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

arXiv: https://arxiv.org/abs/2502.01564 · HTML: https://mlsanity.com/paper/2502.01564/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 104 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## Builds on (most cited first, 17 of 17)

- [PromptChainer: Chaining Large Language Model Prompts through Visual Programming](https://mlsanity.com/paper/2203.06566/) (2022)
- [The Metacognitive Demands and Opportunities of Generative AI](https://mlsanity.com/paper/2312.10893/) (2023)
- [Graphologue: Exploring Large Language Model Responses with Interactive Diagrams](https://mlsanity.com/paper/2305.11473/) (2023)
- [Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models](https://mlsanity.com/paper/2402.11723/) (2024)
- [VISAR: A Human-AI Argumentative Writing Assistant with Visual Programming and Rapid Draft Prototyping](https://mlsanity.com/paper/2304.07810/) (2023)
- [Synergi: A Mixed-Initiative System for Scholarly Synthesis and Sensemaking](https://mlsanity.com/paper/2308.07517/) (2023)
- [The Role of Inclusion, Control, and Ownership in Workplace AI-Mediated Communication](https://mlsanity.com/paper/2309.11599/) (2023)
- [Summaries, Highlights, and Action items: Design, implementation and evaluation of an LLM-powered meeting recap system](https://mlsanity.com/paper/2307.15793/) (2023)
- [Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making](https://mlsanity.com/paper/2302.08807/) (2023)
- [AffectiveSpotlight: Facilitating the Communication of Affective Responses from Audience Members during Online Presentations](https://mlsanity.com/paper/2101.12284/) (2021)
- [Facilitating Global Team Meetings Between Language-Based Subgroups: When and How Can Machine Translation Help?](https://mlsanity.com/paper/2209.02906/) (2022)
- [CrossTalk: Intelligent Substrates for Language-Oriented Interaction in Video-Based Communication and Collaboration](https://mlsanity.com/paper/2308.03311/) (2023)
- [LADICA: A Large Shared Display Interface for Generative AI Cognitive Assistance in Co-Located Team Collaboration](https://mlsanity.com/paper/2409.13968/) (2024)
- [Large Scale Analysis of Multitasking Behavior During Remote Meetings](https://mlsanity.com/paper/2101.11865/) (2021)
- [The CoExplorer Technology Probe: A Generative AI-Powered Adaptive Interface to Support Intentionality in Planning and Running Video Meetings](https://mlsanity.com/paper/2405.18239/) (2024)
- [MeetCues: Supporting Online Meetings Experience](https://mlsanity.com/paper/2010.06259/) (2020)
- [MeetScript: Designing Transcript-based Interactions to Support Active Participation in Group Video Meetings](https://mlsanity.com/paper/2309.12115/) (2023)

## Built on by (most cited first, 3 of 3)

- [Designing AI-Supported Focus Groups: A Role x Modality Playbook](https://mlsanity.com/paper/2606.11835/) (2026)
- [MOONWALK: Mediating Operations with Intent-Evidence-Action Alignment Across Junior-Supervisor Review Workflows in Animation/VFX Pre-Production](https://mlsanity.com/paper/2609.10385/) (2026)
- [DocuTeam: Mixed-Initiative Multi-Agent Discussions around Evolving Documents](https://mlsanity.com/paper/2609.29309/) (2026)

## Abstract (arXiv)

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.