MeetMap: Balancing AI Assistance and User Agency for Effective Real-Time Sense-Making in Meetings

Video meeting platforms display conversations linearly through transcripts or summaries. However, ideas during a meeting do not linearly emerge. We leverage LLMs to create dialogue maps in real-time to help people visually structure and connect ideas. Balancing the need to reduce the cognitive load on users during the conversation and give users sufficient control when using AI-generated content, we explore two human-AI collaborative methods. In Human-Map, AI generates summaries of conversations as nodes, and users create dialogue maps with the nodes. In AI-Map, AI produces dialogue maps where users can make edits. We ran a within-subject experiment with ten pairs of users, comparing the two MeetMap variants and a baseline. Users preferred MeetMap to traditional methods for note-taking, which aligned better with their mental models of conversations. Users liked the ease of use for AI-Map due to the low effort demands and appreciated the hands-on opportunity in Human-Map for sense-making. This work informs the future design of AI-assisted tools for real-time cognitive scaffolding in meetings by emphasizing the necessity to balance AI assistance with synchronicity and user agency to enhance collaborative sense-making.