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VectorizationLLM is a specialized Large Language Model tailored to enhance student learning in advanced computational topics such as vectorization and differential equations using MATLAB. By leveraging a Retrieval Augmented Generation (RAG) architecture, the model delivers contextualized explanations and examples drawn from course materials, ensuring that students engage with the content rather than receive direct answers. The implementation in the CTEC 247 course at NYIT demonstrates its effectiveness as an instructive assistant in a complex subject area.
This AI assistant transforms how students grasp complex computational concepts by providing contextualized, example-driven learning without giving away answers.
VectorizationLLM is a specialized Large Language Model based on Google open-weight LLMs. The model is designed to assist students to learn smart vectorization, time/wave vector analysis, piecewise functions, Fourier analysis, and differential equations in MATLAB. The course application is CTEC 247: Applied Computational Analysis II by the Department of Electrical&Computer Engineering Technology at New York Institute of Technology Old Westbury. The LLM model is designed to be an instructive assistant, providing detailed explanations of concepts with examples from in-class notes without providing direct answers to questions. The model is designed with a RAG (Retrieval Augmented Generation) knowledge base and system prompt architecture. Examples in both code, text, and images are provided in the LLM responses.
