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 introduce distinctiveness, a representation-level measure that evaluates how each learner differs from others in the cohort using pairwise distances, without requiring clustering, labels, or task-specific evaluation. Using student-authored questions collected through a conversational AI agent in an online learning environment, we compare representations based on individual questions with representations that aggregate patterns across a student's interactions over time. Results show that learner-level representations yield higher separation, stronger clustering structure, and more reliable pairwise discrimination than interaction-level representations. These findings demonstrate that learner representations can be evaluated independently of instructional outcomes and provide a practical pre-deployment criterion using distinctiveness as a diagnostic metric for assessing whether a representation supports differentiated modeling or personalization.

翻译：学习者表征在教育人工智能系统中扮演核心角色，然而当教学结果不可获取或高度依赖情境时，这些表征能否保留学生间的有意义差异往往难以确定。本研究探讨如何在共享比较规则下，基于学习者表征能否保持其区分性来评估这些表征。我们引入"区分度"这一表征层面指标，通过成对距离评估每个学习者在群体中的差异性，无需依赖聚类、标签或特定任务评估。基于在线学习环境中通过对话式AI代理收集的学生原创问题，我们比较了基于单个问题的表征与跨时间聚合学生交互模式的表征。结果表明，相较于交互层面表征，学习者层面表征呈现出更高的分离度、更强的聚类结构以及更可靠的成对区分能力。这些发现证实学习者表征可在脱离教学结果的情况下独立评估，并提供了基于区分度这一诊断指标的实际部署前评估标准，用以判断表征是否支持差异化建模或个性化适配。