AI-Assisted Inquiry-Based Learning Activity Design and Practice in High School Mathematics

Abstract

This study aims to explore how Artificial Intelligence (AI) technology can empower inquiry-based learning in high school mathematics. Addressing issues such as limited resources, insufficient guidance, and simplistic evaluation in traditional inquiry activities, this research proposes a design framework for AI-assisted inquiry-based learning activities, with the core concept of "invisible technology, visible thinking". This framework organically integrates AI technologies like big data analysis, intelligent tutoring, and knowledge graphs into the entire inquiry process: "context creation - question posing - collaborative inquiry - outcome generation - reflective evaluation". Through practical implementation in multiple classes of a high school, focusing on topics such as "functions", "geometry", and "probability and statistics", this study collected students' activity outcomes, classroom observation data, and learning feedback. The results indicate that AI technology can provide dynamic, visual learning scaffolds and personalized cognitive support for inquiry-based learning, effectively fostering students' higher-order mathematical thinking skills like question posing, logical reasoning, and collaborative inquiry, thereby enhancing the depth and efficiency of inquiry activities. This study offers an operable practical paradigm for the deep integration of "AI + Education" in subject-specific teaching.