A meta-analysis of AI-enabled personalized STEM education in schools

AI-enabled personalization in STEM education is a transformative paradigm in pedagogical approaches, offering students a learning experience that is dynamically responsive to their nuanced requisites. Owing to the rapid development of AI-enabled personalized STEM education, there is a need for more systematic meta-analyses synthesizing its implementation in K-12 schools and examining its effects on improving educational outcomes. To examine the effect of AI technologies on personalized K-12 STEM education under different conditions, a meta-analysis was conducted by synthesizing 99 effect sizes extracted from 32 randomized controlled trial studies. The screening process followed the PRISMA flow diagram. In the coding process, the PICO tool was adapted as a coding sheet to extract pertinent details of each study. The analysis reveals a medium effect of AI technologies in personalized K-12 STEM education, indicating potential advantages over traditional non-AI methods. Six moderators were examined, with four (i.e., school levels, AI application types, personalized learning model types, and subjects) found to be significant in improving educational outcomes. Specifically, AI-enabled STEM education is more effective in junior and senior high school. Secondly, AR or VR tools show the most significant impact on students’ learning outcomes. Furthermore, learning models combined with classroom use exert more significant effects. Moreover, AI technology demonstrates the most important effects in comprehensive courses. Finally, observed items and GAI usage show no significant moderating effects. This paper advocates for an evidence-based and pedagogically aligned integration of AI-enabled personalized STEM education.