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TRUVACE RECORD VERSION
record: TRV-2026-2850
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:34:44.353994Z
status: published
lens: g_space
sector: education
headline: The Efficacy of Artificial Intelligence-Enabled Adaptive Learning Systems From 2010 to 2022 on Learner Outcomes: A Meta-Analysis
dek: The purpose of this research study was to examine the overall effect of adaptive learning systems deployed using artificial intelligence technology across a range of relevant variables (e.g., duration, student level, etc.). Following a systematic procedure, this meta-analysis examined literature from 18 academic databases and identified N = 45 independent studies utilizing AI-enabled adaptive learning. This meta-analysis examined the overall effect of AI-enabled adaptive learning systems on students’ cognitive l…
gain_title: A meta-analysis of 45 independent studies found AI-enabled adaptive learning systems had a medium to large positive effect (g = 0.70) on students' cognitive learning outcomes compared with non-adaptive teaching.
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of 45 independent studies found AI-enabled adaptive learning systems had a medium to large positive effect (g = 0.70) on students' cognitive learning outcomes compared with non-adaptive teaching.
gain_evidence: found that they have a medium to large positive effect size ( g = 0.70)
problem_reading: (none)
problem_evidence: (none)
quick_read: A meta-analysis pooled 45 independent studies of adaptive learning systems run on artificial intelligence and compared them with non-adaptive learning interventions. It found a medium to large positive effect on students' cognitive learning outcomes, with a pooled effect size of g = 0.70.

The authors searched 18 academic databases for studies covering 2010 to 2022. The size of the effect varied with publication type, study origin, student level, discipline, duration and research design, so the average does not apply equally to every classroom.

Each of the three adaptive sources (cognitive, affective and behavioral) and both adaptive targets (navigation and assessment) significantly moderated the results. The specific type of AI used in the adaptive engine did not.
limitation: The effect size varied significantly with publication type, study origin, student level, discipline, duration and research design, so the average of g = 0.70 may not hold in every setting.
tag: Evidence-backed gain
key_points: The meta-analysis drew on literature from 18 academic databases and included 45 independent studies. | AI-enabled adaptive learning beat non-adaptive interventions on cognitive learning outcomes, with a pooled effect of g = 0.70. | Publication type, study origin, student classification level, discipline, duration and research design significantly moderated the effect. | Cognitive, affective and behavioral adaptive sources were all significant moderators. | Adaptive targets, navigation and assessment, were also significant moderators. | The type of AI used in the adaptive engine did not change the effect.
rundown: The study followed a systematic procedure to find research on adaptive learning systems deployed with AI. It searched 18 academic databases and identified 45 independent studies, covering work published from 2010 to 2022.

The headline result is a medium to large positive effect (g = 0.70) on students' cognitive learning outcomes, measured against non-adaptive learning interventions.

Moderator analyses showed the effect differed by publication type, study origin, student level, discipline, duration and design. The adaptive engine could draw on cognitive, affective or behavioral signals and could target navigation or assessment; all of these were significant moderators, whereas the type of AI used was not.
sources:
- peer_reviewed | Journal of Educational Computing Research | https://doi.org/10.1177/07356331241240459 | 2024-05-15
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