arXiv:2606.15914v1 Announce Type: new Abstract: 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 contr
The proliferation of LLMs in educational and professional settings necessitates a deeper understanding of their subtle, yet pervasive, societal impacts, especially concerning biases.
This research provides empirical evidence that LLM biases can transfer to human users, potentially amplifying and entrenching societal stereotypes through widespread AI-assisted communication.
The understanding of AI bias expands from merely model outputs to human-produced text, highlighting a new vector for bias propagation that requires mitigation strategies in system design and educational policy.
- · AI ethicists
- · Educational technology providers focused on bias detection
- · Organizations promoting AI literacy
- · Developers ignoring human-AI interaction biases
- · Users unaware of LLM bias transfer
- · Educational institutions without bias-mitigation policies
Increased awareness among users and developers about the potential for LLM-assisted bias transfer in writing.
Development of new features in LLMs and writing tools specifically designed to detect and mitigate transferred biases.
Formal integration of AI ethics, focusing on bias transfer, into educational curricula and professional writing standards.
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