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Social Dynamics of AI Support in Creative Writing
Open accessAbstract
Recently, large language models have made huge advances in generating coherent, creative text. While much research focuses on how users can interact with language models, less work considers the social-technical gap that this technology poses. What are the social nuances that underlie receiving support from a generative AI? In this work we ask when and why a creative writer might turn to a computer versus a peer or mentor for support. We interview 20 creative writers about their writing practice and their attitudes towards both human and computer support. We discover three elements that govern a writer’s interaction with support actors: 1) what writers desire help with, 2) how writers perceive potential support actors, and 3) the values writers hold. We align our results with existing frameworks of writing cognition and creativity support, uncovering the social dynamics which modulate user responses to generative technologies.
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“Yet, only a handful of user studies have investigated how professional writers perceive and use AI systems [13,27,28]”
From Playwriting with Large Language Models: Perceived Features, Interaction Strategies and Outcomes (2024) · cites this paper as [27]
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“Empirical studies describing how users evaluate LLMs are still a minority and difficult to generalise due to a variation of participants, methods, systems, and disciplinary perspectives [13,27,28,38,47].”
From Playwriting with Large Language Models: Perceived Features, Interaction Strategies and Outcomes (2024) · cites this paper as [27]
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“Current systems fail to satisfy the writers' needs because they do not understand their intentions [27].”
From Playwriting with Large Language Models: Perceived Features, Interaction Strategies and Outcomes (2024) · cites this paper as [27]
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“The models are often described as biased.They perpetuate stereotypes and jeopardise trust in the generated content [27,38].”
From Playwriting with Large Language Models: Perceived Features, Interaction Strategies and Outcomes (2024) · cites this paper as [27]
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In the citing paper
“Despite these limitations, playwrights identified utility at different stages of creative writing, from brainstorming through the summarisation of relevant sources to editing [27,42,47].”
From Playwriting with Large Language Models: Perceived Features, Interaction Strategies and Outcomes (2024) · cites this paper as [27]
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“From writers' perspectives, authenticity often determines the value of their work, which co-writing with AI might potentially threaten [18].”
From 'It was 80% me, 20% AI': Seeking Authenticity in Co-Writing with Large Language Models (2025) · cites this paper as [18]
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In the citing paper
“To support broader adoption of AI co-writing systems among writers, it is crucial to consider perceived agency and perceived ownership, as these factors can influence a writer's long-term satisfaction with such systems [23].”
From Where Do I 'Add the Egg'?: Exploring Agency and Ownership in AI Creative Co-Writing Systems (2026) · cites this paper as [23]
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“However, these accounts focus on isolated stages of use and therefore provide only snapshots of human-AI collaboration.”
From How Do Human Creators Embrace Human-AI Co-Creation? A Perspective on Human Agency of Screenwriters (2026) · cites this paper as [19]
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“As a result, it faces similar challenges to creative writing, such as a lack of inspiration and guidance [34].”
From Understanding Screenwriters' Practices, Attitudes, and Future Expectations in Human-AI Co-Creation (2025) · cites this paper as [34]
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“Not only did the participants report utility for editorial tasks [27,42,47], but they also acknowledge potential for idea generation [13,38,42].”
From Playwriting with Large Language Models: Perceived Features, Interaction Strategies and Outcomes (2024) · cites this paper as [27]
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“They provide non-judgemental support to human creativity [27], which comes at no cost to their users.”
From Playwriting with Large Language Models: Perceived Features, Interaction Strategies and Outcomes (2024) · cites this paper as [27]
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“A final barrier was identified in the politics embedded in LLMs.”
From Playwriting with Large Language Models: Perceived Features, Interaction Strategies and Outcomes (2024) · cites this paper as [27]
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“From writers' perspectives, authenticity often determines the value of their work, which co-writing with AI might potentially threaten [18].”
From 'It was 80% me, 20% AI': Seeking Authenticity in Co-Writing with Large Language Models (2025) · cites this paper as [18]
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“Users may have preconceived notions about AI systems, so metaphors can help users understand the capabilities of an AI agent by proposing an alternative mental model [11, 23].”
From Where Do I 'Add the Egg'?: Exploring Agency and Ownership in AI Creative Co-Writing Systems (2026) · cites this paper as [23]
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Citations
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Schrödinger's Update: User Perceptions of Uncertainties in Proprietary Large Language Model Updates
Ma, Z., Mei, Y., Gajos, K. Z., & Arawjo, I. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3651100 · Cited by 7
Ink and algorithm: Exploring temporal dynamics in generative AI ‐assisted writing
Yang, K., Cheng, Y., Zhao, L., Raković, M., Swiecki, Z., Gašević, D., & Chen, G. (2026) · British Journal of Educational Technology · doi:10.1111/bjet.70031 · Cited by 22
Zhou, D., Gallagher, J. R., & Sterman, S. (2025) · Creativity and Cognition · doi:10.1145/3698061.3726907 · Cited by 6
StorySage: Conversational Autobiography Writing Powered by a Multi-Agent Framework
Talaei, S., Li, M., Grover, K., Hippler, J. K., Yang, D., & Saberi, A. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747681 · Cited by 9
Facilitating Longitudinal Interaction Studies of AI Systems
Long, T., Wang, S., Fabre, É., Wang, T., Sathya, A., Wu, J., Petridis, S., Li, D., Chakrabarty, T., Jiang, Y., et al. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746058.3758469 · Cited by 4
Self-citationMa, Z., Mei, Y., Bruderlein, C., Gajos, K. Z., & Pan, W. (2025) · Annual Symposium on Human-Computer Interaction for Work (CHIWORK) · doi:10.1145/3729176.3729194 · Cited by 12
Where Do I 'Add the Egg'?: Exploring Agency and Ownership in AI Creative Co-Writing Systems
Carrera, D., Thomas-Mitchell, J., & Wigdor, D. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791952 · Cited by 3
“Two of the ways in which writers perceive their interactions with AI co-writing systems are through their perceptions of agency when they use a system and their perceptions of ownership over the final writing product [26].”
InfluentialLyric Poetry in the Face of Posthumanism: An Analysis of Generative AI-Assisted Poetry Writing
Huang, Y., Shea, J., Howe, D. C., & Holopainen, J. P. (2025) · Creativity and Cognition · doi:10.1145/3698061.3726919 · Cited by 5
Mixed Initiative Comic Making in the Wild: Taking an Artist’s Approach Out of the Studio
Knight, Y., & Eladhari, M. P. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-12405-0_4 · Cited by 2
Stop perfecting the feedback, start supporting the uptake: rethinking AI in writing instruction
Helm, G., & Hesse, F. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1737037 · Cited by 6
Wu, M., Liu, G., Wang, B., & Ma, Z. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2633738 · Cited by 2
Fu, K., Huang, J., Ling, L., Hong, S., Zuo, Y., Ray, L. C., & Li, T. J.-J. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790400 · Cited by 7
Hu, Y., & Freeman, G. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791077 · Cited by 2
AI-Facilitated Coercive Control: An Experimental Study
Kim, H., Ristenpart, T., & Dell, N. L. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790859 · Cited by 2
From Crafting Text to Crafting Thought: Grounding AI Writing Support to Writing Center Pedagogy
Liu, Y., Gallagher, J., Sterman, S., & August, T. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790292 · Cited by 4
Biskjær, M. M., Dalsgaard, P., & Halskov, K. (2023) · Palgrave studies in creativity and culture · doi:10.1007/978-3-031-41907-2_7 · Cited by 2
Jiang, L., Zhu, A., & Oppegaard, B. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719966 · Cited by 2
Panchanadikar, R., & Freeman, G. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790869 · Cited by 3
Understanding Screenwriters' Practices, Attitudes, and Future Expectations in Human-AI Co-Creation
Tang, Y., Li, H., Lan, M., Ma, X., & Qu, H. (2025) · arXiv · Cited by 18
Fitzsimons, A., Gerber, E. M., & Long, D. (2024) · Workshop on Intelligent and Interactive Writing Assistants (In2Writing) · doi:10.1145/3690712.3690719 · Cited by 1
Scripted Spaces - An Approach to Generative AI for 3D Spaces
Kratky, A., Brown, M. O., Chuang, S., & Liu, Q. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-94168-9_9 · Cited by 1
Authorship and Hallucination in AI-Generated Literary Texts
Raffini, D. (2025) · Springer Proceedings in Humanities and Social Sciences · doi:10.1007/978-3-031-88334-7_3 · Cited by 1
Constella: Supporting Storywriters’ Interconnected Character Creation through LLM-Based Multi-Agents
Park, S., Park, S., & Lim, Y.-K. (2026) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3796234 · Cited by 6
Chakrabarty, T., & Dhillon, P. S. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791276 · Cited by 6
“Control Is a Trajectory, Not a Point”: Conceptualizing Control in Human-AI Co-Creativity
Issak, A., Rezwana, J., & Harteveld, C. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790861 · Cited by 6
Supervisory Control Theory for LLM Revision
Li, W., & Toxtli, C. (2026) · ACM Conference on AI and Agentic Systems (CAIS) · doi:10.1145/3786335.3813152 · Cited by 1
From Planning to Revision: How AI Writing Support at Different Stages Alters Ownership
Gero, K. I., Long, T., Schnitzler, C., & Dhillon, P. S. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3813003 · Cited by 4
Self-citationBeyond Compliance: How AI Could Help Creative Writers by Refusing Them
Qin, H. X., Zhu, G., Fan, M., & Hui, P. (2026) · Creativity and Cognition · doi:10.1145/3803784.3807571 · Cited by 1
How different mental models of AI-based writing assistants impact writers’ interactions with them
Rismani, S., Blodgett, S. L., Olteanu, A., Liao, Q. V., & Moon, A. (2024) · Workshop on Intelligent and Interactive Writing Assistants (In2Writing) · doi:10.1145/3690712.3690722 · Cited by 1
Designing for Posthuman Critical Literacy
Calderwood, A. (2024) · Workshop on Intelligent and Interactive Writing Assistants (In2Writing) · doi:10.1145/3690712.3690729 · Cited by 1
Chniter, M., Kallel, I., García, A. B., & Medina, J. (2024) · International Conference on Information Technology Based Higher Education and Training (ITHET) · doi:10.1109/ithet61869.2024.10837631 · Cited by 3
Mapping the Transformative Effects of Artificial Intelligence on Journalistic Practices
Aziz, S. (2025) · Komunikator · doi:10.18196/jkm.v17i1.25112 · Cited by 2
Panchanadikar, R., Hu, Y., & Freeman, G. (2026) · Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3805689.3812248 · Cited by 1
Diffraction, Creativity and AI: Towards New Methods for Design Research
Bird, C. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3721091
Designing AI-Based Language Tools for Non-Native Speakers' Language Use and Development
Xiao, Y. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3721094
Emerging Paradigms in Human–AI Creativity: From Visualisation to Biofeedback in Adaptive Interfaces
Herrington, J., Biedermann, T. A. J., & Swift, B. (2025) · Springer series on cultural computing · doi:10.1007/978-3-031-86551-0_2 · Cited by 1
Metadata
- Authors
- Gero, K. I., Long, T., & Chilton, L. B.
- Affiliations
- Columbia University (US)
- Venue
- Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2023-04-19
- Pages
- 1-15
- OpenAlex
- W4366594764
- Cited by
- 129
- References
- 24
- Citations by year
- 2026: 43 · 2025: 41 · 2024: 42 · 2023: 3
- FWCI
- 29.94
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- National Science Foundation (2129020)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Art, Technology, and Culture
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Systems Theories and Implementation
- Keywords
- generative AI, creative writing, large language models, peer support, mentor support, writing cognition, creativity support
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