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.

翻译：招聘面试是认知负荷较高的互动过程，面试官需要同时完成倾听、评估候选人、记录笔记及构思后续问题等多重任务。为深入理解这些挑战，我们与八位人力资源专业人员开展了一项形成性研究，从中提炼出实时AI支持的关键设计目标。基于这些发现，我们开发了InterPilot原型系统，该系统通过智能笔记记录与面试后总结、自适应问题生成以及实时技能-证据映射功能来增强面试流程。我们采用被试内设计，邀请另外七位人力资源专业人员在模拟面试中对系统进行评估。结果表明，InterPilot在未增加整体工作负荷的前提下减轻了文档记录负担，但在视觉注意力分配与交互复杂性方面存在可用性权衡。定性研究进一步揭示了当AI建议高度专业的技术问题时引发的信任与验证矛盾。我们探讨了未来专业场景中实时人机协作设计的启示，强调需要平衡辅助粒度、注意力需求与人类自主权之间的关系。