An Empirical Framework for Assessing AI in Early Childhood Education: A Global vs. Local Perspective

• In Event: AERA Poster Session Saturday 9:45 am
  In Poster Session: AI and Technology Integration in K-12 Education

Abstract

This study evaluates Generative AI's (GenAI) effectiveness in the sensitive context of Chinese Early Childhood Education (ECE). We conducted a comparative analysis of four leading AI models—two global (ChatGPT and Gemini) and two prominent local Chinese (Doubao and DeepSeek) models—using seven theory-grounded prompts within a Chinese ECE context. Through blind expert ratings and statistical analyses (ANOVA, MANOVA), we found that local Chinese models significantly outperformed global models on tasks requiring cultural nuance and pedagogical alignment, with effect sizes (Cohen's d) reaching as high as 14.63. These findings highlight the critical limitations of a one-size-fits-all approach and argue that deep localization is essential for developing effective and equitable AI-driven educational tools

Authors

• Sihang Yu, The Education University of Hong Kong

• Yichen Hou, The Education University of Hong Kong

• Hui Li, The Education University of Hong Kong