污染的合作：测量LLM辅助学生写作中的性别偏见迁移
Contaminated Collaboration: Measuring Gender Bias Transfer in LLM-Assisted Student Writing
AI总结：
通过实验发现，使用性别偏见的LLM写作助手会显著增加学生职业规划作文中的性别刻板印象，且偏见迁移不对称：女性目标作文的能动性被抑制，男性目标作文受影响较小。
摘要：
LLM中的性别偏见已在模型输出中得到广泛研究，有偏见的提示被证明会放大刻板印象生成。然而，这种偏见是否会传播到使用这些系统的人类所写的文本中，仍未被充分探索。我们研究了LLM写作助手中的性别偏见是否会迁移到学生撰写的职业规划作文中。我们首先验证了性别偏见的提示会诱导LLM生成性别差异化的语言，而中性提示则不会。然后，我们在受控环境中招募参与者（N = 123），在三种条件下为仅性别不同的配对传记档案撰写职业规划作文：无AI辅助、中性LLM辅助或性别偏见LLM辅助。与对照组和中性条件相比，偏见条件下的学生作文产生了显著更大的能动性差距和更多的性别刻板职业建议。我们的结果还揭示了这种偏见迁移是不对称的：女性目标作文中的能动性受到抑制，而男性目标写作基本不受影响。我们的发现凸显了AI辅助写作中偏见传播的风险，呼吁在教育AI工具中进行公平性意识设计。
英文摘要：
Gender bias in LLMs has been studied extensively in model outputs, with biased prompts shown to amplify stereotyped generations. Whether such bias propagates into text produced by humans who use these systems, however, remains underexplored. We investigate whether gender bias in an LLM writing assistant transfers into career plan essays written by students. We first verify that a gender-biased prompt induces gender-differentiated language in LLM-generated essays, while a neutral prompt does not. We then recruited participants (N = 123) in a controlled environment to write career plan essays for paired biographical profiles differing only in gender under three conditions: no AI assistance, neutral LLM assistance, or gender-biased LLM assistance. Students in the biased condition produced essays with a significantly larger agentic gap and more gender-stereotypic occupation suggestions than those in the control and neutral conditions. Our results also reveal that this bias transfer is asymmetric: agency is suppressed in female-target essays while male-target writing remains largely unaffected. Our findings highlight the risk of bias propagation in AI-assisted writing, calling for fairness-aware design in educational AI tools.
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