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English(EN) The Easy Trap: Why LLMs Underestimate Misconception-Driven Difficulty

大型语言模型难以准确评估项目难度，低估学习者的复杂挑战

作者 PulseAugur 编辑部 · [2 个来源] ·

近期研究表明，虽然大型语言模型（LLMs）可以以中等准确度预测项目难度级别，但它们在识别真正困难的项目时却力不从心。研究发现，LLMs 倾向于低估因误解而对学习者构成挑战的项目难度，这种现象被称为“简单陷阱”。这表明当前的 LLMs 可能在近似课程难度而非认知难度，从而在教育评估和自适应系统中带来偏见的风险。 AI

影响 由于无法准确评估学习者驱动的难度，大型语言模型可能会在教育评估中引入偏见。

排序理由 arXiv 上发表的两篇关于大型语言模型在教育评估中表现的学术论文。

在 arXiv cs.AI 阅读 →

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大型语言模型难以准确评估项目难度，低估学习者的复杂挑战

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arXiv 上发表的两篇关于大型语言模型在教育评估中表现的学术论文。
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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Xinyi Wang, Hong Jiao, Ming Li, Sydney Peters, Hanna Choi, Tianyi Zhou, Qingshu Xu ·

    大型语言模型（LLM）真的能理解项目难度级别吗？这对使用LLM进行自动化项目生成有何启示

    arXiv:2607.28634v1 Announce Type: new Abstract: The estimation of item difficulty plays a key role in both formative assessment and large-scale high-stakes summative assessments. This study explores how large language models (LLMs) perform in predicting item difficulty levels usi…

  2. arXiv cs.AI TIER_1 English(EN) · Amanda La Hadi, Muhammad Johan Alibasa, Guanliang Chen, A. Taufiq Asyhari ·

    简单的陷阱：为什么大型语言模型会低估由误解驱动的难度

    arXiv:2607.26067v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for estimating item difficulty in educational assessment. However, it remains unclear whether such estimates reflect how learners actually experience difficulty. This study invest…

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