BLADE：通过对话和解释提升语言答案
BLADE: Better Language Answers through Dialogue and Explanations
AI总结：
BLADE通过引导学习者接触相关教学资源而非直接提供答案，提升学生对课程资源的导航能力和概念理解。
摘要：
基于大型语言模型（LLM）的教育助手通常提供直接答案，这会减少探索、自我解释和与课程材料的互动。我们提出了BLADE（通过对话和解释提升语言答案），一种基于对话的助教，引导学习者接触相关教学资源而非直接提供解决方案。BLADE使用经过精选的课程内容进行检索增强生成（RAG）框架，在响应学生查询时动态呈现教学上相关的摘录。与其提供最终答案，BLADE促使学习者直接与源材料互动以支持概念理解。我们在一个本科计算机科学课程中进行影响研究，展示了不同课程资源配置下BLADE相比单纯提供完整课程资源能够提升学生对课程资源的导航能力和概念表现。这些结果展示了基于对话的AI在促进主动学习和基于证据的推理方面的潜力。
英文摘要：
Large language model (LLM)-based educational assistants often provide direct answers offering little incentive for students to explore or engage with course materials. We present BLADE (Better Language Answers through Dialogue and Explanations), a retrieval-augmented generation (RAG) based conversational assistant grounded in course-specific content that guides students toward relevant materials through citation-grounded dialogue rather than delivering unsourced solutions. We evaluate BLADE in an advanced undergraduate NLP course with extensive instructional resources, where locating and synthesizing relevant material is a central challenge. During quizzes, students are assigned to one of three conditions: BLADE only, direct course materials only, or both; we measure performance and resource usage. Results show that students consistently select BLADE over direct materials when both are available, and that quiz performance is highest when students use BLADE alone. In contrast, simultaneous use of both resources is associated with significantly lower performance, suggesting that combining modalities may introduce cognitive load without benefit. These findings show the potential of RAG-based assistants as a citation-grounded interface for applying course content under open-book exam-like conditions, while cautioning that adding direct-material access does not necessarily help, and may hinder, in-task performance.
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。
