将人工智能融入高等教育：一项使用“SAMCares：自适应学习中心”的试点研究方案
Integrating A.I. in Higher Education: Protocol for a Pilot Study with 'SAMCares: An Adaptive Learning Hub'
AI总结：
本文提出一个名为SAMCares的自适应学习中心，结合LLaMa-2 70B大语言模型与检索增强生成技术，为高校学生提供实时、个性化的学习支持，并通过对照试验评估其有效性。
摘要：
学习永无止境，自我成长也没有年龄限制。然而，教育环境在有效满足学生的包容性和多样化学习需求方面可能面临挑战。这些学生应当能够获得最先进的授课方法、在线资源和技术支持。然而，面对如此多样化的学习来源，学生在短时间内理解大量知识变得更加困难。传统的辅助技术和学习辅助工具往往缺乏个性化教育计划所需的动态适应性。大语言模型（LLM）已被用于语言翻译、文本摘要和内容生成等应用。随着过去几年人工智能的快速发展，AI驱动的聊天机器人和虚拟助手已经被开发出来。本研究旨在通过引入一个创新的学习伙伴来弥合这一差距，我们将其称为“SAMCares”。该系统利用大语言模型（LLM）（在我们的案例中，以LLaMa-2 70B作为基础模型）和检索增强生成（RAG）来提供实时、上下文感知且具有自适应性的教育支持。模型的上下文将仅限于萨姆休斯顿州立大学（SHSU）课程笔记的知识库。LLM组件支持类似聊天的交互环境，以满足每个学生独特的学习需求。为此，我们将构建一个基于Web的自定义图形用户界面（GUI）。同时，RAG增强了实时信息检索和文本生成能力，从而提供更准确且更具上下文针对性的帮助。Web GUI中还增加了上传额外学习资料的选项，以备需要额外知识支持时使用。该系统的有效性将通过对照试验和迭代反馈机制进行评估。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
