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This study investigates the socio-technical dynamics of LLM adoption among freelance writers, highlighting that peer influence and professional outlook drive usage more than demographic factors. The research underscores critical barriers for multilingual practitioners and reveals that job security anxieties persist regardless of actual AI tool adoption rates. These findings offer essential insights into the human-centric challenges of integrating generative AI into creative professions.
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Voices of Freelance Professional Writers on AI: Limitations, Expectations, and Fears

arXiv2 min read

arXiv:2504.05008v3 Announce Type: replace Abstract: The rapid development of AI-driven tools, particularly large language models (LLMs), is reshaping professional writing. Still, key aspects of their adoption such as language support, ethics, and long-term impact on writers' voice and creativity remain underexplored. In this work, we carried out a questionnaire (N = 301) and an interactive task (N = 36) targeting freelance professional writers regularly using AI. We examined AI-assisted writing practices across 25+ languages, ethical concerns, and user expectations. Our findings reveal that adoption is shaped more by peer influence and professional outlook than demographics, that multilingual writers face both performance and perception barriers to equitable AI use, and that job security concerns are widespread and adoption-independent.

Abstract:The rapid development of AI-driven tools, particularly large language models (LLMs), is reshaping professional writing. Still, key aspects of their adoption such as language support, ethics, and long-term impact on writers' voice and creativity remain underexplored. In this work, we carried out a questionnaire (N = 301) and an interactive task (N = 36) targeting freelance professional writers regularly using AI. We examined AI-assisted writing practices across 25+ languages, ethical concerns, and user expectations. Our findings reveal that adoption is shaped more by peer influence and professional outlook than demographics, that multilingual writers face both performance and perception barriers to equitable AI use, and that job security concerns are widespread and adoption-independent.

Computation and Language (cs.CL); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)

arXiv:2504.05008 [cs.CL]

(or arXiv:2504.05008v3 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2504.05008 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 212 words

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