Amid calls to redefine core teacher competencies in the era of generativeAI, this study aimed to diagnose in-service early childhood teachers’ AI competency levels and educational needs and identify the core components of a professional development (PD) program to strengthen those competencies. Building on prior research, a survey instrument was developed and administered to 180 in-service early childhood teachers; descriptive statistics and reliability indices (Cronbach’s α) were calculated. Open-ended responses were analyzed using content analysis, and three focus group interviews (FGIs) were conducted with 18 teachers (≥5 years of experience) to derive in-depth qualitative insights. Findings indicated relatively high levels ofAI conceptual understanding, human-AI relationship perception, and AI ethics awareness, but a lower level of AI utilization competency, revealing a gap between awareness and classroom practice. Synthesizing the open-ended and FGI results, the proposed PD should adopt an integrated design (embedding AI within the existing cycle of observation-analysis-lesson planning-assessment), experiential/project-based blended delivery, and performance-based assessment (portfolio, product demonstration, and classroom application). The recommended content triad comprises ethics/privacy/copyright, AI-based instructional material development, and child developmentdata analysis. In addition, standard guidelines, verification checklists, post-training coaching, practitioner communities, and periodic updates, supported by institutional infrastructure and time allocation, are essential for sustainable scaling. By proposingan evidence-based, modular PD framework — Foundations (concepts/ethics) - Practice (materials/data) - Application (play/assessment) — this study provides baselineevidence to support safe and sustainable AI use in early childhood education
Deahun Choi (Tue,) studied this question.