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.

翻译：人工智能增强课堂在评分结果可用之前便生成丰富的师生反馈信号，然而这些信号难以转化为及时的教学决策。我们提出一种可解释的决策层：该透明机制无需依赖成绩或事后结果标签，即可对需要关注的课程主题进行排序。该方法融合三种信号：学生普遍面临的学习难度、学习者自我报告与观测困难之间的不一致性，以及教师未解决的关切。输出结果为按优先级排序的主题集合，并附有解释各排序依据的逐主题决策记录。在一门研究生计算机科学课程（包含5次教师访谈与279份学生问卷）中，优先级排序主题与教师关注点高度吻合（前5项重合度3/5；斯皮尔曼ρ=0.80），且与学生自报主题难度相关（ρ=0.46，p=0.048）。多信号整合还能发现仅凭单一信号源无法识别的学习者（AUC=0.96，而仅凭差距流行率为0.91）。反思性思维、求助行为与自我效能感提供了额外证据，表明学生行为信号与学习相关构念一致。尽管本研究尚属初步，但结果表明：在反馈不完整时，透明协调机制或可支撑人机协同决策。