Effects of GenAI Interventions on Student Academic Performance: A Meta-Analysis
Jiahe Gu, Zi Yan
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.