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

2026-02-06

AI Summary

This study addresses the challenge interviewers face in semi-structured interviews, where high cognitive load impedes their ability to simultaneously listen attentively, adapt interview guides flexibly, and pose effective follow-up questions. To mitigate this, the authors propose InterFlow—a non-intrusive, user-directed AI-assisted system that automates three-tiered information capture through dynamic script adaptation, visual progress tracking, AI-generated summaries, and a collaborative agent. The system also allows users to specify focal points of interest. In a within-subjects experiment with twelve participants, InterFlow significantly reduced cognitive load while enhancing both interview efficiency and user experience, offering a novel paradigm for human-AI collaborative decision-making in high-stakes, time-sensitive scenarios.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Intelligent Robots: Human-Robot Interaction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Semi-structured interviews are a common method in qualitative research. However, conducting high-quality interviews is challenging, as it requires interviewers to actively listen to participants, adapt their plans as the conversation unfolds, and probe effectively. 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.

Problem

• semi-structured interviews
• cognitive load
• interview flow
• real-time sensemaking
• AI assistance

Innovation

• unobtrusive AI
• semi-structured interviews
• real-time sensemaking
• adaptive scripting
• co-interview agent