InterFlow: Designing Unobtrusive AI to Empower Interviewers in Semi-Structured Interviews
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915825405919232 |
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| author | Wen, Yi Zhang, Yu Suresh, Sriram Lu, Zhicong Liu, Can Xia, Meng |
| author_facet | Wen, Yi Zhang, Yu Suresh, Sriram Lu, Zhicong Liu, Can Xia, Meng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06396 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | InterFlow: Designing Unobtrusive AI to Empower Interviewers in Semi-Structured Interviews Wen, Yi Zhang, Yu Suresh, Sriram Lu, Zhicong Liu, Can Xia, Meng Human-Computer Interaction 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. |
| title | InterFlow: Designing Unobtrusive AI to Empower Interviewers in Semi-Structured Interviews |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2602.06396 |