Classifying Epistemic Relationships in Human-AI Interaction: An Exploratory Approach

• URL: http://arxiv.org/abs/2508.03673v1
• Date: Sat, 02 Aug 2025 23:41:28 GMT
• Title: Classifying Epistemic Relationships in Human-AI Interaction: An Exploratory Approach
• Authors: Shengnan Yang, Rongqian Ma,
• Abstract summary: This study examines how users form relationships with AI-how they assess, trust, and collaborate with it in research and teaching contexts.<n>Based on 31 interviews with academics across disciplines, we developed a five-part codebook and identified five relationship types.
• Score: 0.6906005491572401
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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