MeetMap: Real-Time Collaborative Dialogue Mapping with LLMs in Online Meetings

TL;DR

MeetMap introduces real-time, AI-assisted dialogue mapping in online meetings, enabling users to visually organize ideas and improve note-taking by balancing AI automation with user control.

Contribution

This work presents two novel system variants for real-time dialogue mapping using LLMs, enhancing user interaction and mental model alignment during online meetings.

Findings

Users preferred MeetMap over traditional note-taking methods.

MeetMap's AI assistance improved ease of use and sense-making.

Participants appreciated the balance of AI automation and user control.

Abstract

Video meeting platforms display conversations linearly through transcripts or summaries. However, ideas during a meeting do not emerge linearly. 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 while giving them sufficient control when using AI, we explore two system variants that encompass different levels of AI assistance. 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 over traditional methods for taking notes, 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.