The Role of Implicit and Explicit Demographic Signals in Large Language Model-based Student Assessment

2026-09-15

AI Summary

研究探讨了学生人口统计信息对大型语言模型评估的影响，通过显式和隐式方式测试模型在自动评分、形成性反馈等任务中的反应差异及潜在偏见。

Abstract

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.

Problem

• demographic signals
• Large Language Models
• student assessment
• discrimination
• readability

Innovation

• demographic signals
• Large Language Models
• student assessment
• readability adjustment
• unpredictable biases