A New Paradigm for Preschool Teachers: Integrating STEM and AI in Flipped Learning

Abstract

Integrating Artificial Intelligence (AI) and STEM in early childhood education requires effective teacher professional development, yet practical and pedagogical barriers often hinder this. This study addresses this gap by examining the effects of a 13-week online flipped learning model, focused on STEM-AI integration, on 42 preschool teachers. Using an embedded mixed-methods design, the study collected quantitative data (pre/post-test scales) and qualitative data (semi-structured interviews) to measure changes in AI awareness, innovative and computational thinking skills, and teacher opinions. Quantitative results showed that the intervention had a significant positive effect on all three measured skills. Qualitative findings corroborated this, revealing that the training met teachers' expectations, expanded their understanding of AI to include complex concepts like machine learning, and critically, highlighted their significant concerns regarding data privacy and the ethical use of student data. This study provides novel evidence for the effectiveness of an online flipped learning model in developing preschool teachers' advanced pedagogical and technical competencies. Findings underscore the model's practical utility but also signal an urgent need for policies and training that directly address edu-cators' ethical and security concerns.