Classifying Epistemic Relationships in Human-AI Interaction: An Exploratory Approach
This work addresses the problem of overlooked human epistemic roles in AI interaction for HCI researchers, offering an incremental exploratory framework.
This study tackled the problem of how users form epistemic relationships with AI in knowledge-intensive work, identifying five dynamic relationship types based on interviews with 31 academics. The result provides a nuanced framework that captures how humans and AI co-construct knowledge, enriching HCI understanding.
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.