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Generative AI technologies and educational outcomes: a comprehensive meta-analysis comparing traditional and AI-driven approaches

Truvace record
TRV-2026-2813raw data (JSON)
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v1Published
First published
Evidence type
Systematic review or meta-analysis
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1 sourcePeer-reviewed
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85211e2c13db…check it

With the exponentially rapid development of information technologies, generative artificial intelligence (GenAI) technologies have been widely applied to various fields, including education. However, few studies have synthesized the pooled effect of GenAI on educational outcomes, which is an important research gap this study aims to address. Through the PRISMA-based meta-analysis and systematic review, this study concludes a central finding: GenAI technologies may generally outperform traditional and non-GenAI e…

The gain

A meta-analysis of 53 studies found generative AI-assisted education produced higher academic achievement (g=0.40), higher-order thinking (g=0.72) and writing skills (g=0.76) than non-AI approaches.

What this doesn’t fix

Effect sizes were significantly heterogeneous across studies (I²>50%), and several included studies were rated medium or low quality, so pooled estimates vary widely and have wide confidence intervals.

Sources

1 on file
  1. Humanities and Social Sciences Communicationsdoi:10.1057/s41599-026-06903-y
    Peer-reviewed2026-03-12

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.

  • Academic achievement with generative AI-assisted education compared with non-generative-AI approaches

    Hedges' g 0.40 (95% CI 0.08 to 0.71)

    53 studies

    The review’s own words
    GenAI-assisted education may cause significantly higher academic achievements (g=0.40, small-to-moderate effect; 95%CI=0.08~0.71), higher-order thinking abilities (g=0.72; 95%CI=0.30~1.13, moderate-to-large effect), learning motivation (g=0.81; 95%CI=0.20~1.41, large effect), and writing skills (g=0.76; 95%CI=0.18~1.35, moderate-to-large effect) than the non-

  • Educational outcomes with game-assisted generative AI compared with non-game-assisted generative AI

    Hedges' g 0.24 (95% CI -0.40 to 0.88)

    53 studies

    The review’s own words
    It is demonstrated that GenAI feedback may exert a significant influence on educational outcomes (g=1.27; 95%CI=0.49~2.05), while game-assisted GenAI may not cause significant differences in educational outcomes (g=0.24; 95%CI=-0.40~0.88).

  • Educational outcomes with generative AI feedback compared with non-generative-AI feedback

    Hedges' g 1.27 (95% CI 0.49 to 2.05)

    53 studies

    The review’s own words
    It is demonstrated that GenAI feedback may exert a significant influence on educational outcomes (g=1.27; 95%CI=0.49~2.05), while game-assisted GenAI may not cause significant differences in educational outcomes (g=0.24; 95%CI=-0.40~0.88).

  • Writing skills with generative AI-assisted education compared with non-generative-AI approaches

    Hedges' g 0.76 (95% CI 0.18 to 1.35)

    53 studies

    The review’s own words
    GenAI-assisted education may cause significantly higher academic achievements (g=0.40, small-to-moderate effect; 95%CI=0.08~0.71), higher-order thinking abilities (g=0.72; 95%CI=0.30~1.13, moderate-to-large effect), learning motivation (g=0.81; 95%CI=0.20~1.41, large effect), and writing skills (g=0.76; 95%CI=0.18~1.35, moderate-to-large effect) than the non-

  • Learning motivation with generative AI-assisted education compared with non-generative-AI approaches

    Hedges' g 0.81 (95% CI 0.20 to 1.41)

    53 studies

    The review’s own words
    GenAI-assisted education may cause significantly higher academic achievements (g=0.40, small-to-moderate effect; 95%CI=0.08~0.71), higher-order thinking abilities (g=0.72; 95%CI=0.30~1.13, moderate-to-large effect), learning motivation (g=0.81; 95%CI=0.20~1.41, large effect), and writing skills (g=0.76; 95%CI=0.18~1.35, moderate-to-large effect) than the non-

  • Educational outcomes with generative AI in Pakistan compared with non-generative-AI approaches

    Hedges' g 0.75 (95% CI 0.58 to 0.92)

    53 studies

    The review’s own words
    As shown in Figure 5, in China (g=0.71; 95%CI=0.5~0.92) and Pakistan (g=0.75; 95%CI=0.58~0.92), GenAI technologies cause significantly higher educational outcomes than the non-GenAI technologies.

  • Educational outcomes with generative AI in China compared with non-generative-AI approaches

    Hedges' g 0.71 (95% CI 0.5 to 0.92)

    53 studies

    The review’s own words
    As shown in Figure 5, in China (g=0.71; 95%CI=0.5~0.92) and Pakistan (g=0.75; 95%CI=0.58~0.92), GenAI technologies cause significantly higher educational outcomes than the non-GenAI technologies.

  • Higher-order thinking abilities with generative AI-assisted education compared with non-generative-AI approaches

    Hedges' g 0.72 (95% CI 0.30 to 1.13)

    53 studies

    The review’s own words
    GenAI-assisted education may cause significantly higher academic achievements (g=0.40, small-to-moderate effect; 95%CI=0.08~0.71), higher-order thinking abilities (g=0.72; 95%CI=0.30~1.13, moderate-to-large effect), learning motivation (g=0.81; 95%CI=0.20~1.41, large effect), and writing skills (g=0.76; 95%CI=0.18~1.35, moderate-to-large effect) than the non-

  • Educational outcomes with generative AI at secondary level compared with non-generative-AI approaches

    Hedges' g .80 (95% CI 0.15 to 1.44)

    53 studies

    The review’s own words
    At both university (g=.70; 95%CI=0.52~0.88) and secondary (g=.80; 95%CI=0.15~1.44) educational levels, GenAI technologies cause significant differences in educational outcomes (Figure 6).

  • Educational outcomes with generative AI at university level compared with non-generative-AI approaches

    Hedges' g .70 (95% CI 0.52 to 0.88)

    53 studies

    The review’s own words
    At both university (g=.70; 95%CI=0.52~0.88) and secondary (g=.80; 95%CI=0.15~1.44) educational levels, GenAI technologies cause significant differences in educational outcomes (Figure 6).

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

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v1

Truvace Impact Record TRV-2026-2813, v1: “Generative AI technologies and educational outcomes: a comprehensive meta-analysis comparing traditional and AI-driven approaches.” Truvace, 2026-11-12. /record/TRV-2026-2813 (accessed on the day you copy it). sha256 85211e2c13dbd6bf…

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