VectorizationLLM: Google open-weight AI tutors MATLAB vectorization

VectorizationLLM is a specialized large language model created by Ryan Duke for New York Institute of Technology's course CTEC 247: Applied Computational Analysis II. Built on Google open-weight LLMs, it employs a Retrieval-Augmented Generation (RAG) knowledge base and a carefully designed system prompt to deliver instructive assistance. The model helps students master smart vectorization, time/wave vector analysis, piecewise functions, Fourier analysis, and differential equations in MATLAB. Crucially, it avoids providing direct answers to problems; instead, it offers detailed conceptual explanations and examples drawn directly from in-class notes. Responses can include code snippets, text descriptions, and images, making it a multimodal teaching tool.

This approach represents a thoughtful application of LLMs in education, balancing the need for student support with academic integrity. By grounding its outputs in course-specific materials and withholding solutions, VectorizationLLM encourages active learning and problem-solving skills. The model's architecture—combining a RAG pipeline with a structured prompt—ensures relevance and accuracy while preventing misuse. For educators and institutions, this demonstrates how custom LLMs can be tailored to specific curricula to enhance understanding without compromising assessment integrity. As AI tutors proliferate, VectorizationLLM sets a precedent for responsible, pedagogically sound deployment.