A Study Regarding Experiences and Perceptions of Pre-primary Early Childhood Teachers in Simulation Classes Using Generative AI

Purpose: This study aimed to explore how pre-service Early Childhood Education teachers experienced the teaching process, encountered challenges and achievements in technology implementation, and perceived changes in their attitudes toward generative AI-based mock teaching activities. By qualitatively analyzing the complex experiences of pre-service teachers, this study aimed to provide insights for teacher education institutions in the preparation of pre-service Early Childhood Education teachers. Methods: Twenty third-year students majoring in Early Childhood Education at a four-year university located in Gyeonggi-do participated in this study. Over a 15-week period during the 'Early Childhood Mathematics Education' course, generative AI-based mock teaching activities were implemented. Reflective journals were collected as primary data. Additional data sources included mock teaching plans, peer evaluations, instructor feedback, and researcher field notes. Data analysis employed an inductive thematic analysis approach to generate codes, which were subsequently organized into subcategories and core categories. Results: The analysis yielded four core categories and eight subcategories. First, 'enhancing teaching efficiency' enabled participants to reduce class preparation time and develop creative teaching materials. Second, 'transformation from initial hesitation to positive expectations' revealed how repeated experience helped pre-service teachers overcome techno-anxiety. Third, 'responsible AI use and ethical deliberation' demonstrated participants' growing awareness of the need to verify the reliability of AI-generated content and protect personal information. Fourth, 'professional development and positive perspectives on future education' showed how participants redefined teachers' roles in the AI era and developed motivation for continuous learning. Conclusion: The experience of generative AI-based mock teaching enables comprehensive growth for pre-service Early Childhood Education teachers, encompassing enhanced teaching efficiency, overcoming initial hesitation, ethical maturity, and professional development. These findings suggest that such experiences contribute to cultivating competent teachers capable of leading future early childhood education, transcending mere technical skill acquisition.