Contexty: Capturing and Organizing In-situ Thoughts for Context-Aware AI Support

2026-04-13

AI Summary

This study addresses the challenge of capturing and sharing users’ cognitive traces in complex knowledge work, which often leaves AI systems without a meaningful understanding of authentic task context. The authors propose a novel paradigm centered on in-situ, user-generated snippet memoing—fragmentary notes that serve as primary sources of contextual information for AI. For the first time, this approach treats such cognitive traces as first-class inputs and grants users direct editorial control over their contextual representations. Through a probe system comprising interaction mechanisms and a context-organizing interface, users can instantly record, inspect, and restructure their thought processes, thereby guiding AI to produce responses more aligned with their intent. Experimental results demonstrate that this method significantly enhances users’ task awareness and ability to structure thinking, with 78.1% of participants preferring AI responses grounded in snippet-based context, leading to greater perceived agency and ownership in collaborative creation.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

During complex knowledge work, people engage in iterative sensemaking: interpreting information, connecting ideas, and refining their understanding. Yet in current human-AI collaboration, these cognitive processes are difficult to share and organize for AI. They arise in situ and are rarely captured without interrupting the task, and even when expressed, remain scattered or reduced to system-generated summaries that fail to reflect users' cognitive processes. We address this challenge by enabling AI context that is grounded in users' cognitive traces and can be directly inspected and revised by the user. We first explore this through a probe system that supports in-situ snippet memoing, allowing users to easily share their cognitive moves. Our study (N=10) highlights the value of capturing such context and the challenge of organizing it once accumulated. We then present Contexty, which supports users in inspecting and refining these contexts to better reflect their understanding of the task. Our evaluation (N=12) showed that Contexty improved task awareness, thought structuring, and users' sense of authorship and control, with participants preferring snippet-grounded AI responses over non-grounded ones (78.1%). We discuss how capturing and organizing users' cognitive context enables AI as a context-aware collaborator while preserving user agency.

Problem

• sensemaking
• cognitive traces
• context-aware AI
• human-AI collaboration
• in-situ thinking

Innovation

• context-aware AI
• cognitive traces
• in-situ memoing
• human-AI collaboration
• user agency