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English(EN) Beyond Response Scoring: An AI-aware Cognitive Supervision Framework for Automated Mathematical Short Answer Grading

新AI框架通过检测AI辅助来增强数学评分

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了AICG，一个新颖的自动数学简答评分框架，解决了当前基于AI的系统的局限性。AICG采用双分支多任务方法，一个分支使用RoBERTa专注于单个答案评分，另一个分支通过Q矩阵将响应与评估的技能关联起来，以预测掌握程度和学习增益。一项关键创新是集成了一个生成式AI检测器，该检测器估算辅助概率，并将其转换为可靠性权重，以减轻学习评估期间不可靠的认知证据。在一个大型数据集上的实验表明，与标准的RoBERTa回归相比，AICG显著降低了平均绝对误差并提高了评分准确性。 AI

影响 该框架可以提高自动评分系统的准确性和公平性，尤其是在数学等AI生成内容普遍存在的学科中。

排序理由 详细介绍新的自动评分AI框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新AI框架通过检测AI辅助来增强数学评分

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详细介绍新的自动评分AI框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Shengran Guo, Min Chi, Feifei Qiao, Varad Patwardhan ·

    Beyond Response Scoring: An AI-aware Cognitive Supervision Framework for Automated Mathematical Short Answer Grading

    arXiv:2610.41215v1 Announce Type: new Abstract: Transformer-based language models have achieved remarkable success in automated short answer grading (ASAG). However, current methods in ASAG struggle to comprehend, reason about and evaluate complex mathematical answers involving s…

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