The Empty Quadrant: AI Teammates for Embodied Field Learning

Researchers propose Field Atlas, a new AI framework that moves beyond traditional screen-based learning to create AI teammates for embodied field learning in physical spaces. The framework uses Socratic questioning rather than direct answers and tracks learning through continuous trajectories in physical-epistemic space, offering a paradigm shift from instruction-based to sensemaking-based AI education.

• →AIED research has been limited by the 'Sedentary Assumption' of stationary learners at screens for four decades.
• →Field Atlas framework positions AI as an epistemic teammate rather than an information delivery tool for place-based learning.
• →The system uses volitional photography paired with voice reflection and constrains AI to Socratic questioning methods.
• →Epistemic Trajectory Modeling represents learning as continuous movement through combined physical and knowledge spaces.
• →The approach creates process-based evidence that is structurally resistant to AI fabrication due to its embodied nature.