InterFlow: Designing Unobtrusive AI to Empower Interviewers in Semi-Structured Interviews

Yi Wen; Yu Zhang; Sriram Suresh; Zhicong Lu; Can Liu; Meng Xia

doi:10.1145/3772318.3790866

InterFlow: Designing Unobtrusive AI to Empower Interviewers in Semi-Structured Interviews

Yi Wen
, Yu Zhang
, Sriram Suresh
, Zhicong Lu
, Can Liu
, Meng Xia^*

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Downloads (CityUHK Scholars)

Abstract

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. © 2026 Copyright held by the owner/author(s).

Original language	English
Title of host publication	CHI '26
Subtitle of host publication	Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
Publisher	Association for Computing Machinery
Number of pages	21
ISBN (Print)	9798400722783
DOIs	https://doi.org/10.1145/3772318.3790866
Publication status	Published - 2026
Event	2026 ACM CHI Conference on Human Factors in Computing Systems (CHI 2026): Creating Tomorrow Together - Centre de Convencions Internacional de Barcelona, Barcelona, Spain Duration: 13 Apr 2026 → 17 Apr 2026 https://chi2026.acm.org/

Publication series

Name	Conference on Human Factors in Computing Systems - Proceedings

Conference

Conference	2026 ACM CHI Conference on Human Factors in Computing Systems (CHI 2026)
Abbreviated title	CHI'26
Place	Spain
City	Barcelona
Period	13/04/26 → 17/04/26
Internet address	https://chi2026.acm.org/

Bibliographical note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Funding

The first author started this project as her final year project at City University of Hong Kong. We thank Dr. Sangho Suh, Xinyue Chen, and Dr. Chengbo Zheng for their insightful feedback on this project. We also thank Dr. Ping Ma and Dr. Ananya Tiwari for validating and enriching the scheme for suggesting follow-up questions in semi-structured interviews.

Research Keywords

Human-AI Interaction
Semi-structured Interview
Sensemaking

Publisher's Copyright Statement

This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/

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10.1145/3772318.3790866

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Wen, Y., Zhang, Y., Suresh, S., Lu, Z., Liu, C., & Xia, M. (2026). InterFlow: Designing Unobtrusive AI to Empower Interviewers in Semi-Structured Interviews. In CHI '26: Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems Article 1611 (Conference on Human Factors in Computing Systems - Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3772318.3790866

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Wen, Y, Zhang, Y, Suresh, S, Lu, Z , Liu, C & Xia, M 2026, InterFlow: Designing Unobtrusive AI to Empower Interviewers in Semi-Structured Interviews. in CHI '26: Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems., 1611, Conference on Human Factors in Computing Systems - Proceedings, Association for Computing Machinery, 2026 ACM CHI Conference on Human Factors in Computing Systems (CHI 2026), Barcelona, Spain, 13/04/26. https://doi.org/10.1145/3772318.3790866

InterFlow: Designing Unobtrusive AI to Empower Interviewers in Semi-Structured Interviews. / Wen, Yi; Zhang, Yu; Suresh, Sriram et al.
CHI '26: Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, 2026. 1611 (Conference on Human Factors in Computing Systems - Proceedings).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AU - Suresh, Sriram

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AU - Liu, Can

AU - Xia, Meng

N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

PY - 2026

Y1 - 2026

N2 - 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. © 2026 Copyright held by the owner/author(s).

AB - 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. © 2026 Copyright held by the owner/author(s).

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M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9798400722783

T3 - Conference on Human Factors in Computing Systems - Proceedings

BT - CHI '26

PB - Association for Computing Machinery

Y2 - 13 April 2026 through 17 April 2026

ER -

Wen Y, Zhang Y, Suresh S, Lu Z , Liu C, Xia M. InterFlow: Designing Unobtrusive AI to Empower Interviewers in Semi-Structured Interviews. In CHI '26: Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery. 2026. 1611. (Conference on Human Factors in Computing Systems - Proceedings). Epub 2026 Apr 13. doi: 10.1145/3772318.3790866

InterFlow: Designing Unobtrusive AI to Empower Interviewers in Semi-Structured Interviews

Abstract

Publication series

Conference

Bibliographical note

Funding

Research Keywords

Publisher's Copyright Statement

Access Output

Other Access Options

Permanent Link

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