用双路知识融合让虚拟助教更懂学生和老师。
YA-TA: Towards Personalized Question-Answering Teaching Assistants using Instructor-Student Dual Retrieval-augmented Knowledge Fusion
- 双路检索：同时抓取教师讲义与学生提问中的知识
- 生成回答时融合双端信息，提升个性化与准确性
- 适合大班教学场景，帮助教师减轻答疑负担
师生互动对提升学业表现至关重要，但在大班授课中，教师往往难以及时提供个性化支持。为此，我们提出一种新型虚拟助教YA-TA，其响应基于课程内容且易于理解。为实现该目标，我们引入双路检索增强知识融合（DRAKE）框架，通过同时检索教师与学生知识并进行融合，生成定制化回复。在真实课堂环境中的实验表明，DRAKE框架能有效对齐来自教师与学生两方的知识。此外，我们还扩展了YA-TA功能，包括问答看板与自测工具，以增强整体学习体验。相关视频已公开。
原文摘要 · Abstract (English)
Engagement between instructors and students plays a crucial role in enhancing students'academic performance. However, instructors often struggle to provide timely and personalized support in large classes. To address this challenge, we propose a novel Virtual Teaching Assistant (VTA) named YA-TA, designed to offer responses to students that are grounded in lectures and are easy to understand. To facilitate YA-TA, we introduce the Dual Retrieval-augmented Knowledge Fusion (DRAKE) framework, which incorporates dual retrieval of instructor and student knowledge and knowledge fusion for tailored response generation. Experiments conducted in real-world classroom settings demonstrate that the DRAKE framework excels in aligning responses with knowledge retrieved from both instructor and student sides. Furthermore, we offer additional extensions of YA-TA, such as a Q&A board and self-practice tools to enhance the overall learning experience. Our video is publicly available.
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