Cognitive-Uncertainty Guided Knowledge Distillation for Accurate Classification of Student Misconceptions

ArXi:2605.14752v1 Announce Type: new Accurately identifying student misconceptions is crucial for personalized education but faces three challenges: (1) data scarcity with long-tail distribution, where authentic student reasoning is difficult to synthesize; (2) fuzzy boundaries between error categories with high annotation noise; (3) deployment parado-large models overlook unconventional approaches due to pre