CHOIR: A Chatbot-mediated Organizational Memory Leveraging Communication in University Research Labs

要旨

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.

受賞
Honorable Mention
著者
Sangwook Lee
Virginia Tech, Blacksburg, Virginia, United States
Adnan Abbas
Virginia Polytechnic Institute & State University (Virginia Tech), Blacksburg, Virginia, United States
Yan Chen
Virginia Tech, Blacksburg, Virginia, United States
Young-Ho Kim
NAVER AI Lab, Seongnam, Korea, Republic of
Sang Won Lee
Virginia Tech, Blacksburg, Virginia, United States
動画

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: Communication

P1 - Room 131
7 件の発表
2026-04-17 20:15:00
2026-04-17 21:45:00