Developing an AI-Supported Approach to Identify Instructional Groupings in Early Childhood Education Classrooms 

Abstract

High-quality early childhood classrooms provide children with a nurturing environment to develop their physical, social, and academic capabilities. Understanding how instructional time is organized in pre-K classrooms is essential for supporting high-quality teaching and coaching. This technical white paper examines the feasibility of using AI-supported approaches to automatically identify instructional groupings (e.g., small group, whole group) in pre-K classroom video recordings. The research team developed a codebook, conducted human annotations and evaluated large language models using OpenAI’s GPT-4.1-mini and GPT-5 mini on the automatic identification task. Findings demonstrate promising early performance, highlighting identification challenges, and offer future steps needed to advance scalable, trustworthy tools for early learning settings.