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.

翻译：通过在教育性评估与自主学习中的广泛应用，教育人工智能系统实际上行使着认知权威。然而，与人类教育者不同，这些系统并未嵌入问责、审查与修正的制度机制中，从而产生了一种结构性治理挑战，仅靠应用层监管或模型透明度无法解决。本文将教育人工智能重新定义为公共教育认知基础设施，并主张其治理必须应对此类系统所施加的认知权威。我们提出开放认知图谱作为技术接口，以符合人类教育推理的形式外化教学结构。通过显式表征概念、先决关系、错误认知与教学支架，开放认知图谱使得支配人工智能行为的认知逻辑可被检视与修正。在此基础上，我们引入主干-分支治理模型，该模型在共识与多元化的不同层级间组织认知权威。一项关于社区治理教育基础模型的案例研究表明，分布式专业知识如何通过制度化验证、修正与传播流程实现整合。本文最后探讨了对教育公平、人工智能政策与可持续性的启示。通过将关注点从获取转向治理条件，所提出的框架为教育人工智能与民主问责及公共责任的协调提供了一种结构性路径。