🤖 AI Summary
This work addresses the challenges engineering students face in grasping complex concepts such as intelligent vectorization, time–frequency analysis, piecewise functions, Fourier analysis, and differential equations in computational coursework. To support learning in CTEC 247, we developed an AI teaching assistant based on Google’s open-source large language model, uniquely integrating retrieval-augmented generation (RAG) with a pedagogical scaffolding mechanism. Through carefully engineered system prompts, the assistant delivers multimodal responses—comprising code, text, and visualizations—that provide conceptual explanations grounded in lecture notes rather than direct solutions. Deployed at New York Institute of Technology, the system significantly enhanced students’ comprehension of advanced computational topics and received positive instructional feedback, demonstrating its effectiveness and innovation in engineering education.
📝 Abstract
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.