Generative AI User Experience: Developing Human--AI Epistemic Partnership

TL;DR

This paper introduces the Human--AI Epistemic Partnership Theory (HAEPT), explaining the user experience of generative AI as a dynamic negotiation of epistemic, agency, and accountability contracts, advancing understanding beyond traditional adoption models.

Contribution

It develops the HAEPT framework to explain GenAI user experience as a form of epistemic partnership, addressing gaps in existing theories and analyzing collaborative learning scenarios.

Findings

Trust and skepticism coexist as users calibrate their relationship with AI.

GenAI experiences involve negotiated contracts affecting user interactions.

Application of HAEPT to collaborative learning demonstrates different partnership configurations.

Abstract

Generative AI (GenAI) has rapidly entered education, yet its user experience is often explained through adoption-oriented constructs such as usefulness, ease of use, and engagement. We argue that these constructs are no longer sufficient because systems such as ChatGPT do not merely support learning tasks but also participate in knowledge construction. Existing theories cannot explain why GenAI frequently produces experiences characterized by negotiated authority, redistributed cognition, and accountability tension. To address this gap, this paper develops the Human--AI Epistemic Partnership Theory (HAEPT), explaining the GenAI user experience as a form of epistemic partnership that features a dynamic negotiation of three interlocking contracts: epistemic, agency, and accountability. We argue that findings on trust, over-reliance, academic integrity, teacher caution, and relational interaction about GenAI can be reinterpreted as tensions within these contracts rather than as isolated issues. Instead of holding a single, stable view of GenAI, users adjust how they relate to it over time through calibration cycles. These repeated interactions account for why trust and skepticism often coexist and for how partnership modes describe recurrent configurations of human--AI collaboration across tasks. To demonstrate the usefulness of HAEPT, we applied it to analyze the UX of collaborative learning with AI speakers and AI-facilitated scientific argumentation, illustrating different contract configurations.