# Designing AI-Supported Focus Groups: A Role x Modality Playbook

arXiv: https://arxiv.org/abs/2606.11835 · HTML: https://mlsanity.com/paper/2606.11835/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 10 Jun 2026.

## Builds on (most cited first, 11 of 11)

- [Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making](https://mlsanity.com/paper/2301.05809/) (2023)
- [Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care](https://mlsanity.com/paper/2302.00096/) (2023)
- [Overcoming Algorithm Aversion: A Comparison between Process and Outcome Control](https://mlsanity.com/paper/2303.12896/) (2023)
- [Summaries, Highlights, and Action items: Design, implementation and evaluation of an LLM-powered meeting recap system](https://mlsanity.com/paper/2307.15793/) (2023)
- [AffectiveSpotlight: Facilitating the Communication of Affective Responses from Audience Members during Online Presentations](https://mlsanity.com/paper/2101.12284/) (2021)
- [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)
- [Observe, Ask, Intervene: Designing AI Agents for More Inclusive Meetings](https://mlsanity.com/paper/2501.10553/) (2025)
- [MeetMap: Real-Time Collaborative Dialogue Mapping with LLMs in Online Meetings](https://mlsanity.com/paper/2502.01564/) (2025)
- [Interview AI-ssistant: Designing for Real-Time Human-AI Collaboration in Interview Preparation and Execution](https://mlsanity.com/paper/2504.13847/) (2025)
- [What was Hybrid? A Systematic Review of Hybrid Collaboration and Meetings Research](https://mlsanity.com/paper/2111.06172/) (2021)

## Abstract (arXiv)

Collecting participants' lived experiences is central to design research. Focus groups are uniquely valuable because participants not only share individual accounts but also respond to one another, surfacing comparison, disagreement, and collective sensemaking. However, focus groups are resource-intensive and highly sensitive to facilitation: moderators must probe for specificity, balance participation, manage topic flow, and sustain psychological safety, and subtle facilitation choices can shape what becomes salient. Recent HCI work and commercial meeting tools show that generative AI can scaffold live conversation through prompting, turn regulation, thematic mapping, and real-time summarization. Yet UXR teams lack a clear map of what these capabilities mean in focus groups and what methodological risks they introduce. We synthesize AI supports for live conversation and translate them into a focus-group-specific playbook organized by AI role (tool, co-host, host) and modality (text, voice, embodied).We synthesize prior work on AI-supported live conversation and propose a focus-group-specific playbook of AI supports organized by role (tool, co-host, host) and modality (text, voice, embodied). We characterize interactional trade-offs and identify open questions for evaluating AI-supported focus groups as methodological configurations.