REC-CBM：面向可信开放评分的基于规则感知的错误修正概念瓶颈模型
REC-CBM: Rubric-Aware Error-Correction Concept Bottleneck Models for Trustworthy Open-Ended Grading
AI总结：
提出REC-CBM模型，通过规则感知概念编码器、序数成对校准目标和潜在概念错误修正模块，解决开放评分中标准概念瓶颈模型无法建模细粒度规则维度、忽略评分序数语义和概念标注不可靠的问题，在提升评分性能的同时保持可解释性。
摘要：
开放评分对于公平和个性化教育至关重要，但人工评分耗时且成本高，凸显了自动化评分系统的必要性。尽管基于神经和大语言模型（LLM）的系统表现出优越性能，但它们通常是黑箱模型，其评分过程和理由难以让教育者验证和信任。概念瓶颈模型（CBM）通过将预测路由到人类可解释的概念，提供透明度的机制保证，成为一种有前景的方法。然而，标准CBM不适用于开放评分：它们没有显式建模细粒度的规则维度，未能充分捕捉评分量表的序数语义，并忽略了人类概念标注中固有的可靠性问题。为解决这些局限，我们提出REC-CBM，一种面向可信开放评分的规则感知错误修正概念瓶颈模型。REC-CBM引入了规则感知概念编码器，学习针对回答的概念特定表示，以及一个序数成对校准目标，保留规则维度间的排序结构。它还结合了一个潜在概念错误修正模块，在最终评分预测前对概念预测进行去噪，同时保持可解释性。在公开数据集上的全面实验表明，REC-CBM在评分性能上持续提升，并产生比最先进基线更忠实的概念级推理。进一步分析验证了每个组件的贡献，并展示了在真实教育环境中的适用性。总体而言，这项工作提供了一种实用、可解释的评分解决方案，使教育者能够检查、干预和信任自动化决策，推动更透明和可信的教育。
英文摘要：
Open-ended grading is central to equitable and personalized education, yet manual grading remains time-consuming and costly, underscoring the need for automated grading systems. Although recent neural and large language model (LLM) based systems have demonstrated superior performance, they are typically black-box models whose scoring processes and rationales are difficult for educators to verify and trust. Concept bottleneck models (CBMs) have emerged as a promising approach by routing predictions through human-interpretable concepts, providing a mechanistic guarantee of transparency. However, standard CBMs are not tailored to open-ended grading: they do not explicitly model fine-grained rubric dimensions, inadequately capture the ordinal semantics of scoring scales, and neglect inherent reliability issues in human concept annotations. To address these limitations, we propose REC-CBM, a rubric-aware error-correction concept bottleneck model for trustworthy open-ended grading. REC-CBM introduces a rubric-aware concept encoder that learns concept-specific representations over responses and an ordinal pairwise calibration objective that preserves ranking structure among rubric dimensions. It further incorporates a latent concept error-correction module that denoises concept predictions before final grade prediction while preserving interpretability. Comprehensive experiments on publicly available datasets show that REC-CBM consistently improves grading performance and produces more faithful concept-level reasoning than both state-of-the-art baselines. Further analyses validate the contribution of each component and demonstrate the applicability in realistic educational settings. Overall, this work provides a practical, interpretable grading solution that enables educators to inspect, intervene in, and trust automated decisions, advancing more transparent and trustworthy education.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
