用可靠知识增强的智能助教，帮学生精准答疑并持续进化
Machine Assistant with Reliable Knowledge: Enhancing Student Learning via RAG-based Retrieval
- 结合向量与关键词双重检索，提升问答准确性
- 学生评分+教师修正反馈，知识库动态优化
- 适合教育场景替代课后答疑，也适用于技术客服
我们提出机器助教系统MARK，基于检索增强生成（RAG）框架，通过结构化知识库确保回答的事实一致性。为应对多样问题类型，采用密集向量与稀疏关键词联合检索策略，提升通用与专业问题的检索鲁棒性。系统包含反馈机制：学生可评分，教师可修订答案，并将修正内容回流至检索语料库，实现持续优化。该系统在课堂环境中替代传统答疑时段，成功处理各类学生提问；同时集成客户专属知识库，用于提供技术支持，验证其在实际应用中对上下文敏感任务的有效性。系统已公开，访问地址：https://app.eduquery.ai。
原文摘要 · Abstract (English)
We present Machine Assistant with Reliable Knowledge (MARK), a retrieval-augmented question-answering system designed to support student learning through accurate and contextually grounded responses. The system is built on a retrieval-augmented generation (RAG) framework, which integrates a curated knowledge base to ensure factual consistency. To enhance retrieval effectiveness across diverse question types, we implement a hybrid search strategy that combines dense vector similarity with sparse keyword-based retrieval. This dual-retrieval mechanism improves robustness for both general and domain-specific queries. The system includes a feedback loop in which students can rate responses and instructors can review and revise them. Instructor corrections are incorporated into the retrieval corpus, enabling adaptive refinement over time. The system was deployed in a classroom setting as a substitute for traditional office hours, where it successfully addressed a broad range of student queries. It was also used to provide technical support by integrating with a customer-specific knowledge base, demonstrating its ability to handle routine, context-sensitive tasks in applied domains. MARK is publicly accessible at https://app.eduquery.ai.
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