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TRUVACE RECORD VERSION
record: TRV-2026-2823
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:30:24.993521Z
status: published
lens: g_space
sector: policy
headline: Artificial intelligence and learner autonomy: a meta-analysis of self-regulated and self-directed learning
dek: Introduction As artificial intelligence (AI) becomes increasingly embedded in educational environments, understanding its role in shaping learners’ self-regulated learning (SRL) and self-directed learning (SDL) has emerged as a central concern in contemporary learning science. While prior studies suggest that AI-driven systems may support planning, monitoring, and autonomy in learning, empirical evidence remains fragmented across contexts, learner groups, and instructional designs. This study synthesizes existin…
gain_title: A meta-analysis of 32 controlled studies with 3,029 learners found AI-based interventions raised self-regulated learning (g = 1.613) and self-directed learning (g = 1.111) versus control groups, with very high variation between studies.
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of 32 controlled studies with 3,029 learners found AI-based interventions raised self-regulated learning (g = 1.613) and self-directed learning (g = 1.111) versus control groups, with very high variation between studies.
gain_evidence: AI-based interventions demonstrated a large and statistically significant | AI systems also yielded moderate improvements in learning outcomes and achievement
problem_reading: (none)
problem_evidence: (none)
quick_read: A meta-analysis in Frontiers in Education pooled 32 studies that compared AI-supported learners with control groups, covering 92 effect sizes and 3,029 participants. AI-based interventions had a large positive effect on overall self-regulated learning (g = 1.613) and on self-directed learning (g = 1.111), and a smaller effect on learning outcomes and achievement (g = 0.350).

The gains were uneven. AI helped most with planning and goal setting before a task and gave smaller gains in self-reflection afterwards, but effects on behavioural regulation such as time management and effort were inconsistent and not statistically significant. Effects were stronger among older learners, in longer interventions and in language learning with interactive AI systems.

The large headline figures come with very high variation between studies (I2 of 98.85% for overall self-regulated learning). Tests also flagged possible publication bias for the overall result, so the size of the effect is less certain than its positive direction.
limitation: Variation between studies was extremely high for overall self-regulated learning, and tests indicated significant publication bias for that result, so the pooled effect size may be overestimated.
tag: Evidence-backed gain
key_points: The review searched Scopus and Google Scholar on 27 September 2025, found 427 documents and kept 32 studies with a control group; participants were aged 18 to 37, mostly undergraduates and early postgraduates. | Overall self-regulated learning rose by g = 1.613 (p = 0.032; 11 studies; 95% CI 0.14 to 3.09), and self-directed learning rose by g = 1.111 (p = 0.043; 8 studies). | Cognitive and metacognitive regulation improved by g = 0.377 (p = 0.0004; 10 studies) and motivational and affective regulation by g = 0.505 (p = 0.013); behavioural regulation showed g = 0.914 but was not significant (p = 0.284). | By phase, the forethought phase gained g = 0.4739 and self-reflection g = 0.2774; the performance phase (g = 0.78) had a confidence interval of -0.11 to 1.67 and was not significant. | Learning outcomes and achievement improved by g = 0.350 (p = 0.034) across seven studies, a small-to-moderate effect. | Older learners and longer interventions showed larger gains in overall self-regulation; gender made no significant difference. | Conversational and interactive systems such as chatbots produced strong effects on nearly all outcomes, while instructional and adaptive systems showed no significant moderating effect.
rundown: The studies covered language learning, STEM and medical education, using tools ranging from chatbots and a Study Buddy SRL chatbot to LLaMA-based AI textbooks, analytics-enhanced feedback systems and rule-based scaffolding engines. Effect sizes were Hedges' g, pooled with random-effects models and robust variance estimation where studies reported several effects.

Heterogeneity differed sharply by outcome. Cognitive and metacognitive regulation had low variation (I2 = 30.98%) and forethought effects moderate variation (about 44%), while behavioural regulation (98.32%) and the performance phase (about 97%) varied widely across studies. The author reads this as AI supporting in-the-head planning and monitoring better than habits that depend on a learner's environment.

Leave-one-out checks kept the overall self-regulation effect between g = 0.914 and 1.746, so no single study drove it. Egger's and Begg's tests nonetheless indicated significant publication bias for overall self-regulated learning, and Begg's test also flagged self-directed learning (p = 0.0098).

The author cautions that opaque or overly prescriptive AI recommendations could make learners follow generated paths rather than direct their own study, and that some learners may offload effort instead of building it.
sources:
- peer_reviewed | Frontiers in Education | https://doi.org/10.3389/feduc.2025.1738751 | 2025-12-29
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