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Cited by 30Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Healthcare and Education

Effects of GenAI Interventions on Student Academic Performance: A Meta-Analysis

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Gu, Jiahe, Yan, Zi·2025·Journal of Educational Computing Research
DOI 10.1177/07356331251349620
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Abstract

Generative artificial intelligence (GenAI) has the potential to change student learning. Despite the popularity of integrating this novel technology into teaching and learning practices, few meta-analyses have synthesised its effect in the education context with K-12 and college students. This review examined the effects of GenAI interventions on student academic performance. A total of 19 studies with 24 effect sizes were included. These studies either compared the GenAI group with control groups ( n = 17, k = 22) or applied a repeated-measure design ( n = 2, k = 2). The results revealed an overall large effect size ( g = 0.683), supporting the arguments that GenAI can positively affect student academic achievement. Students with teacher support in the student-GenAI interaction have significantly larger gains ( g = 1.426) than those without teacher support ( g = 0.077). No other significant moderators were identified. We concluded by discussing the implications for policy and practice and provided suggestions for future research.

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  • The distinction between assistive and substitutive use helps reconcile evidence showing that GenAI can improve the performance of academic students while also raising concerns about independent thinking, source evaluation, and long-term skill development.

    “This distinction helps reconcile evidence showing that GenAI can improve the performance of the academic students while also raising concerns about independent thinking, source evaluation, and longterm skill development (Deng et al., 2025; Gu & Yan, 2025; Jin & Sercu, 2025).”

    From From Assistance to Substitution: Generative AI and Undergraduate Research Skills (2026) · cites this paper as Gu & Yan, 2025

    Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • Gu and Yan's (2025) large pooled effect is accompanied by very high heterogeneity.

    “Gu and Yan's (2025) large pooled effect is accompanied by very high heterogeneity”

    From From Assistance to Substitution: Generative AI and Undergraduate Research Skills (2026) · cites this paper as Gu and Yan's (2025)

  • Gu and Yan (2025) reported a large overall effect of GenAI interventions on academic performance (Hedges' g = 0.683) across 19 studies and 24 effect sizes.

    “Gu and Yan (2025) reported a large overall effect of GenAI interventions on academic performance (Hedges' g = 0.683) across 19 studies and 24 effect sizes.”

    From From Assistance to Substitution: Generative AI and Undergraduate Research Skills (2026) · cites this paper as Gu and Yan, 2025

  • Teacher support was a significant moderator in Gu and Yan's meta-analysis.

    “However, heterogeneity was very high (I² = 94.4%), the prediction interval was wide, and teacher support was a significant moderator.”

    From From Assistance to Substitution: Generative AI and Undergraduate Research Skills (2026) · cites this paper as Gu and Yan, 2025

  • Instructor direction is repeatedly associated with stronger outcomes.

    “Instructor Direction | Repeatedly associated with stronger outcomes; teacher support was significant in Gu & Yan (2025).”

    From From Assistance to Substitution: Generative AI and Undergraduate Research Skills (2026) · cites this paper as Gu & Yan (2025)

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Cited by

Sorted by citations

Authors
Gu, J., & Yan, Z.
Affiliations
Education University of Hong Kong (HK)
Corresponding author
Yan, Zi
ORCID
Venue
Journal of Educational Computing Research
Publisher
SAGE Publications
Published
2025-06-17
Volume
63
Issue
6
Pages
1460-1492
ISSN
  • 0735-6331 (ISSN-L)
  • 1541-4140
DOI
10.1177/07356331251349620
OpenAlex
W4411372787
Cited by
30
References
46
Citations by year
2026: 29 · 2025: 1
FWCI
4.61
Type
journal article
Language
en
Open access
Licence
Locations
Journal of Educational Computing Research (publishedVersion, cc-by-nc)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
generative AI, academic performance, K-12 students, university students, teacher support, educational technology intervention

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