How should AI knowledge be governed? Epistemic authority, structural transparency, and the case for open cognitive graphs

TL;DR

This paper proposes a structural governance framework for educational AI, emphasizing epistemic authority, transparency, and community validation through open cognitive graphs and layered governance models.

Contribution

It introduces the Open Cognitive Graph as a technical interface for transparent pedagogical reasoning and the trunk-branch governance model for managing epistemic authority.

Findings

OCGs make AI pedagogical logic inspectable and revisable

Community governance can effectively validate and correct AI educational models

The framework promotes educational equity and democratic accountability

Abstract

Through widespread use in formative assessment and self-directed learning, educational AI systems exercise de facto epistemic authority. Unlike human educators, however, these systems are not embedded in institutional mechanisms of accountability, review, and correction, creating a structural governance challenge that cannot be resolved through application-level regulation or model transparency alone. This paper reconceptualizes educational AI as public educational cognitive infrastructure and argues that its governance must address the epistemic authority such systems exert. We propose the Open Cognitive Graph (OCG) as a technical interface that externalizes pedagogical structure in forms aligned with human educational reasoning. By explicitly representing concepts, prerequisite relations, misconceptions, and scaffolding, OCGs make the cognitive logic governing AI behaviour inspectable and revisable. Building on this foundation, we introduce the trunk-branch governance model, which organizes epistemic authority across layers of consensus and pluralism. A case study of a community-governed educational foundation model demonstrates how distributed expertise can be integrated through institutionalized processes of validation, correction, and propagation. The paper concludes by discussing implications for educational equity, AI policy, and sustainability. By shifting attention from access to governance conditions, the proposed framework offers a structural approach to aligning educational AI with democratic accountability and public responsibility.