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The contingent impact of artificial intelligence on teaching effectiveness: a meta-analytic review of boundary conditions and moderating factors
- Truvace record
- TRV-2026-2806raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Systematic review or meta-analysis
- Sources
- 1 sourcePeer-reviewed
- sha256
- 8332ad689f4d…check it
The digital transformation of education is reshaping pedagogical landscapes and driving the shift toward smart teaching. This meta-analysis examines the impact of Artificial Intelligence (AI)-enabled teaching on intelligent teaching assessment, synthesizing evidence from 35 pre-test-post-test designs and 37 post-test studies. Results show that AI-driven interventions yield significant positive effects, with the post-test effect size (g_p = 0.586) exceeding the pre-test-post-test gain (g_delta = 0.136), primarily…
The gain
A meta-analysis of 37 post-test studies found AI-assisted teaching scored 0.586 standard deviations higher than comparison teaching, while 35 pre-test/post-test studies that controlled for baseline found a smaller 0.136 gain.
What this doesn’t fix
The authors say the dataset is too small for reliable subgroup analysis, with many subgroups holding only one or two studies, and their funnel-plot section warns the pooled effect may overestimate AI's real impact.
Sources
1 on file- Frontiers in Psychologydoi:10.3389/fpsyg.2026.1744690
What the review found when it combined the studies
These are the review’s own pooled results, copied from its abstract. Truvace did not recalculate them.
Post-test learning outcomes with AI-assisted teaching versus traditional teaching
SMD 0.586 (95% CI 0.521 to 0.650)
37 studies
The review’s own words
The overall effect size g_p was 0.586 (95% confidence interval [0.521, 0.650], p < 0.001), showing an upward trend.
Learning outcomes gain from pre-test to post-test with AI-assisted teaching versus traditional teaching
SMD 0.136 (95% CI 0.071 to 0.200)
35 studies
The review’s own words
Figure 2 only included k equal to 35 studies that adopted a strict pretest-posttest design, and the cumulative analysis yielded a total effect size of 0.136 (95% confidence interval [0.071, 0.200], p < 0.001), which had a certain effect but was relatively small (i.e., the average increase was limited).
Code checks every number above against the review’s text before it is shown. The short labels are an AI model’s reading of that text, so the review’s own sentence sits under each one. A pooled figure is only as strong as the studies behind it. How Truvace uses pooled results
Cite this record
v1Truvace Impact Record TRV-2026-2806, v1: “The contingent impact of artificial intelligence on teaching effectiveness: a meta-analytic review of boundary conditions and moderating factors.” Truvace, 2026-11-12. /record/TRV-2026-2806 (accessed on the day you copy it). sha256 8332ad689f4d3352…
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