Evaluating Learner Representations for Differentiation Prior to Instructional Outcomes

ArXi:2604.05848v1 Announce Type: cross Learner representations play a central role in educational AI systems, yet it is often unclear whether they preserve meaningful differences between students when instructional outcomes are unavailable or highly context-dependent. This work examines how to evaluate learner representations based on whether they retain separation between learners under a shared comparison rule. We