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

Researchers present a playbook for integrating generative AI into focus group research, organizing AI support systems by role (tool, co-host, host) and modality (text, voice, embodied). The work addresses methodological gaps in how AI can scaffold live conversation while identifying interactional trade-offs and risks that UXR teams must navigate.

This research bridges human-computer interaction and design methodology by formalizing how AI can augment focus group facilitation—a cornerstone of user research. Focus groups require skilled moderation to balance participation, maintain psychological safety, and extract nuanced insights through participant interaction. Traditional approaches are resource-intensive and moderator-dependent, creating variability in data quality. The authors recognize that AI tools already embedded in commercial meeting platforms (real-time summarization, turn management, thematic mapping) have practical applications for focus groups but lack clear methodological guidance.

The playbook's organization around role and modality reflects a sophisticated understanding of implementation trade-offs. An AI functioning as a tool (assisting the moderator) differs fundamentally from co-hosting arrangements or full autonomous hosting—each introduces different risks around bias, participant reactivity, and conversation authenticity. Voice and embodied modalities carry unique social dynamics absent from text-based systems. This work emerges amid broader adoption of AI in research workflows, where organizations seek to reduce costs and scale studies while maintaining methodological rigor.

For UXR teams and design researchers, the framework provides a decision matrix for evaluating when and how to deploy AI supports without compromising research validity. Organizations investigating AI-augmented research may find structured guidance lacking elsewhere. However, the primary impact is methodological rather than commercial—this work informs academic and professional research practice rather than creating market opportunities or affecting investor positioning. The research contributes to responsible AI implementation in knowledge work by centering risks and trade-offs rather than capability expansion.

• →AI can scaffold focus group facilitation through prompting, turn regulation, thematic mapping, and real-time summarization across three distinct roles.
• →The same AI capabilities produce different methodological outcomes depending on whether AI functions as tool, co-host, or autonomous host.
• →Text, voice, and embodied modalities create distinct interactional dynamics that researchers must evaluate when designing AI-supported focus groups.
• →UXR teams lack clear guidance on evaluating AI-supported focus groups as valid methodological configurations for collecting lived experiences.
• →AI facilitation introduces psychological safety and authenticity risks alongside efficiency gains that require explicit methodological consideration.