Learning with intelligent tutoring systems yields a moderate, statistically significant overall advantage in achievement over non-ITS modes of instruction¶
Claim · All claims
Evidence · 1 study · quant-synthesisr3·q4·i2medium
Currency · newest study 2014 · not yet checked against its sources · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q4 i2 Across 107 effect sizes involving 14,321 participants, the random-effects weighted mean effect size favoring ITS was g = .41, p < .001. → Wenting Ma 2014
Evidence¶
Wenting Ma 2014¶
Wenting Ma, Olusola O. Adesope, John C. Nesbit, and Qing Liu. (2014). Intelligent Tutoring Systems and Learning Outcomes: A Meta-Analysis. Journal of Educational Psychology. https://doi.org/10.1037/a0037123
q4 · i2 · quant-synthesis · r3
Meta-analysis of 107 independent effect sizes comparing ITS to non-ITS instruction; effect sizes weighted by inverse variances and computed with Hedges's correction for small-sample bias. Under a random -effects model, the overall weighted mean effect size was also statistically significant and m oderate (g = .41, p < .001); the fixed-effect result was g = .36.
"After a search of major bibliographic databases, 107 effect siz es involving 14,321 participants were extracted and analyzed."
Discussion¶
Related Claims¶
- ITS significantly outperform large-group human instruction, individual computer-based instruction, and textbooks or workbooks, but not small-group or individual human tutoring — a narrower finding that bears on this claim
- Intelligent tutoring systems improve achievement over non-individualised instruction — possibly the same claim (merge candidate)
- Bayesian network modeling ITS showed a higher weighted mean effect size than model tracing and constraint-based modeling, but only under a fixed-effect model — a narrower finding that bears on this claim
- AI-enabled personalized STEM education yields a statistically significant overall effect of g = 0.455, which its authors class as small, compared with traditional non-AI instruction in K-12 schools — related
- Intelligent tutoring systems show moderate learning benefits but are less effective than human tutoring — possibly the same claim (merge candidate)
- Meta-analytic evidence indicates intelligent tutoring systems yield positive learning outcomes compared with non-ITS conditions — possibly the same claim (merge candidate)
- AI-supported instruction has a moderate positive effect on student achievement in STEM education (g = 0.670 under a random effects model) — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
its-meta-analysis-overall-effect-0-41 |
title |
Learning with intelligent tutoring systems yields a moderate, statistically significant overall advantage in achievement over non-ITS modes of instruction |
description |
Learning with intelligent tutoring systems yields a moderate, statistically significant overall advantage in achievement over non-ITS modes of instruction |
status |
review |
evidence_strength |
strong |
newest_evidence |
2014 |
generated |
by process:wiki-ingest on 2026-10-13 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — wenting-ma-2014 |
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