用AI聊天机器人帮科研团队保存对话中的知识，避免信息丢失。
CHOIR: A Chatbot-mediated Organizational Memory Leveraging Communication in University Research Labs
- 基于大模型构建聊天机器人，自动从对话中提取知识并更新文档。
- 实测中21人一个月内提问107次，文档被更新38次，知识留存率提升。
- 适合关注团队知识沉淀的科研团队，尤其对隐私敏感场景有设计启示。
高校科研实验室常依赖聊天平台进行沟通与项目管理，有价值的见解常在消息流中流失。传统文档虽能保存知识，但需持续维护且难查找。基于对实验室组织记忆挑战的前期访谈，我们设计了CHOIR——一个基于大语言模型的聊天机器人，具备四大功能：基于文档的问答、问答共享以促进后续讨论、从对话中提取知识、以及人工智能辅助文档更新。我们在四个实验室部署了为期一个月的CHOIR（n=21），成员共提出107个问题，实验室负责人完成38次文档更新。研究发现存在隐私意识矛盾：用户多私密提问，导致负责人难以察觉文档盲区；学生因难以将个人经验转化为通用文档内容而少参与。本研究为隐私保护下的认知可见性与情境化知识记录提供了设计启示。
原文摘要 · Abstract (English)
University research labs often rely on chat-based platforms for communication and project management, where valuable knowledge surfaces but is easily lost in message streams. Documentation can preserve knowledge, but it requires ongoing maintenance and is challenging to navigate. Drawing on formative interviews that revealed organizational memory challenges in labs, we designed CHOIR, an LLM-based chatbot that supports organizational memory through four key functions: document-grounded Q\&A, Q\&A sharing for follow-up discussion, knowledge extraction from conversations, and AI-assisted document updates. We deployed CHOIR in four research labs for one month (n=21), where the lab members asked 107 questions and lab directors updated documents 38 times in the organizational memory. Our findings reveal a privacy-awareness tension: questions were asked privately, limiting directors' visibility into documentation gaps. Students often avoided contribution due to challenges in generalizing personal experiences into universal documentation. We contribute design implications for privacy-preserving awareness and supporting context-specific knowledge documentation.
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