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.

翻译：大型语言模型（LLM）中的性别偏见已在模型输出中得到广泛研究，已有研究表明，包含偏见的提示会加剧刻板内容的生成。然而，这种偏见是否会传播到使用这些系统的人类撰写的文本中，仍尚未得到充分探索。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文章中。我们首先验证了带有性别偏见的提示会促使LLM生成的文章中出现性别差异化语言，而中性提示则不会。随后，我们在受控环境中招募了参与者（N=123），在三种条件下（无人工智能辅助、中性LLM辅助或存在性别偏见的LLM辅助）为仅性别不同的成对传记资料撰写职业规划文章。与对照组和中性条件组相比，处于偏见条件组的学生撰写的文章表现出显著更大的主体性差距，并包含更多性别刻板的职业建议。我们的结果还揭示，这种偏见迁移是不对称的：女性目标文章中的主体性受到抑制，而男性目标写作则基本不受影响。我们的研究结果凸显了人工智能辅助写作中偏见传播的风险，并呼吁在面向教育的AI工具设计中纳入公平性考量。