Large Language Models (LLMs) have the potential to enhance learning among students. These tools can be used in chatbot systems allowing students to ask questions about course material, in particular when plugged with the so-called Retrieval Augmented Systems (RAGs). RAGs allow LLMs to access external knowledge, which improves tailored responses when used in a chatbot system. This thesis studies different RAGs through an experimentation approach where each RAG is constructed using different sets of parameters and tools, including small and large language models. We conclude by suggesting which of the RAGs best adapts to high school courses in Physics and undergraduate courses in Mathematics, such that the retrieval systems together with the LLMs are able to return the most relevant answers from provided course material. We conclude with two RAG-powered LLM with different configurations performing over 64% accuracy in physics and 66% in mathematics.