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TRUVACE RECORD VERSION
record: TRV-2026-2790
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:24:25.666085Z
status: published
lens: g_space
sector: education
headline: The potential of artificial intelligence to enhance self-regulated learning: A three-level meta-analysis
dek: As artificial intelligence (AI) technologies rapidly advance in education, their potential to support learners’ self-regulated learning (SRL) has gained increasing attention. This meta-analysis synthesized 95 effect sizes from 28 empirical studies to evaluate the overall impact of AI interventions on SRL and examine moderating variables. The results showed that AI interventions significantly improved learners’ SRL. Further analysis indicated that intervention duration and subject domain moderated effectiveness.…
gain_title: A meta-analysis pooling 95 effect sizes from 28 empirical studies found that AI interventions significantly improved learners' self-regulated learning.
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis pooling 95 effect sizes from 28 empirical studies found that AI interventions significantly improved learners' self-regulated learning.
gain_evidence: The results showed that AI interventions significantly improved learners’ SRL.
problem_reading: (none)
problem_evidence: (none)
quick_read: A three-level meta-analysis in the Journal of Research on Technology in Education pooled 95 effect sizes from 28 empirical studies. It found that AI interventions significantly improved learners' self-regulated learning, meaning their ability to manage their own study strategies.

Intervention duration and subject domain moderated how well the AI interventions worked, and a multiple-moderator model within the three-level framework confirmed that both variables significantly affect outcomes. The authors say the findings offer guidance for educators and policymakers on integrating AI into practice.
limitation: 
tag: Evidence-backed gain
key_points: The meta-analysis synthesized 95 effect sizes drawn from 28 empirical studies. | It used a three-level meta-analytic framework to handle multiple effect sizes per study. | AI interventions significantly improved learners' self-regulated learning overall. | Intervention duration moderated how effective the AI interventions were. | Subject domain also moderated effectiveness. | A multiple-moderator model showed both duration and subject domain significantly affect outcomes.
rundown: The study evaluated the overall impact of AI interventions on self-regulated learning (SRL) by combining 95 effect sizes from 28 empirical studies. A three-level framework accounts for several effect sizes coming from the same study.

Moderator analyses found that how long an intervention ran and which subject it was applied to shaped how much it helped. A multiple-moderator model showed both variables significantly affect outcomes.

The authors conclude the findings support integrating AI technologies into educational practice to strengthen learners' SRL strategies, and say they offer guidance for educators and policymakers.
sources:
- peer_reviewed | Journal of Research on Technology in Education | https://doi.org/10.1080/15391523.2026.2643171 | 2026-05-01
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