We introduce a unified Learning Context (LC) framework designed to transition AI-based education from context-blind mimicry to a principled, holistic understanding of the learner. This white paper provides a multidisciplinary roadmap for making teaching and learning systems context-aware by encoding cognitive, affective, and sociocultural factors over the short, medium, and long term. To realize this vision, we outline concrete steps to operationalize LC theory into an interoperable computational data structure. By leveraging the Model Context Protocol (MCP), we will enable a wide range of AI tools to "warm-start" with durable context and achieve continual, long-term personalization. Finally, we detail our particular LC implementation strategy through the OpenStax digital learning platform ecosystem and SafeInsights R&D infrastructure. Using OpenStax's national reach, we are embedding the LC into authentic educational settings to support millions of learners. All research and pedagogical interventions are conducted within SafeInsights' privacy-preserving data enclaves, ensuring a privacy-first implementation that maintains high ethical standards while reducing equity gaps nationwide.

翻译：本文提出一个统一的学习情境（LC）框架，旨在将基于人工智能的教育从无视情境的模仿转变为对学习者系统化、整体性的理解。本白皮书提供了一条多学科路线图，通过编码短期、中期和长期认知、情感与社会文化因素，使教学系统具备情境感知能力。为实现这一愿景，我们概述了将LC理论转化为可互操作计算数据结构的具体步骤。通过利用模型情境协议（MCP），我们将使各类AI工具能够借助持久化情境实现"热启动"，并达成持续、长期的个性化教学。最后，我们通过OpenStax数字学习平台生态系统和SafeInsights研发基础设施详述了具体的LC实施策略。借助OpenStax的全国覆盖网络，我们将LC嵌入真实教育场景以服务数百万学习者。所有研究和教学干预均在SafeInsights隐私保护数据安全区内进行，确保隐私优先的实施原则，在维护高标准伦理规范的同时缩小全国范围内的教育公平差距。