Classifying Epistemic Relationships in Human-AI Interaction: An Exploratory Approach
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.
Cite
@article{arxiv.2508.03673,
title = {Classifying Epistemic Relationships in Human-AI Interaction: An Exploratory Approach},
author = {Shengnan Yang and Rongqian Ma},
journal= {arXiv preprint arXiv:2508.03673},
year = {2025}
}