从教材到谈bot：面向高等教育的希腊语基于检索增强生成的聊天机器人案例研究
From Textbook to Talkbot: A Case Study of a Greek-Language RAG-Based Chatbot in Higher Education
AI总结：
本研究开发了一个基于RAG框架的希腊语AI聊天机器人，用于高等教育中的教学支持，旨在提升教育实践和AI技术在语言教育中的应用。
摘要：
将AI聊天机器人引入教育领域为教学和学习的变革开辟了新路径，为教育工作者和学习者提供了增强的支持。本研究探讨了AI聊天机器人作为教育工具在高等教育中的设计和应用。该聊天机器人专为希腊语设计，解决希腊语特有的语言挑战，同时提供准确、基于上下文的支持，与课程内容一致。AI聊天机器人基于检索增强生成（RAG）框架，通过特定课程内容来支撑其响应。RAG架构显著提高了聊天机器人的可靠性，通过提供准确、基于上下文的响应，同时缓解与大型语言模型（LLMs）相关的常见挑战，如幻觉和错误信息。AI聊天机器人具有双重功能：它使学生能够获取准确的即时学术支持，同时帮助教育工作者快速创建相关教育材料。这种双重功能促进学习者的自主性，并简化了教学设计过程。本研究旨在评估RAG聊天机器人在高等教育中的有效性、可靠性和感知可用性，探索其在增强教育实践和成果以及支持更广泛采用AI技术在语言特定教育环境中的潜力。本研究的发现预计将为新兴的AI驱动教育领域做出贡献，通过展示智能系统如何有效与教学目标对齐。
英文摘要：
The integration of AI chatbots into educational settings has opened new pathways for transforming teaching and learning, offering enhanced support to both educators and learners. This study investigates the design and application of an AI chatbot as an educational tool in higher education. Designed to operate in the Greek language, the chatbot addresses linguistic challenges unique to Greek while delivering accurate, context grounded support aligned with the curriculum. The AI chatbot is built on the Retrieval Augmented Generation (RAG) framework by grounding its responses in specific course content. RAG architecture significantly enhances the chatbots reliability by providing accurate, context-aware responses while mitigating common challenges associated with large language models (LLMs), such as hallucinations and misinformation. The AI chatbot serves a dual purpose: it enables students to access accurate, ondemand academic support and assists educators in the rapid creation of relevant educational materials. This dual functionality promotes learner autonomy and streamlines the instructional design process. The study aims to evaluate the effectiveness, reliability, and perceived usability of RAG based chatbots in higher education, exploring their potential to enhance educational practices and outcomes as well as supporting the broader adoption of AI technologies in language specific educational contexts. Findings from this research are expected to contribute to the emerging field of AI driven education by demonstrating how intelligent systems can be effectively aligned with pedagogical goals.
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。
