From Conversation to Human-AI Common Ground: Extracting Cognitive Workflows for Reuse in Sense-making Tasks

要旨

Knowledge workers increasingly rely on conversational AI for sense-making tasks (e.g., conducting market analysis), yet must repeatedly reconstruct context and intent to meet their goals. A formative study (N=10) showed that workflow reuse with AI often failed. Current tools either only remember preferences or enforce rigid, predefined workflows—neither adapts to evolving goals. We present ThinkFlow, a system that maintains a dynamic common ground through a cognitive workflow schema, enabling users to express intent and AI to adapt and reuse workflows across contexts. An expert-rating study shows that the schema can accurately capture the collocutor's reasoning process, and when reused for a similar task, improves the AI's responses compared to when the schema isn't present. A user study with eight knowledge workers demonstrates that ThinkFlow supports awareness of evolving workflows, intent expression, and flexible application across contexts.

著者
Xinyue Chen
University of Michigan, Ann Arbor, Michigan, United States
Varun Manjunatha
Adobe Research, College Park, Maryland, United States
Xu Wang
University of Michigan, Ann Arbor, Michigan, United States
Alexa Siu
Adobe Research, San Jose, California, United States

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: Multi-Agent Reasoning Systems for Sensemaking and Planning

P1 - Room 134
7 件の発表
2026-04-17 18:00:00
2026-04-17 19:30:00