Raeanne Li¹ and Carlos Gonzalez², ¹USA, ²University of California, USA

For many students, mathematics is a challenging subject because of its abstract nature. Through my own learning experience, I realized how visual representations can simplify complex problems, making them intuitive and engaging. These moments sparked my passion for exploring how visualization can empower math learners, especially those with diverse learning needs and styles, to overcome the barriers associated with traditional teaching methods [1]. Students with dyslexia, ADHD, or autism often face challenges with text-heavy explanations and abstract concepts, but visualization tools, such as charts, diagrams, and interactive models, can make a difference, offering alternative approaches that better support their learning [2]. This project focuses on developing an AI-powered visualization platform designed to generate visual representations for math word problems. With Retrieval-Augmented Generation (RAG), the smart system retrieves relevant data from external sources and generates content-specific math problems, ensuring high accuracy and alignment with user queries. A key contribution of this research is the integration of a dual-LLM architecture with RAG to enhance diagram creation. The first LLM generates clear, concise, and imperative instructions from natural language queries, while the second LLM translates these instructions into valid Scalable Vector Graphics (SVG) code for precise diagrams. The integrated approach allows for automated, scalable, and customizable diagram generation, offering an engaging and accessible learning experience for different problems. Ultimately, the smart system combines problem generation and visualization into a unified web and mobile application, providing diverse learners with powerful tools to engage with math.

Math Visualization, Accessible Learning, Dual-LLM Architecture, AI-Powered Learning Tools