arXiv：cs.AI· Eun Jeong Kang (Xianpi), Peter (Xianpi), Duan, Swati Mishra·· 6 小时前AI 评分36
AI 辅助决策中的新手依赖校准：解释与自我评估的作用
Novice Reliance Calibration in AI-Assisted Decision Making: The Role of Explanations and Self-Assessment
AI 导读
一项 110 人参与的临床实体抽取实验发现，在缺乏即时绩效反馈时，AI 解释会让新手用户系统性地滑向过度依赖，而自评任务理解度越高，依赖行为越有选择性。研究提出"依赖校准"这一构念，用于刻画无外部反馈时用户如何动态调整对 AI 的依赖，并为需在此类场景中支持适度依赖的 AI 工具设计提供可操作指南。
正文
Abstract:Artificial Intelligence (AI) tools are widely used to support decision making in tasks and domains where no immediate performance feedback is available. In these settings, users cannot learn to adjust their reliance behavior over time through trial and error. However, little is known about how novice users calibrate reliance on AI when external feedback is unavailable, or whether AI explanations can support calibration in its absence. We introduce reliance calibration as an organizing construct for studying how novice users dynamically adjust reliance behavior, and examine how AI explanations and meta-cognitive self-assessment shape it. Through a between-subjects study with 110 participants completing a clinical entity extraction task with AI assistance and limited performance feedback, we observe that novice users exhibit systematic drift toward over-reliance in the presence of explanations, while higher self-reported task understanding is associated with more selective reliance behavior. These results extend reliance calibration research into human-AI collaboration contexts without real-time performance signals and present actionable guidelines on designing AI tools that must support appropriate reliance in these settings.
| Comments: | under review |
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.07800 [cs.HC] |
| (or arXiv:2610.07800v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07800 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Eun Jeong Kang [view email]
[v1]
Tue, 6 Oct 2026 05:48:16 UTC (1,435 KB)
来源：arXiv：cs.AI · arxiv.org