用希腊语RAG聊天机器人辅助高校教学,提升学习支持与备课效率
From Textbook to Talkbot: A Case Study of a Greek-Language RAG-Based Chatbot in Higher Education
- 基于RAG框架,结合课程内容生成准确回答
- 有效减少大模型幻觉,提供上下文相关的可靠信息
- 适合需要多语言AI教育工具的高校师生及教务人员
将AI聊天机器人引入教育场景,为教学与学习提供新路径。本研究设计并应用了一款面向高等教育的希腊语AI聊天机器人,解决希腊语特有的语言挑战,确保回答精准且符合课程内容。该系统基于检索增强生成(RAG)架构,通过引用具体课程资料生成响应,显著提升可靠性,缓解大语言模型常见的幻觉和错误信息问题。该聊天机器人兼具双重功能:帮助学生即时获取学术支持,协助教师快速生成教学材料,促进学习自主性并优化教学设计流程。研究旨在评估其有效性、可靠性及可用性,探索其在提升教育实践与成果方面的潜力,并推动特定语言环境下AI技术的普及。研究成果有望为智能教育发展提供可借鉴的范例。
原文摘要 · Abstract (English)
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.
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