Abstract
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.
Abstract (translated)
学习永无止境，没有年龄限制去发展自己。然而，教育领域可能会面临照顾学生多样性需求和理解能力不足的挑战。这些学生应享有最先进的大课教学方法、在线资源和科技需求。然而，尽管有各种各样的学习资源，学生在短时间内理解大量知识仍然变得更加困难。传统的辅助技术和学习辅助工具通常缺乏个性化的教育计划所需的动态适应性。近年来，随着人工智能的快速发展，已经开发出了一些基于人工智能的聊天机器人或虚拟助手。这项研究旨在通过介绍我们称之为“SAMCares”的创新学习伙伴来填补这一空白。该系统利用大型语言模型（LLM）（在我们的案例中，LLaMa-2 70B作为基础模型）和Retriever-Augmented Generation（RAG）为每个学生提供实时的、上下文感知和自适应的教育支持。模型的上下文将局限于德克萨斯州休斯顿州立大学（SHSU）的课程笔记知识库。LLM部分使学生能够以类似聊天室的环境与它互动，满足每个学生的独特学习需求。为此，我们将构建一个自定义的网页GUI。同时，RAG通过实时信息检索和文本生成增强，提供更准确、上下文相关的帮助。在网页GUI中增加上传额外学习材料的选项，以便需要额外知识支持时使用。系统将通过控制试验和迭代反馈机制来评估其有效性。
URL
https://arxiv.org/abs/2405.00330
