AR/VR tools show the largest effect among AI application types (g = 1.765), while ITS and personalized educational games show small effects¶

Claim · All claims
Evidence · 1 study · quant-synthesis r2 · q4 · i3 large
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q4 i3 AI application type significantly moderated effect sizes (QB = 16.776, p = 0.001); AR/VR had a large effect size (g = 1.765, p < 0.001), exceeding ITS (g = 0.347) and games (g = 0.292). → Li 2025

Evidence¶

Li 2025¶

Li, S., Zeng, C., Liu, H., Jia, J., Liang, M., Cha, Y., Lim, C. P., & Wu, X. (2025). A meta-analysis of AI-enabled personalized STEM education in schools. International Journal of STEM Education. https://doi.org/10.1186/s40594-025-00566-y

q4 · i3 · quant-synthesis · r2

Subgroup analysis of AI application types (AR/VR k = 8, ITS k = 61, games k = 23, others k = 7) in the meta-analysis, with AR/VR at "g = 1.765". The "other technologies" category was not statistically significant (g = 0.893, p = 0.078).

"Among all the studies, AR/VR technology had a large effect size (g = 1.765, p < 0.001). ITS (g = 0.347, p < 0.001) and personalized educational games (g = 0.292, p < 0.001) applied in STEM education for K-12 both had small effect sizes."

Discussion¶

Related Claims¶

• Type of AI moderated effect sizes: VR showed the largest pooled effect (gp = 1.28) and AR the smallest and non-significant in the post-test analysis — related
• Subject significantly moderates effects, with AI-enabled personalized STEM education most effective in comprehensive courses (g = 2.161) — related
• GAI-related and GAI-unrelated AI tools show no significant difference in their moderating effects on K-12 STEM learning outcomes — related
• School level significantly moderates effects: AI-enabled personalized STEM education is more effective in junior and senior high school than in kindergarten — related
• Most VR/AR-supported K-12 STEM studies targeted elementary students and used quantitative methods, with most samples under 100 participants — related
• AI-enabled personalized STEM education yields a statistically significant overall effect of g = 0.455, which its authors class as small, compared with traditional non-AI instruction in K-12 schools — related
• ITS produce significant positive effects at all grade levels and in almost all subject domains evaluated, with exceptions such as chemistry — related
• Personalized learning models combining classroom use with student-centered pedagogies (Model C) exert larger effects than fully automated platforms (Model A) — related