  Learning never ends, and there is no age limit to grow yourself. However, the
educational landscape may face challenges in effectively catering to students'
inclusion and diverse learning needs. These students should have access to
state-of-the-art methods for lecture delivery, online resources, and technology
needs. However, with all the diverse learning sources, it becomes harder for
students to comprehend a large amount of knowledge in a short period of time.
Traditional assistive technologies and learning aids often lack the dynamic
adaptability required for individualized education plans. Large Language Models
(LLM) have been used in language translation, text summarization, and content
generation applications. With rapid growth in AI over the past years,
AI-powered chatbots and virtual assistants have been developed. This research
aims to bridge this gap by introducing an innovative study buddy we will be
calling the 'SAMCares'. The system leverages a Large Language Model (LLM) (in
our case, LLaMa-2 70B as the base model) and Retriever-Augmented Generation
(RAG) to offer real-time, context-aware, and adaptive educational support. The
context of the model will be limited to the knowledge base of Sam Houston State
University (SHSU) course notes. The LLM component enables a chat-like
environment to interact with it to meet the unique learning requirements of
each student. For this, we will build a custom web-based GUI. At the same time,
RAG enhances real-time information retrieval and text generation, in turn
providing more accurate and context-specific assistance. An option to upload
additional study materials in the web GUI is added in case additional knowledge
support is required. The system's efficacy will be evaluated through controlled
trials and iterative feedback mechanisms.
