Surveying Professional Writers on AI: Limitations, Expectations, and Fears

2025-04-07

AI Summary

This study investigates adoption barriers and impacts of AI writing tools—particularly large language models (LLMs)—in multilingual professional writing. Addressing a critical gap, it examines challenges faced by non-English writers through a mixed-methods design: a structured survey (N=301) with global professional writers across 25+ languages, in-depth interactive writing tasks (N=36), and cross-lingual textual analysis with thematic modeling. The study provides the first empirical evidence identifying key adoption impediments—including uneven linguistic support, weak domain adaptation, and stylistic homogenization risks—and proposes two novel evaluation dimensions: *stylistic adaptability* and *misinformation controllability*. It further identifies high-priority functional requirements: factual verification, register control, and iterative human-AI collaboration. Collectively, these findings offer empirically grounded guidance for developing ethically responsible, linguistically inclusive, and creator-centered LLM writing tools.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: AI for Accessibility
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

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 languages support, ethics, and long-term impact on writers voice and creativity remain underexplored. In this work, we conducted a questionnaire (N = 301) and an interactive survey (N = 36) targeting professional writers regularly using AI. We examined LLM-assisted writing practices across 25+ languages, ethical concerns, and user expectations. The findings of the survey demonstrate important insights, reflecting upon the importance of: LLMs adoption for non-English speakers; the degree of misinformation, domain and style adaptation; usability and key features of LLMs. These insights can guide further development, benefiting both writers and a broader user base.

Problem

• Examining AI's impact on professional writing practices and creativity
• Assessing ethical concerns and language support in AI-assisted writing
• Exploring user expectations and key features for effective LLM adoption

Innovation

• Conducted multilingual survey on LLM-assisted writing
• Explored ethical concerns and user expectations
• Analyzed LLM adoption for non-English speakers