InterPilot: Exploring the Design Space of AI-assisted Job Interview Support for HR Professionals
Zhengtao Xu, Zimo Xia, Zicheng Zhu, Nattapat Boonprakong, Yu-An Chen, Rabih Zbib, Casimiro Pio Carrino, Yi-Chieh Lee

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…
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Taxonomy
TopicsEthics and Social Impacts of AI · Artificial Intelligence in Healthcare and Education · Explainable Artificial Intelligence (XAI)
