AI集成与专家表现的评价动态：异质风险下认知依赖的视角

中文摘要 AI 辅助

AI正日益融入专家工作流程，然而，在非专业用户对AI辅助专家存在认知依赖的领域中，集成如何影响对专家、AI及其组合的感知仍不清楚。我们通过一项新颖的受控医学研究（N=166）和一项与先前学术咨询数据（n=157，合并N=323）的直接跨领域分析来考察这一点。专家错误在各领域均降低了对人类专家的评价。然而，感知专业度在高风险医学任务中随AI集成策略而变化，其中自动AI监督产生的评分高于仅专家或专家发起的AI。探索性有序敏感性分析识别出一种绩效依存的复用模式，即在成功的医学表现后，自动监督产生更高的预期复用。总体而言，与绩效相关的重新校准显得相对可移植，而集成结构效应则更具选择性和情境敏感性。这些发现表明，系统设计者应考虑AI如何进入专家工作流程，而不仅仅是其是否存在。

英文摘要

AI is increasingly integrated into expert workflows, yet how integration affects perceptions of the expert, AI, and their combination remains unclear in domains where lay users are epistemically dependent on AI-assisted experts. We examine this through a novel controlled medical study (N = 166) and a direct cross-domain analysis with pre-existing academic-advising data (n = 157, combined N = 323). Expert errors reduced evaluations of the human expert across domains. Perceived expertise, however, varied by AI integration strategy in the higher-stakes medical task, where automatic AI oversight produced higher ratings than expert-only or expert-initiated AI. Exploratory ordinal sensitivity analyses identified a performance-contingent reuse pattern, with automatic oversight producing greater intended reuse after successful medical performance. Overall, performance-related recalibration appeared comparatively portable, while integration-structure effects were more selective and context-sensitive. These findings suggest that system designers should consider how AI enters expert workflows, not only whether it is present.

发表机构

• Colorado State University（科罗拉多州立大学）

机构由 AI 辅助整理，请以论文原文为准。