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

TL;DR

InterFlow is an AI-powered system designed to assist interviewers during semi-structured interviews by managing flow, supporting data capture, and reducing cognitive load, thereby improving interview quality and efficiency.

Contribution

This paper introduces InterFlow, a novel unobtrusive AI system that dynamically adapts interview scripts and supports real-time data sensemaking in semi-structured interviews.

Findings

InterFlow reduces interviewers' cognitive load.

InterFlow facilitates more effective interview management.

User study shows positive impact on interview quality.

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.