为焦点小组设计AI辅助方案，提升访谈效率与深度。
Designing AI-Supported Focus Groups: A Role x Modality Playbook
- 按角色与模态划分AI支持方式，提供可操作的实践框架。
- 明确不同AI介入对讨论平衡性与心理安全的影响。
- 适合用户体验研究团队探索人机协同访谈新范式。
收集参与者的切身体验是设计研究的核心。焦点小组的独特价值在于参与者不仅分享个人经历，还能相互回应，揭示对比、分歧与集体意义建构。然而，焦点小组耗时耗力，且高度依赖主持技巧：主持人需引导具体细节、平衡发言机会、管理话题流动，并维持心理安全感，细微的主持决策可能影响讨论重点。近期人机交互研究及商用会议工具表明，生成式AI可通过提示生成、发言调控、主题映射和实时摘要等方式辅助实时对话。但用户体验研究团队缺乏清晰的指南，难以理解这些能力在焦点小组中的实际意义及方法论风险。本文综合现有AI辅助实时对话的研究，提出一个基于角色（工具、协主持人、主持人）与模态（文本、语音、具身）的焦点小组专用行动手册，并分析互动权衡，识别评估AI支持型焦点小组作为方法配置的开放问题。
原文摘要 · Abstract (English)
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.
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