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A Meta-Analysis of the Impact of Generative Artificial Intelligence on Learning Outcomes

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Title: A Meta-Analysis of the Impact of Generative Artificial Intelligence on Learning Outcomes
Language: English
Authors: Nan Ma (ORCID 0009-0004-1808-270X), Zhiyong Zhong
Source: Journal of Computer Assisted Learning. 2025 41(5).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 21
Publication Date: 2025
Document Type: Journal Articles
Information Analyses
Reports - Research
Descriptors: Meta Analysis, Artificial Intelligence, Technology Uses in Education, Outcomes of Education, Technology Integration, Effect Size, Mathematics Education, Science Education, Humanities Instruction, Computer Science Education, Medical Education, Nursing Education
DOI: 10.1111/jcal.70117
ISSN: 0266-4909
1365-2729
Abstract: Background: With the rapid advancement of technology, the integration of Generative Artificial Intelligence (GAI) in education has gained considerable attention. Many studies have examined GAI's impact on learning outcomes, yet their conclusions are inconsistent, highlighting the need for a comprehensive review to clarify its overall effects and identify influential factors. Objectives: This study aims to conduct a meta-analysis of the effects of GAI on student learning outcomes across cognitive, competency and affective dimensions. Additionally, it seeks to explore how various moderating factors, including subject discipline, instructional duration, knowledge type, prior knowledge and tool type, influence GAI's effectiveness. Methods: A meta-analysis was performed on 34 experimental and quasi-experimental studies published internationally. Effect sizes were calculated for overall learning outcomes and categorised by dimension. Further analysis was conducted to assess the influence of moderating variables on the impact of GAI. Results: The meta-analysis indicates that Generative Artificial Intelligence has a significant positive impact on overall learning outcomes, with a combined effect size of 0.68 (p < 0.001). The impact is particularly pronounced in the cognitive dimension (g = 0.795) and the competency dimension (g = 0.711), while its effect on the affective dimension (g = 0.507) is moderate but still significant. The analysis of moderating variables reveals that the effectiveness of GAI is influenced by discipline type but is not significantly affected by instructional period, knowledge type, prior knowledge level, or tool type. Specifically, GAI exhibits the highest positive effects in mathematics, science and humanities, whereas its impact is relatively lower yet still significant in computer science and medical/nursing education. Additionally, GAI's effectiveness does not significantly differ across various instructional periods, different knowledge types, learners with varying prior knowledge levels, or different AI tool versions. Conclusions: To optimise GAI's use in education, the study suggests aligning GAI with specific subject needs, adapting tools for different student levels, integrating GAI with traditional teaching and establishing monitoring mechanisms. These strategies aim to maximise GAI's positive impact on learning efficiency and quality across educational settings.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1484315
Database: ERIC
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  Data: A Meta-Analysis of the Impact of Generative Artificial Intelligence on Learning Outcomes
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  Data: &lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Nan+Ma%22&quot;&gt;Nan Ma&lt;/searchLink&gt; (ORCID &lt;externalLink term=&quot;https://orcid.org/0009-0004-1808-270X&quot;&gt;0009-0004-1808-270X&lt;/externalLink&gt;)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Zhiyong+Zhong%22&quot;&gt;Zhiyong Zhong&lt;/searchLink&gt;
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  Data: Wiley. Available from: John Wiley &amp; Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: 10.1111/jcal.70117
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  Data: Background: With the rapid advancement of technology, the integration of Generative Artificial Intelligence (GAI) in education has gained considerable attention. Many studies have examined GAI&#39;s impact on learning outcomes, yet their conclusions are inconsistent, highlighting the need for a comprehensive review to clarify its overall effects and identify influential factors. Objectives: This study aims to conduct a meta-analysis of the effects of GAI on student learning outcomes across cognitive, competency and affective dimensions. Additionally, it seeks to explore how various moderating factors, including subject discipline, instructional duration, knowledge type, prior knowledge and tool type, influence GAI&#39;s effectiveness. Methods: A meta-analysis was performed on 34 experimental and quasi-experimental studies published internationally. Effect sizes were calculated for overall learning outcomes and categorised by dimension. Further analysis was conducted to assess the influence of moderating variables on the impact of GAI. Results: The meta-analysis indicates that Generative Artificial Intelligence has a significant positive impact on overall learning outcomes, with a combined effect size of 0.68 (p &lt; 0.001). The impact is particularly pronounced in the cognitive dimension (g = 0.795) and the competency dimension (g = 0.711), while its effect on the affective dimension (g = 0.507) is moderate but still significant. The analysis of moderating variables reveals that the effectiveness of GAI is influenced by discipline type but is not significantly affected by instructional period, knowledge type, prior knowledge level, or tool type. Specifically, GAI exhibits the highest positive effects in mathematics, science and humanities, whereas its impact is relatively lower yet still significant in computer science and medical/nursing education. Additionally, GAI&#39;s effectiveness does not significantly differ across various instructional periods, different knowledge types, learners with varying prior knowledge levels, or different AI tool versions. Conclusions: To optimise GAI&#39;s use in education, the study suggests aligning GAI with specific subject needs, adapting tools for different student levels, integrating GAI with traditional teaching and establishing monitoring mechanisms. These strategies aim to maximise GAI&#39;s positive impact on learning efficiency and quality across educational settings.
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