Subject significantly moderates effects, with AI-enabled personalized STEM education most effective in comprehensive courses (g = 2.161)¶

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 Effect sizes differed significantly across subjects (QB = 10.908, p = 0.028), with comprehensive courses showing the largest effect (g = 2.161, p = 0.004). → 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 across five subject categories (biology, mathematics, IT-related, science, comprehensive course) in the meta-analysis. Comprehensive courses showed "effect sizes of 2.161"; small significant effects appeared in mathematics, IT-related subjects, and science, while biology was not significant.

"AI-enabled personalized STEM education is most effective for com - prehensive courses, with effect sizes of 2.161 (p = 0.004)."

Discussion¶

Related Claims¶

• School level significantly moderates effects: AI-enabled personalized STEM education is more effective in junior and senior high school than in kindergarten — related
• AR/VR tools show the largest effect among AI application types (g = 1.765), while ITS and personalized educational games show small effects — related
• Personalized learning models combining classroom use with student-centered pedagogies (Model C) exert larger effects than fully automated platforms (Model A) — 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 — a broader claim this one bears on