For four decades, AIED research has rested on what we term the Sedentary Assumption: the unexamined design commitment to a stationary learner seated before a screen. Mobile learning and museum guides have moved learners into physical space, and context-aware systems have delivered location-triggered content -- yet these efforts predominantly cast AI in the role of information-de-livery tool rather than epistemic partner. We map this gap through a 2 x 2 matrix (AI Role x Learning Environment) and identify an undertheorized intersection: the configuration in which AI serves as an epistemic teammate during unstruc-tured, place-bound field inquiry and learning is assessed through trajectory rather than product. To fill it, we propose Field Atlas, a framework grounded in embod-ied, embedded, enactive, and extended (4E) cognition, active inference, and dual coding theory that shifts AIED's guiding metaphor from instruction to sensemak-ing. The architecture pairs volitional photography with immediate voice reflec-tion, constrains AI to Socratic provocation rather than answer delivery, and ap-plies Epistemic Trajectory Modeling (ETM) to represent field learning as a con-tinuous trajectory through conjoined physical-epistemic space. We demonstrate the framework through a museum scenario and argue that the resulting trajecto-ries -- bound to a specific body, place, and time -- constitute process-based evi-dence structurally resistant to AI fabrication, offering a new assessment paradigm and reorienting AIED toward embodied, dialogic human-AI sensemaking in the wild.

翻译：四十年来，人工智能教育研究一直基于我们所谓的"静态假设"：即未经审视地将学习者设计为固定于屏幕前的静止个体。移动学习与博物馆导览系统虽将学习者带入物理空间，情境感知系统也能提供位置触发的内容——但这些努力主要将人工智能定位为信息传递工具，而非认知伙伴。我们通过一个2×2矩阵（人工智能角色×学习环境）描绘这一空白，并识别出一个理论化不足的交叉点：即人工智能在非结构化、场所限定的实地探究中作为认知队友，且学习通过过程轨迹而非成果进行评估的配置。为填补这一空白，我们提出Field Atlas框架，该框架基于具身、嵌入、生成与延展认知理论、主动推理及双重编码理论，将人工智能教育的核心隐喻从"教学"转向"意义建构"。该架构将自主摄影与即时语音反思相结合，将人工智能限定于苏格拉底式启发而非答案提供，并应用认知轨迹建模（ETM）将实地学习表征为贯穿物理-认知耦合空间的连续轨迹。我们通过博物馆场景演示该框架，并论证由此生成的轨迹——绑定于特定身体、场所与时间——构成了在结构上能抵御人工智能伪造的过程性证据，从而提供新的评估范式，并将人工智能教育重新导向具身化、对话式的人机协同意义建构实践。