The Empty Quadrant: AI Teammates for Embodied Field Learning
This work is significant for AIED researchers and practitioners, as it proposes a new paradigm for AI-human interaction in mobile, unstructured learning environments, offering a novel assessment method resistant to AI fabrication.
This paper addresses the gap in AI in Education (AIED) research concerning AI teammates for embodied field learning, where learners are mobile and AI acts as an epistemic partner. They propose Field Atlas, a framework that shifts the AIED metaphor from instruction to sensemaking, using volitional photography, voice reflection, Socratic AI provocation, and Epistemic Trajectory Modeling to represent learning as a continuous trajectory in physical-epistemic space.
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.