用AI整合反馈信号，提前发现需要关注的课程知识点和孤立学生。
Surfacing Isolated Learners with Outcome-Independent Mediation of Feedback between Teachers and Students Using AI
- 通过学习难度、自我报告与行为差异、教师关切三信号融合，生成透明排名。
- 在1门研究生课程中，排名与教师关注点重合率达60%，学生难度感知相关性显著。
- 能识别单信号无法发现的学生，适合教育机构优化教学决策。
AI增强的课堂在成绩公布前生成丰富的师生反馈，但这些信号难以转化为及时的教学决策。本文提出一种可解释的决策层：无需成绩或事后标签，仅基于三类信号——学生学习难度普遍性、自我报告与观察到困难的不一致、教师未解决的关切——生成透明的话题优先级排序，并附每项的决策记录。在一门研究生计算机科学课程（5位教师访谈；279份问卷）中，优先话题与教师关注点高度一致（前5名重合3/5，Spearman ρ=0.80），且与学生报告的学习难度相关（ρ=0.46，p=.048）。多信号融合还比单一信号更有效识别出未被发现的学习者（AUC=0.96 vs. 0.91）。反思、求助行为和自我效能感进一步验证了学生行为信号与学习构念的一致性。初步结果表明，透明的协同机制可在反馈不完整时支持人机共治。
原文摘要 · Abstract (English)
AI-augmented classrooms generate rich teacher and student feedback before graded outcomes become available, yet these signals can be difficult to translate into timely instructional decisions. We propose an interpretable decision layer: a transparent mechanism that ranks course topics requiring attention without using grades or post-hoc outcome labels. The approach combines three signals: student learning difficulty prevalence, disagreement between learner self-reports and observed difficulties, and unresolved teacher concerns. The output is a ranked set of topic priorities with per-topic decision records explaining each ranking. In one graduate CS course offering ($n=5$ instructor interviews; $n=279$ survey responses), prioritized topics aligned with instructor concerns (top-5 overlap 3/5; Spearman $ρ=0.80$) and student-reported topic difficulty ($ρ=0.46$, $p=.048$). Multi-signal integration also surfaced learners not identified through individual signal sources alone (AUC $=0.96$ vs. $0.91$ for gap prevalence alone). Reflective thinking, help-seeking, and self-efficacy provided additional evidence that student behavioral signals align with learning-related constructs. While preliminary, these findings suggest that transparent coordination mechanisms may help support human-AI co-agency when feedback is incomplete.
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