Creativity Support in the Age of Large Language Models: An Empirical Study Involving Professional Writers
Abstract
The development of large language models (LLMs) capable of following instructions and engaging in conversational interactions has led to increased interest in their use across various support tools. We investigate the effectiveness of contemporary LLMs in assisting professional writers via an empirical user study (n=30). The design of our collaborative writing interface is grounded in the cognitive process model of writing [17]. This allows writers to obtain model help in each of the three non-linear cognitive activities in the writing process: planning, translating and reviewing. Participants write short fiction/non-fiction with model help and are subsequently asked to submit a post-completion survey to provide qualitative feedback on the potential and pitfalls of LLMs as writing collaborators. Upon analyzing the writer-LLM interactions, we find that while seeking help across all three types of cognitive activities, writers find LLMs more helpful in translation and reviewing. Our findings from analyzing both the interactions and the survey responses highlight future research directions in creative writing assistance using LLMs.
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These findings are in line with previous studies about AI assistants for creative writing.
“Participants described the generations as straightforward and obvious, echoing the findings from previous studies about AI assistants for creative writing (Ippolito et al., 2022; Chakrabarty et al., 2024).”
From Game Plot Design With an LLM-Powered Assistant: An Empirical Study With Game Designers (2026) · cites this paper as Chakrabarty et al., 2024
Prior studies revealed inherent limitations with LLM-based creative writing, including repetitive, predictable, and clichéd responses, challenges in maintaining the writer's unique style and voice, and superficial language understanding abilities.
“Both studies revealed inherent limitations with LLM-based creative writing, including the generation of repetitive, predictable, and clichéd responses, challenges in maintaining the writer's unique style and voice, and superficial language understanding abilities.”
From Game Plot Design With an LLM-Powered Assistant: An Empirical Study With Game Designers (2026) · cites this paper as Chakrabarty et al., 2024
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References 45 · Cited by 61
- Authors
- Chakrabarty, T., Padmakumar, V., Brahman, F., & Muresan, S.
- Affiliations
- Columbia University (US)
- New York University (US)
- University of Washington (US)
- Venue
- Creativity and Cognition
- Publisher
- ACM
- Published
- 2024-06-23
- Pages
- 132-155
- OpenAlex
- W4399917664
- Cited by
- 61
- References
- 45
- Citations by year
- 2026: 24 · 2025: 35 · 2024: 2
- FWCI
- 38.20
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Creativity and Cognition (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Creativity in Education and Neuroscience
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- large language models, professional writers, writing planning, writing reviews
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