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A Meta-Review of Generative AI in Education: Synthesizing Findings from Systematic Reviews

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Title: A Meta-Review of Generative AI in Education: Synthesizing Findings from Systematic Reviews
Language: English
Authors: Lijie Zhang (ORCID 0009-0001-8965-5577), Xinyan Deng (ORCID 0000-0002-9116-3870), Rustam Shadiev (ORCID 0000-0001-5571-1158)
Source: Journal of Educational Computing Research. 2026 64(4):1068-1092.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 25
Publication Date: 2026
Document Type: Journal Articles
Information Analyses
Reports - Research
Descriptors: Literature Reviews, Meta Analysis, Educational Research, Artificial Intelligence, Technology Uses in Education, Research Methodology, Research Design, Trend Analysis, Review (Reexamination)
DOI: 10.1177/07356331261419689
ISSN: 0735-6331
1541-4140
Abstract: Generative Artificial Intelligence (GenAI), exemplified by models such as DeepSeek and ChatGPT, is rapidly reshaping education by fostering new pedagogical approaches, including personalized learning, adaptive feedback, and multi-modal instruction. This pedagogical transformation has led to a growing number of review studies examining the applications of GenAI applications across diverse educational contexts. Existing reviews tend to concentrate on various dimensions, such as educational levels, subject domains, or particular GenAI tools and their applications to support teaching and learning. However, to the best of our knowledge, no meta-review has yet been conducted to systematically examine and consolidate the findings of existing review studies on GenAI in education. To address this gap, the present study conducts a systematic meta-review of 35 published reviews, guided by PRISMA protocol. The analysis is structured around three key dimensions: methodological characteristics, thematic focus, and existing issues. Results revealed both advances and inconsistencies in methodological characteristics, including variation in database selection, search strategy transparency, and quality appraisal. The thematic focus shows diverse applications of GenAI across educational levels and disciplines, yet lacks theoretical grounding and comprehensive evaluation of learning outcomes. Furthermore, although the reviews acknowledge GenAI's potential benefits, few offer concrete strategies to mitigate identified risks such as bias, over-reliance, or ethical concerns. This meta-review provides an integrated overview of the current evidence base and identifies directions for future research to support more rigorous, equitable, and pedagogically sound implementation of GenAI in education.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1502467
Database: ERIC
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  Data: A Meta-Review of Generative AI in Education: Synthesizing Findings from Systematic Reviews
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  Data: <searchLink fieldCode="AR" term="%22Lijie+Zhang%22">Lijie Zhang</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0001-8965-5577">0009-0001-8965-5577</externalLink>)<br /><searchLink fieldCode="AR" term="%22Xinyan+Deng%22">Xinyan Deng</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-9116-3870">0000-0002-9116-3870</externalLink>)<br /><searchLink fieldCode="AR" term="%22Rustam+Shadiev%22">Rustam Shadiev</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-5571-1158">0000-0001-5571-1158</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Educational+Computing+Research%22"><i>Journal of Educational Computing Research</i></searchLink>. 2026 64(4):1068-1092.
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  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
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  Data: Generative Artificial Intelligence (GenAI), exemplified by models such as DeepSeek and ChatGPT, is rapidly reshaping education by fostering new pedagogical approaches, including personalized learning, adaptive feedback, and multi-modal instruction. This pedagogical transformation has led to a growing number of review studies examining the applications of GenAI applications across diverse educational contexts. Existing reviews tend to concentrate on various dimensions, such as educational levels, subject domains, or particular GenAI tools and their applications to support teaching and learning. However, to the best of our knowledge, no meta-review has yet been conducted to systematically examine and consolidate the findings of existing review studies on GenAI in education. To address this gap, the present study conducts a systematic meta-review of 35 published reviews, guided by PRISMA protocol. The analysis is structured around three key dimensions: methodological characteristics, thematic focus, and existing issues. Results revealed both advances and inconsistencies in methodological characteristics, including variation in database selection, search strategy transparency, and quality appraisal. The thematic focus shows diverse applications of GenAI across educational levels and disciplines, yet lacks theoretical grounding and comprehensive evaluation of learning outcomes. Furthermore, although the reviews acknowledge GenAI's potential benefits, few offer concrete strategies to mitigate identified risks such as bias, over-reliance, or ethical concerns. This meta-review provides an integrated overview of the current evidence base and identifies directions for future research to support more rigorous, equitable, and pedagogically sound implementation of GenAI in education.
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