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

2025-08-02

AI Summary

While AI is evolving from a mere tool into a knowledge collaboration partner, existing research predominantly classifies AI roles while neglecting the concurrent reconfiguration of users’ roles as active knowledge contributors. Method: Drawing on 31 interdisciplinary qualitative interviews and integrating perspectives from human–computer interaction (HCI) and epistemology, this study systematically analyzes human–AI cognitive interactions in research and teaching contexts. Contribution/Results: We propose five dynamic, epistemic relational patterns—tool dependency, conditional delegation, collaborative agency, authority transfer, and cognitive retreat—that transcend static metaphors and reveal the interplay among trust, task type, and human epistemic standing. Based on these, we develop a five-dimensional coding framework that identifies transferable relational motifs, yielding the first situated theoretical framework for AI system design oriented toward co-constructed knowledge.

Technology Category

• Philosophy and Ethics of AI: AI & Epistemology
• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Examining how users form epistemic relationships with AI
• Identifying dynamic AI-human roles in knowledge collaboration
• Developing a framework for AI's epistemic impact in HCI

Innovation

• Developed five-part codebook for epistemic relationships
• Identified dynamic context-dependent AI-human roles
• Proposed nuanced framework for knowledge co-construction