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The Impact of Artificial Intelligence-Supported Instruction on Student Learning in STEM: A Systematic Review and Meta-Analysis

Truvace record
TRV-2026-2782raw data (JSON)
Version
v1Published
First published
Evidence type
Systematic review or meta-analysis
Sources
1 sourcePeer-reviewed
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3b27ca4db9ba…check it

The rapid integration of artificial intelligence (AI) technologies into educational contexts has introduced innovative instructional approaches, particularly within Science, Technology, Engineering, and Mathematics (STEM) education. Although an increasing number of empirical studies have examined AI-supported instruction, existing findings remain heterogeneous, making it difficult to draw firm conclusions about its overall effectiveness. This study aims to systematically synthesize experimental and quasi-experim…

The gain

A meta-analysis of 35 experimental and quasi-experimental studies found students taught with AI-supported instruction in STEM scored higher than peers taught traditionally (Hedges' g = 0.67, 95% CI 0.49 to 0.85), with the largest gain among high schoolers (g = 1.099).

What this doesn’t fix

Studies varied widely in their results (I² about 83%), much of the variance is unexplained, and most interventions were short to medium term, so long-term effects on learning are unknown.

Sources

1 on file
  1. Journal of Intelligencedoi:10.3390/jintelligence14060109
    Peer-reviewed2026-06-15

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.

  • Student learning outcomes in STEM education with AI-supported instruction compared with control instruction

    Hedges' g 0.67 (95% CI 0.49 to 0.85)

    35 studies · search to 2025

    The review’s own words
    statistically significant overall positive effect of AI-supported instruction on student learning outcomes in STEM education (g = 0.67, 95% CI [0.49, 0.85], p < 0.001)

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

v1

Truvace Impact Record TRV-2026-2782, v1: “The Impact of Artificial Intelligence-Supported Instruction on Student Learning in STEM: A Systematic Review and Meta-Analysis.” Truvace, 2026-11-12. /record/TRV-2026-2782 (accessed on the day you copy it). sha256 3b27ca4db9ba3e05…

Change history

1 version

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  1. Publishedv13b27ca4db9ba…

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