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

TL;DR

This paper presents InterPilot, an AI-assisted system designed to support HR professionals during interviews by improving note-taking, question generation, and evidence mapping, based on formative and evaluative studies.

Contribution

The paper introduces InterPilot, a novel AI support system for interviews, with insights from user studies guiding its design and evaluation in realistic scenarios.

Findings

InterPilot reduces documentation effort for HR professionals.

Usability trade-offs include increased visual attention demands.

Trust issues arise with AI-suggested technical questions.

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.