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Exploring the effect of GenAI on learning outcomes in higher education: a three-level meta-analysis
Changxin Fan1,2, Lele Ke3, Zexiong Chen1
1Institute of Education, Xiamen University, Xiamen, China.
Frontiers in Psychology
|June 1, 2026
Summary
Generative artificial intelligence (GenAI) positively impacts student learning, especially when used in collaborative and blended teaching methods. This meta-analysis offers insights for educational practices and instructional design.
Area of Science:
- Education
- Artificial Intelligence
- Learning Sciences
Background:
- Generative artificial intelligence (GenAI) integration in higher education requires understanding its learning impact.
- Existing research on GenAI's educational effects needs synthesis.
Purpose of the Study:
- To meta-analyze the impact of GenAI on diverse student learning outcomes.
- To identify moderating factors, particularly teaching methods, influencing GenAI effectiveness.
Main Methods:
- A three-level meta-analysis of 36 empirical studies involving 7,229 participants.
- Categorization of learning outcomes using the Learning Outcomes Thematic Group (LOTG) framework.
- Examination of seven study-level moderators.
Main Results:
- GenAI shows a significant medium overall effect on learning outcomes (g = 0.499).
- Stronger effects observed for cognitive, creative, and higher-order learning outcomes.
- Collaborative and blended learning methods significantly enhance GenAI's positive impact.
Conclusions:
- GenAI is most effective within interactive and collaborative pedagogical approaches.
- Findings inform instructional design, assessment, and professional development for educators.
- Introduces a GenAI-Learning Alignment Perspective for educational integration.
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