InterPilot: Exploring the Design Space of AI-assisted Job Interview Support for HR Professionals

2026-02-24

AI Summary

This study addresses the high cognitive load faced by human resource (HR) professionals during recruitment interviews, where they must simultaneously listen, evaluate, take notes, and ask follow-up questions. It presents the first systematic investigation into HR practitioners’ design requirements for AI assistance, proposing a collaborative framework that balances assistance granularity, attention allocation, and human agency. By integrating intelligent note-taking, real-time skill evidence mapping, adaptive question generation, and interactive visualizations, the system significantly reduces documentation burden without increasing overall workload. The research further uncovers critical usability trade-offs—such as visual attention dispersion and interaction complexity—offering a novel paradigm for human-AI collaboration in high-cognitive-load scenarios.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Recruitment interviews are cognitively demanding interactions in which interviewers must simultaneously listen, evaluate candidates, take notes, and formulate follow-up questions. To better understand these challenges, we conducted a formative study with eight HR professionals, from which we derived key design goals for real-time AI support. Guided by these insights, we developed InterPilot, a prototype system that augments interviews through intelligent note-taking and post-interview summary, adaptive question generation, and real-time skill-evidence mapping. We evaluated the system with another seven HR professionals in mock interviews using a within-subjects design. Results show that InterPilot reduced documentation burden without increasing overall workload, but introduced usability trade-offs related to visual attention and interaction complexity. Qualitative findings further reveal tensions around trust and verification when AI suggests highly specific technical questions. We discuss implications for designing future real-time human-AI collaboration in professional settings, highlighting the need to balance assistance granularity, attentional demands, and human agency.

Problem

• job interview
• cognitive load
• HR professionals
• real-time support
• human-AI collaboration

Innovation

• AI-assisted interviewing
• real-time human-AI collaboration
• adaptive question generation
• skill-evidence mapping
• intelligent note-taking