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

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.