研究人与AI如何建立认知关系，揭示五种互动模式。
Classifying Epistemic Relationships in Human-AI Interaction: An Exploratory Approach
- 通过31位学者访谈，提炼出五类人机认知关系。
- 发现用户对AI的信任与角色认知随场景动态变化。
- 适合关注AI协作机制的教育与人机交互研究者。
随着人工智能在知识密集型工作中的广泛应用，其认知角色问题日益凸显。现有人机交互研究虽提出多种AI角色分类，却常忽视AI如何重塑用户作为知识贡献者的身份。本研究基于跨学科31位学者的访谈，构建了五部分编码手册，识别出五种人机认知关系：工具依赖、条件性授权、共代理协作、权威替代和认知回避。这些关系反映了信任程度、评估方式、任务类型及人类认知地位的差异。研究发现，认知角色具有动态性和情境依赖性。本文主张超越静态的AI比喻，建立更精细的框架，以捕捉人与AI共同建构知识的过程，深化人机交互领域对AI使用中关系性与规范性的理解。
原文摘要 · Abstract (English)
As AI systems become integral to knowledge-intensive work, questions arise not only about their functionality but also their epistemic roles in human-AI interaction. While HCI research has proposed various AI role typologies, it often overlooks how AI reshapes users' roles as knowledge contributors. This study examines how users form epistemic relationships with AI-how they assess, trust, and collaborate with it in research and teaching contexts. Based on 31 interviews with academics across disciplines, we developed a five-part codebook and identified five relationship types: Instrumental Reliance, Contingent Delegation, Co-agency Collaboration, Authority Displacement, and Epistemic Abstention. These reflect variations in trust, assessment modes, tasks, and human epistemic status. Our findings show that epistemic roles are dynamic and context-dependent. We argue for shifting beyond static metaphors of AI toward a more nuanced framework that captures how humans and AI co-construct knowledge, enriching HCI's understanding of the relational and normative dimensions of AI use.
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