Abstract
The integration of artificial intelligence (AI) technologies into science, technology, engineering, and mathematics (STEM) education has attracted growing scholarly interest due to its potential to improve instructional quality and academic outcomes. This study quantitatively evaluated the effect of AI integration in STEM education on learners’ academic achievement using a meta-analytic approach. Following PRISMA guidelines, empirical studies published between 2015 and 2026 were retrieved from Web of Science, IEEE Xplore, and Google Scholar. Studies were included if they used an experimental or quasi-experimental design, included a control group, and provided sufficient statistical information for effect size calculation. A total of 15 studies were included. Effect sizes were calculated using Hedges’ g and synthesized using a random-effects model. The results showed a statistically significant positive effect of AI-based STEM interventions on academic achievement (g = 1.57, 95% CI [0.985, 2.156], Z = 5.256, p < 0.001). Moderate-to-substantial heterogeneity was observed (Q = 72.467, p < 0.001; I² = 73%), indicating variation across educational contexts and implementation conditions. Overall, the findings suggest that pedagogically informed AI integration can improve academic achievement in STEM education and support evidence-based educational practice and policy.