隐性与显性人口统计信号在大语言模型学生评估中的作用

中文摘要 AI 辅助

大语言模型现已普遍应用于学生评估，但我们对学生人口统计特征如何影响其使用知之甚少。有时，考虑学生人口统计特征可能是必要的——例如，为教育水平较低的用户提高可读性。然而，这也可能成为歧视的根源，例如，对来自较低社会经济背景的学生给予较低的分数。我们设置了受控提示词，以测试1)显性人口统计效应，即直接提及人口统计细节；以及2)隐性效应，即利用对话历史作为人口统计信号。我们在三项任务中测试了这些设置：自动作文评分、形成性反馈和元语言问答。我们在这些任务上测试了六个最先进的大语言模型。在显性和隐性两种情况下，模型都会捕捉人口统计线索，并相应地改变其评分、反馈和答案。我们发现，当明确提及教育水平时，大语言模型经常调整反馈的可读性以适应教育水平。另一方面，隐性条件会产生不可预测的偏差，例如在问答中，来自较低教育水平的回答获得较低的情感分数。我们的结果为教育评估任务中大语言模型的人口统计敏感性提供了明确证据。

英文摘要

Large Language Models are now common in student assessment, but we know little about how student demographics affect their use. Sometimes, considering student demographics may be necessary -- for example, to improve readability for users with lower educational levels. However, it also risks being a cause of discrimination, e.g., when assigning lower scores to students from lower socioeconomic backgrounds. We set up controlled prompts to test 1) explicit demographic effects, where we mention demographic details directly, and 2) implicit effects, where we use conversation history as a demographic signal. We test these settings in three tasks: Automated Essay Scoring, Formative Feedback, and Metalinguistic Question Answering. We test six state-of-the-art LLMs on these tasks. In both explicit and implicit cases, the models pick up on demographic cues and can change their scoring, feedback, and answers accordingly. We find that LLMs frequently adjust the readability of feedback to education levels when these are explicitly mentioned. On the other hand, implicit conditions produce unpredictable biases, such as in question answering, where responses from lower-education levels receive lower sentiment scores. Our results provide clear evidence of demographic sensitivity in LLMs for educational assessment tasks.

发表机构

• Bocconi University（博科尼大学）
• Télécom SudParis（巴黎电信南巴黎）
• Institut Polytechnique de Paris（巴黎综合理工学院）

机构由 AI 辅助整理，请以论文原文为准。

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EMNLP 2026 Findings