This research project delves into the potential of machine learning and computer vision to assist students in visualising complex mathematical concepts, with a specific focus on vectors. Recognising the challenges students face in grasping abstract mathematical notions, the study investigates the development of a system using GPT-4o that automates the visualisation of handwritten vector equations. The system employs computer vision, a rapidly evolving field in artificial intelligence, to analyse and interpret handwritten mathematical expressions. By extracting key components such as variables, coefficients, operators, and vector notation, the system generates precise graphical representations of vector equations. This approach aims to bridge the gap between theoretical concepts and visual understanding, enabling students to develop a more intuitive grasp of vectors. The research evaluates the system’s accuracy in recognising and interpreting diverse handwriting styles, assessing its potential to improve students’ understanding of vector concepts. While acknowledging challenges such as the need for extensive training datasets and refined algorithms to handle the nuances of handwritten mathematical notation, the project outlines future work including expanding the system’s scope to other mathematical topics, incorporating interactive features, and integrating with augmented reality for a more immersive experience. The ultimate goal is to seamlessly integrate this tool into students’ learning devices, providing accessible and convenient support for their mathematical development. This research contributes to the advancement of AI-driven educational tools, showcasing the potential of machine learning to enhance students’ learning experiences and bridge the gap between abstract concepts and visual understanding.