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TRUVACE RECORD VERSION
record: TRV-2026-2709
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:12:19.640321Z
status: published
lens: g_space
sector: policy
headline: The Effects of Generative Artificial Intelligence on Self-Regulated Learning: A Meta-Analysis
dek: This meta-analysis systematically examined the effects of generative artificial intelligence (GenAI) on students’ self-regulated learning (SRL), including its overall effectiveness and its impact on the cognitive, metacognitive, motivational, and behavioral dimensions of SRL. Following the PRISMA 2020 guidelines, this study conducted a random-effects meta-analysis to synthesize evidence from 17 studies. The results revealed a statistically significant positive average effect of GenAI on SRL (Hedges’ g = 0.74). N…
gain_title: A meta-analysis of 17 studies found generative AI had a statistically significant positive average effect on students' self-regulated learning (Hedges' g = 0.74).
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of 17 studies found generative AI had a statistically significant positive average effect on students' self-regulated learning (Hedges' g = 0.74).
gain_evidence: The results revealed a statistically significant positive average effect of GenAI on SRL (Hedges’ g = 0.74).
problem_reading: (none)
problem_evidence: (none)
quick_read: A meta-analysis following PRISMA 2020 guidelines pooled evidence from 17 studies on how generative AI affects students' self-regulated learning. A random-effects model gave an average effect of Hedges' g = 0.74, a statistically significant positive result.

Effects were positive across the cognitive, metacognitive, motivational and behavioral dimensions of self-regulated learning. A multilevel analysis with CR2-robust inference found no significant difference between these dimensions (p = 0.564), so no single dimension gained clearly more than the others.

The size of the effect varied considerably between studies, so results differ across educational contexts. The authors conclude that generative AI works best as a scaffold that supports learners' own self-regulation rather than replacing it.
limitation: Substantial between-study heterogeneity suggested considerable variation in the size of the effect across educational contexts, and effect sizes differed by experimental design and intervention duration.
tag: Evidence-backed gain
key_points: The analysis pooled 17 studies using a random-effects model under PRISMA 2020 guidelines. | The average effect on self-regulated learning was Hedges' g = 0.74, statistically significant and positive. | Substantial between-study heterogeneity pointed to wide variation in effect size across educational settings. | Positive average effects appeared in cognitive, metacognitive, motivational and behavioral dimensions, with no significant difference among them (p = 0.564). | Effect sizes differed significantly by experimental design and intervention duration. | Subject domain, type of generative AI intervention, specific tool and personalization showed no significant differences in effect size.
rundown: The study synthesized 17 studies to assess both the overall effectiveness of generative AI for self-regulated learning and its effects on four dimensions: cognitive, metacognitive, motivational and behavioral. It used a random-effects meta-analysis, plus a multilevel random-effects model with CR2-robust inference to compare dimensions.

Moderator analyses found that experimental design and intervention duration were linked to significant differences in effect size. Subject domain, intervention type, tool used and personalization did not produce significant differences.

The authors say the findings suggest generative AI can contribute to developing self-regulated learning, and stress using it as a scaffold that supports rather than replaces learners' active self-regulatory processes.
sources:
- peer_reviewed | Information | https://doi.org/10.3390/info17101028 | 2026-10-15
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