Zhao et al. (2026) — Arizona State University. arXiv preprint.
REC-CBM: Rubric-Aware Error-Correction Concept Bottleneck Models advances the automated-grading frontier by solving a fundamental trust problem: even accurate AI graders are unusable if educators cannot verify their reasoning. Standard llm-based graders operate as black boxes, while earlier Concept Bottleneck Models (CBMs) offer interpretability but fail at modeling rubric dimensions, ordinal score semantics, and noisy human annotations. REC-CBM introduces three innovations: (1) a rubric-aware concept encoder that learns concept-specific representations aligned with actual grading rubrics, (2) an ordinal pairwise calibration objective that preserves score ordering (e.g., 'poor' < 'fair' < 'good'), and (3) a latent error-correction module that denoises concept predictions while maintaining full interpretability. Experiments demonstrate consistent improvements in both grading accuracy and concept-level reasoning faithfulness over baselines. This work directly addresses assessment-validity concerns raised in genai-assessment-governance and complements automatic-short-answer-grading by adding the interpretability dimension. The rubric-aware design aligns with formative-assessment needs and scaffolding principles, and the error-correction approach resonates with work on ground-truth-reliability-aied.
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