Generative AI in Education: A Meta-Analysis of its Impact on Student Achievement

• In Event: AERA Poster Session Wednesday 11:45am
  In Poster Session: ATL Poster Session 1: AI Integration and Student Outcomes in K–12 Settings

Abstract

This meta-analysis examines the impact of generative artificial intelligence (GenAI) tools, such as ChatGPT, on students’ academic achievement. Drawing on 53 experimental and quasi-experimental studies across educational levels and domains, we synthesized evidence from interventions using GenAI to support learning. Eligible studies reported performance outcomes (e.g., test scores, grades, GPA) and met rigorous inclusion criteria. Overall, GenAI-based instruction showed a positive effect (Hedges' g = 1.186) on academic achievement, with variation across educational level, sample size, learning domain, and the instructional role assigned to GenAI. These findings highlight GenAI’s potential as a learning support tool and offer guidance for effective implementation in educational settings.

Authors

• Xiuxiu Tang, University of Notre Dame

• Liu Dong, Purdue University

• Xiyu Wang, Purdue University

• Jingxian Cecilia Zhang, Mount St. Mary's University