InterFlow：设计无感AI以赋能半结构化访谈中的访谈者
InterFlow: Designing Unobtrusive AI to Empower Interviewers in Semi-Structured Interviews
AI总结：
InterFlow通过AI驱动的视觉支架，帮助访谈者更高效地管理半结构化访谈流程，减少认知负担并提升访谈质量。
摘要：
半结构化访谈是定性研究中常用的方法。然而，进行高质量的访谈在认知上具有挑战性，需要很强的访谈技巧。为了降低这一门槛，我们提出了InterFlow，一种由AI驱动的视觉支架，帮助访谈者管理访谈流程，并促进实时数据的理解。该系统会动态地将访谈脚本适应于正在进行的对话，并提供一个视觉计时器来跟踪访谈进度和对话平衡。它进一步通过三个层次的自动化支持信息采集：手动输入、AI辅助摘要（用户指定的重点）以及一个共访谈代理，该代理主动揭示潜在的后续问题。一项被试内用户研究（N=12）表明，InterFlow减少了访谈者的认知负荷，并促进了访谈过程。基于用户研究的结果，我们提供了无感且保持自主权的AI辅助设计启示，适用于时间敏感且认知需求高的情况。
英文摘要：
Semi-structured interviews are a common method in qualitative research. However, conducting high-quality interviews is cognitively demanding and requires strong interviewing skills. To lower this bar, we propose InterFlow, an AI-powered visual scaffold that helps interviewers manage the interview flow and facilitates real-time data sensemaking. The system dynamically adapts the interview script to the ongoing conversation and provides a visual timer to track interview progress and conversational balance. It further supports information capture with three levels of automation: manual entry, AI-assisted summary with user-specified focus, and a co-interview agent that proactively surfaces potential follow-up points. A within-subject user study ($N=12$) indicates that InterFlow reduces interviewers' cognitive load and facilitates the interview process. Based on the user study findings, we provide design implications for unobtrusive and agency-preserving AI assistance under time-sensitive and cognitively-demanding situations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
