Exploring 2D Geometric Shape Classification Using <scp>AI</scp> ‐Driven Feature Tables in Mathematics

ABSTRACT This study explored the effectiveness of an AI‐integrated instructional task designed to enhance preservice teachers' understanding of the features and hierarchical relationships of 2D geometric shapes. Originally developed and tested in online K‐12 professional development settings, this intervention was adapted for in‐person preservice teacher education context in this study. Data were collected from 17 preservice teachers through demographic surveys, pre‐ and posttests using the Van Hiele geometry framework, hierarchical diagram tasks, feature table creation during the intervention, and postintervention reflections. Findings indicated a statistically significant improvement in the accuracy and complexity of postintervention hierarchical diagrams, along with a descriptively higher mean score on the posttest of Van Hiele geometry content knowledge. Postintervention diagram classifications revealed a greater number of participants achieving the highest level of understanding of hierarchical relationships. Thematic analysis of participants' reflections suggested an increased awareness of AI integration in teaching and a deeper conceptual understanding of classification and hierarchy. This study highlights a practical approach for future educators to incorporate AI concepts into mathematics instruction, supporting the connection between abstract geometric ideas and real‐world applications.