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：本文提出了一种统一的学习情境框架，旨在推动基于人工智能的教育从无视情境的模仿转向对学习者原则性、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，我们概述了将学习情境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，我们将使各类人工智能工具能够基于持久情境进行“热启动”，并实现持续、长期的个性化。最后，我们通过OpenStax数字学习平台生态系统和SafeInsights研发基础设施详细阐述了具体的学习情境实施策略。借助OpenStax的全国覆盖范围，我们正在将学习情境嵌入真实教育场景，为数百万学习者提供支持。所有研究和教学干预均在SafeInsights隐私保护数据安全区内进行，确保在降低全国教育公平差距的同时，以隐私优先的实施方式维持高伦理标准。