empirical studies from 2013 to 2025 on AI within Zimmerman's cyclical model of self-regulated learning. | Overall effect size was moderate (g = 0.507). | AI worked better during task performance (g = 0.574) than in forethought (g = 0.401); self-reflection was g = 0.464. | Generative AI showed the strongest effects, with g = 0.709 for forethought and g = 0.938 for performance. | Effects were largest in secondary education, natural sciences, fully online settings and interventions lasting 2–10 weeks. | Behavioural-trace measures showed g = 0.751 versus g = 0.369 for self-reports. | Three AI paradigms coexist: rule-based, data-driven adaptive and generative AI.
rundown: The review traced a decade of technological change through three coexisting paradigms: rule-based architectures, data-driven adaptive systems and generative AI ecosystems. It says these show increasingly sophisticated capacity for human-AI collaboration.

Moderator analysis pointed to the best conditions: secondary education, natural science subjects, fully online delivery and interventions of medium length. The authors advise 2–10 weeks to avoid both novelty effects and scaffold dependency.

The authors recommend combining rule-based systems' stable scaffolding with generative AI's adaptive dialogue, and tailoring support by discipline, with social sciences needing more help for open-ended inquiry. They also call for assessments that measure delayed, unsupported performance alongside immediate gains.
sources:
- peer_reviewed | British Journal of Educational Technology | https://doi.org/10.1111/bjet.70058 | 2026-03-12
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