AI-Driven Teacher–Children Interaction in Health Education Activities: Shaping Future-Ready Kindergarten Instructional Analysis Networks

This study investigates AI-driven teacher–children interaction in health education activities within future-ready kindergarten networks, and uses programs such as “Improved Flanders Interaction Analysis System (IFIAS) coding assistant” and “Information Technology-Based Interaction Analysis System (IFIAS) analysis assistant” to code and statistically analyze the activities. It analyzes the interaction between teachers and children in learning activities through the analysis of the transfer matrix, activity structure ratio, and teacher’s teaching activity map. The results showed that in the three open classes, although the atmosphere between teachers and children was harmonious, the frequency of interaction was higher, but the children’s initiative was weakened. Teachers have less verbal feedback to children, and their feedback ability is more lacking. The application level of information technology of teachers is low, the teaching approach lacks diversity, and the ability to integrate information technology and classroom teaching is relatively lacking. The study suggests that preschool teachers should improve their talk interaction skills. During the educational activities, the teacher should improve their verbal interaction skills, pay attention to children’s performance during teaching activities, strengthen their verbal feedback skills, improve their information technology literacy, and make rational use of digital education platforms and AI-assisted learning devices to build a future-oriented kindergarten instructional analysis network to improve the quality, convenience, and innovation of preschool education.