The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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]
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
A General Inductive Approach for Analyzing Qualitative Evaluation Data
Thomas, D. R. (2006) · American Journal of Evaluation · doi:10.1177/1098214005283748 · Cited by 10,858
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352 · cited 1×
“They are difficult to ‘steer’, and prompt engineering has become a thriving sub-field aimed at figuring out how to best get a model to do what the user would like [2, 24].”
BackgroundRed Teaming Language Models with Language ModelsPreprint
Perez, E., Huang, S., Song, F., Cai, T., Ring, R., Aslanides, J., Glaese, A., McAleese, N., & Irving, G. (2022) · Conference on Empirical Methods in Natural Language Processing · doi:10.48550/arxiv.2202.03286 · Cited by 1,430
A cognitive process theory of writing
Flower, L., & Hayes, J. R. (1981) · College Composition & Communication · doi:10.58680/ccc198115885 · Cited by 1,025
How Can We Know What Language Models Know?
Jiang, Z., Xu, F. F., Araki, J., & Neubig, G. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00324 · Cited by 1,002
The Intellectual Challenge of CSCW: The Gap Between Social Requirements and Technical Feasibility
Ackerman, M. S. (2000) · Human–Computer Interaction · doi:10.1207/s15327051hci1523_5 · Cited by 892
Training writing skills: a cognitive developmental perspective
Kellogg, R. T. (2008) · Journal of Writing Research · doi:10.17239/jowr-2008.01.01.1 · Cited by 868
Boden, M. A. (2009) · AI Magazine · doi:10.1609/aimag.v30i3.2254 · Cited by 527
Research works printed but not linked: 8
- 10.2307/356600 · DOI · Paper page
- 10.18653/v1 · DOI
- John R. Hayes. 1996. A new framework for understanding cognition and affect in writing. In The Science of Writing: Theories, Methods, Individual Differences, and Applications. Lawrence Erbaum Associates.
- Ming Liu Rafael A Calvo and Vasile Rus. 2020. Automatic Generation and Ranking of Questions for Critical Review. (2020) 15.
- Meghan O’Gieblyn. 2021. Babel: Could a machine have an unconscious? (July 2021). https://www.nplusonemag.com/issue-40/essays/babel-4/
- Michael Quinn Patton. 2002. Qualitative Research and Evaluation Methods (Chapter 5) (3 ed.). Sage Publications.
- Robin Sloan. 2016. Writing with the machine. (May 2016). https://www.robinsloan.com/notes/writing-with-the-machine/
- Feiyu Xu, Hans Uszkoreit, Yangzhou Du, Wei Fan, Dongyan Zhao, and Jun Zhu. 2019. Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges. In Natural Language Processing and Chinese Computing, Jie Tang, Min-Yen Kan, Dongyan Zhao, Sujian Li, and Hongying Zan (Eds.). Springer International Publishing, Cham, 563–574.
Cited by
Sorted by citations
Items 101–135 of 135
Setting the Stage for Fictional and Artificial Characters
Angeli, A. D., & Grigis, P. (2025) · Biannual Conference of the Italian SIGCHI Chapter (CHItaly) · doi:10.1145/3750069.3750155
Keyframer: A Design Probe for Exploring LLM Assistance in 2D Animation Design
Tseng, T., Cheng, R., McNutt, A. M., & Nichols, J. W. (2025) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vl-hcc65237.2025.00014 · Cited by 1
Harper, G. (2025) · Springer eBooks · doi:10.1007/978-3-031-99101-1_2
Jahna, R. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.5842662
Developing Metacognition Through LLM-Enhanced Writing Assignments
Gatzia, D., & Wood, M. K. (2026) · The American history teacher · doi:10.1525/abt.2026.88.1.12 · Cited by 1
One Is Not Enough: How People Use Multiple AI Models in Everyday Life
Pyo, S., Lee, D. N., Rhee, J., Park, S. O., & Lim, Y.-K. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3798682
When Behavior Is Not Enough: Identity Biases in Human-AI Creative Collaboration
Won, H., Kwak, H., An, J., & Han, J. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3799001
RAG Without the Lag: Enabling "What-If" Analysis for Retrieval-Augmented Generation Pipelines
Lauro, Q. R., Shankar, S., Zeighami, S., & Parameswaran, A. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790874 · Cited by 9
Instructional Mechanisms for Professional Writing: A Comparison of Scaffolded Annotation and ChatGPT
Corvite, S., Matz, R. L., Mills, M., & Hui, J. S. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790915
Yin, Y., Wu, M., Huang, R., Tong, X., Zhou, J., Yu, C., & Shi, Y. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790848 · Cited by 4
NarrativeLoom: Enhancing Creative Storytelling through Multi-Persona Collaborative Improvisation
Ma, Y., Peng, Y., Yang, F., Zha, S., Zhang, C., Jia, Z., Zheng, Z., & Zhu, Y. B. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790416 · Cited by 1
From Use to Oversight: How Mental Models Influence User Behavior and Output in AI Writing Assistants
Rismani, S., Blodgett, S. L., Liao, Q. V., Olteanu, A., & Moon, A. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791670 · Cited by 1
Zhang, Y., Jia, K., Zhang, H. J., Zhu, K., Meng, C., Zhang, J., Li, Z., Chen, P., & Sun, L. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791460 · Cited by 1
Supporting Reviewing Reviews: How HCI Authors Handle Peer Reviews of Manuscripts
Yeung, C. A., Stark, J., Li, J., Chevalier, F., Park, J., Kim, Y., & Tang, A. (2025) · International Conference on Human-Engaged Computing (ICHEC) · doi:10.1145/3786995.3786998
Zwischen Mensch und Maschine – Werbefilm im Zeitalter der Künstlichen Intelligenz
Eberwein, T. (2026) · Springer eBooks · doi:10.1007/978-3-658-51264-4_6
FutureLetter: Augmenting Personal Writing for Future-Self Dialogue using LLMs
Tsuyuguchi, K., Chishima, Y., Watanabe, K., & Suzuki, K. (2026) · Augmented Humans International Conference (AHs) · doi:10.1145/3795011.3795042
Exploration of GenAI story writing workshop in Chinese universities: an experiment
Lan, Q., Ding, Y., Lin, W., & Zheng, Y. (2026) · New Writing · doi:10.1080/14790726.2026.2660741
Beyond Extraction? Generating Games with AI, Responsibly
Sharma, V., Mather, E., Mirza-Babaei, P., & Kumar, N. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3813025 · Cited by 1
Lee, S., & Jeong, H. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2689545
Manesh, D., Thomas, T., Brown, C., & Lee, S. W. (2026) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3786580.3786991
Qian, Y. (2026) · Creativity and Cognition · doi:10.1145/3803784.3804476
How Playwrights Use Software: Towards Writing Support Tools for Performance
Chen, A. S., Li, I., Huq, P., Donaldson, R., & Ringland, K. E. (2026) · Creativity & Cognition · doi:10.1145/3803784.3807548
Liu, S., Qian, Y. Z., Zhang, F., Allen, R. W., & Lc, R. (2026) · Creativity and Cognition · doi:10.1145/3803784.3807563
Designing Through Dialogue: Interaction Patterns in AI-Assisted UI Prototyping
Breckner, K., Hirschmann, F., Kotzian, D., Neumayr, T., Altmann, J., & Augstein, M. (2026) · Creativity and Cognition · doi:10.1145/3803784.3816854
Ratul, M. A. I., Haque, F., Saha, P., Noor, J., & Haque, S. M. T. (2026) · ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) · doi:10.1145/3811242.3819097
Kuang, E., Vermeulen, J., Fitzmaurice, G. W., & Matejka, J. (2026) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3841172
The Generation Effect and AI-Assisted Writing: Towards a Framework of Cognitive Fidelity
Dalsgaard, P. (2026) · European Conference on Cognitive Ergonomics · doi:10.1145/3822301.3822343
Generative AI as a Mediator in Creator Collaboration: Challenges, Design Opportunities, and Concerns
Kim, H., Kim, J., & Choi, Y. J. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3816940
Exploring the Impact of AI-powered Creativity Support Tools on Professional Creative Workflows
Moruzzi, C., Bird, C., & Herman, L. M. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3816933
Understanding how expertise shapes human-AI collaboration in graphic design with PatternPursuit
Martins, J. R., Martins, P., & Boavida, A. (2026) · Design Science · doi:10.1017/dsj.2026.10078
Framing the Formless: A Xing–Qi–Shen Framework for Culturally Aware Multimodal Human–AI Co-Creation
Yu, C., Tan, S., Zhang, Y., & Yu, L. (2026) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3776574.3831143
Yang, R., Chu, S., 袁道武, Sun, X., & Zeng, H. (2026) · IEEE Information Technology and Mechatronics Engineering Conference (ITOEC) · doi:10.1109/itoec69754.2026.11732778
Freeman, G., Toxtli, C., Panchanadikar, R., Hu, Y., Frommel, J., Mandryk, R. L., Christian Guckelsberger, B., & Denisova, A. (2026) · Annual Symposium on Computer-Human Interaction in Play · doi:10.1145/3800965.3834572
How Do Human Creators Embrace Human-AI Co-Creation? A Perspective on Human Agency of Screenwriters
Tang, Y., Zhou, J., Li, H., Xie, X., Ma, X., & Qu, H. (2026) · arXiv · Cited by 5
Where Do I 'Add the Egg'?: Exploring Agency and Ownership in AI Creative Co-Writing Systems
Carrera, D., Thomas-Mitchell, J., & Wigdor, D. (2025) · arXiv · doi:10.48550/arxiv.2509.15440
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
