Humans have long relied on visual aids like sketches and diagrams to support reasoning and problem-solving. Visual tools, like auxiliary lines in geometry or graphs in calculus, are essential for understanding complex ideas. However, many tutoring systems remain text-based, providing feedback only through natural language. Leveraging recent advances in Large Multimodal Models (LMMs), this paper introduces Interactive Sketchpad, a tutoring system that combines language-based explanations with interactive visualizations to enhance learning. Built on a pre-trained LMM, Interactive Sketchpad is fine-tuned to provide step-by-step guidance in both text and visuals, enabling natural multimodal interaction with the student. Accurate and robust diagrams are generated by incorporating code execution into the reasoning process. User studies conducted on math problems such as geometry, calculus, and trigonometry demonstrate that Interactive Sketchpad leads to improved task comprehension, problem-solving accuracy, and engagement levels, highlighting its potential for transforming educational technologies. All code is available at: \url{https://stevenshinechen.github.io/interactivesketchpad/}.

SuperUROP allows me to leverage my machine learning skills, gained from experience with large language models, machine learning courses, and previous UROP work, in a longer term research project. I aim to advance my knowledge of multimodal AI and develop strong research skills, hoping to publish my results. I believe enhancing generalized reasoning is key to advancing artificial general intelligence, and my project is a step in that direction.