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.
Figures & tables
Contract
Traditional Educational Technology
GenAI
Epistemic Contract (What counts as knowledge? What to trust?)
Knowledge is pre-curated, stable, and institutionally validated (e.g., textbooks, LMS content, simulations). Trust is largely delegated to external authorities such as teachers, publishers, and curriculum designers. Uncertainty is minimized and bounded.
Knowledge is dynamically generated, probabilistic, and potentially fallible. Trust must be actively evaluated by the user. Outputs are fluent but may be inaccurate, requiring verification and critical evaluation. Risks include automation bias and treating AI as a surrogate knower.
Agency Contract (Who does the thinking?)
Cognitive roles are clearly structured: learners engage in tasks designed by instructors, while technology supports delivery, practice, or visualization. Thinking remains primarily human-driven, with tools scaffolding specific processes.
Cognitive labor is fluid and negotiable. AI can generate ideas, evaluate responses, and guide inquiry, potentially taking over core thinking processes. Learning depends on whether users outsource cognition or use AI as a partner for reflection and critique. Risks arise when agency shifts too heavily to AI.
Accountability Contract (Who is responsible?)
Authorship and responsibility are relatively clear and human-centered. Outputs are typically produced by students, with tools playing a transparent, supportive role. Institutional norms for assessment and integrity are well established.
Authorship becomes ambiguous and distributed between human and AI. New norms are required for disclosure, credit, and ethical use. Responsibility for errors, bias, or misconduct is contested and context-dependent, raising concerns about fairness, transparency, and academic integrity.
Table 1. Comparison between traditional Educational Technology and GenAI across three contracts
Figure 1. Flow chart of the CLAIS-Jigsaw system (adapted from Lee and Zhai, 2025).
Contract
CLAIS (AI Speaker in Collaborative Learning)
ArgueAgent (GenAI for Scientific Argumentation)
Key Difference
Epistemic
Stabilized, high-trust knowledge environment. AI provides reliable, pre-curated explanations. User experience: low epistemic friction, smooth and confident use. Risk: over-trust and reduced critical evaluation; AI may become a surrogate knower.
Destabilized, contestable knowledge environment. AI provokes critique and conflicting ideas. User experience: epistemic friction through continuous justification and revision. Risk: overreliance on AI critique; epistemic standards may shift externally.
CLAIS stabilizes knowledge and trust, while ArgueAgent unsettles knowledge to promote critique.
Agency
Structured co-agency. Humans lead explanation and integration; AI supports participation. User experience: efficient, smoother interaction. Risk: cognitive offloading and reduced generative thinking.
AI-orchestrated co-agency. AI structures pairing and discourse; humans argue within it. User experience: guided engagement and sustained high interaction. Risk: process dependency and weakened self-regulation.
CLAIS distributes cognition with human leadership; ArgueAgent places process control partly in AI.
Accountability
Implicit and underspecified. AI is treated as a peer but not clearly accountable. User experience: normative ambiguity. Risk: diffuse authorship and weak ownership of knowledge claims.
Emergent but hidden governance. AI enforces participation and shapes discourse. User experience: distributed accountability with hidden control. Risk: blurred epistemic ownership and responsibility for process.
CLAIS lacks clear accountability structures; ArgueAgent embeds accountability but obscures who governs it.
Overall Partnership Mode
Structured co-agency with epistemic stabilization and weak accountability.
AI-orchestrated epistemic engagement with productive tension but latent dependency.
CLAIS emphasizes stability and support; ArgueAgent emphasizes tension and co-construction.
The encounter between human reasoning and generative artificial intelligence (GenAI) cannot be adequately described by inherited metaphors of tool use, augmentation, or collaborative partnership. This article argues that such interactions produce a qualitatively distinct cognitive-epistemic formation, designated here as the Third Entity: an emergent, transient structure that arises from the transductive coupling of two ontologically incommensurable modes of cognition. Drawing on Peirce semiotics, Polanyi theory of tacit knowledge, Simondon philosophy of individuation, Ihde postphenomenology, and Morin complexity theory, we develop a multi-layered theoretical account of this formation. We introduce the concept of vibe-creation to designate the pre-reflective cognitive mode through which the Third Entity navigates high-dimensional semantic space and argue that this mode constitutes the automation of tacit knowledge - a development with far-reaching consequences for epistemology, the philosophy of mind, and educational theory. We further propose the notion of asymmetric emergence to characterize the agency of the Third Entity: genuinely novel and irreducible, yet anchored in human intentional responsibility. The article concludes by examining the implications of this theoretical framework for the transformation of educational institutions and the redefinition of intellectual competence in the age of GenAI.
Generative AI systems have entered everyday academic, professional, and personal life with remarkable speed, yet most users encounter them as mysterious artifacts rather than intelligible systems. This chapter discusses large language models within a broader historical shift in computing paradigms and argues that many of the confusions surrounding their use arise from a mismatch between how these systems are built, how they behave, and how people expect computers to behave writ large. Rather than treating generative AI as a monolithic technology, the chapter decomposes it into interacting components, spanning data, models, product features, and user inputs, each introducing distinct affordances and tensions. Particular attention is given to the statistical and data-based foundations of these systems and to the fact that their surface behavior is explicitly human-like, a combination that places them squarely within the intellectual traditions of educational and behavioral research. From this perspective, educational researchers are unusually well positioned to study, evaluate, and productively use generative AI systems, drawing on established methods for modeling latent processes, managing uncertainty, and interpreting complex human-system interactions. The goal is to equip readers with a conceptual map that supports more informed experimentation, critical interpretation, and responsible use as these systems continue to evolve.
Epistemic thinking plays a central role in students' learning processes when applying generative artificial intelligence (GenAI), particularly in programming contexts where learners must construct queries, evaluate and validate AI-generated outputs, and regulate problem-solving strategies. This study introduces the conceptual framework of Epistemic AI Literacy (EAIL), reframing AI literacy as a process-oriented epistemic phenomenon that emerges through dynamic human-AI interactions across different domains. Drawing on the AIR (epistemic aims, ideals and reliable epistemic processes) framework, this study examines how epistemic aims and epistemic processes are enacted in GenAI-supported co-programming activities and explores scalable approaches for operationalizing these constructs in interaction data. Using a large dialogue dataset of human-AI co-programming, this study identifies observable dimensions of epistemic aims (i.e., mastery-oriented aims) and epistemic processes (i.e., outsourcing, explanation seeking, verification seeking, prompt monitoring, and epistemic justification). The results reveal a prevalent lack of EAIL, with 78.8% of student-GenAI interactions relying on non-mastery-oriented aims and less reliable epistemic strategies like outsourcing and verification-seeking. Conversely, only 11.1% of interactions showed high epistemic engagement, where mastery-oriented aims were coupled with advanced epistemic strategies like epistemic justification in a more reliable epistemic process.