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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Only a handful of user studies have investigated how professional writers perceive and use AI systems.
“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]
The Methodology (section 3.1) describes interviews with 20 creative writers about their attitudes toward hypothetical AI writing tools and, for some participants, their use of SudoWrite. But Participant Population (section 3.2.4) says only two were full-time writers and the sample skewed slightly amateur, so this is not specifically a study of professional writers. (LLM, conf=0.86)
Experimental — accuracy not yet measuredEmpirical studies describing how users evaluate LLMs are still a minority and difficult to generalise due to variation of participants, methods, systems, and disciplinary perspectives.
“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]
Section 5.1.3 says responses to AI writing tools vary across users and that studies rarely account for this variation. Section 7.2 says the study’s participant population limits broader conclusions. The paper does not establish that empirical studies of how users evaluate LLMs are a minority, or attribute difficulty generalising across that literature to differences in methods, systems, and disciplinary perspectives. The indexed passages provide no page numbers. (LLM, conf=0.85)
Experimental — accuracy not yet measuredCurrent LLM systems fail to satisfy writers' needs because they do not understand writers' intentions.
“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]
Section 6.2.3 says feedback often failed to meet writers’ needs when a support actor did not understand their intention, and that writers were skeptical computers could do so. It also says little work explicitly models writers’ intentions. The claim overstates these qualified observations as a categorical failure of current systems. (LLM, conf=0.86)
Experimental — accuracy not yet measuredLLMs are often described as biased, perpetuate stereotypes, and jeopardise trust in generated content.
“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]
Section 5.1.2 says language models reflect white, Western, and male perspectives shaped by their training data, supporting the bias portion of the claim. It also says making those assumptions explicit will not necessarily make writers see the models as trusted readers. The passages do not establish that the models perpetuate stereotypes or jeopardise trust in generated content specifically. (LLM, conf=0.82)
Experimental — accuracy not yet measuredDespite limitations, playwrights identified utility at different stages of creative writing, from brainstorming through the summarisation of relevant sources to editing.
“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]
The Methodology (§3.1) describes brainstorming and rewriting features, and Reviewing (§4.2.3) reports writers seeking feedback useful for editing. But the study interviewed creative writers across genres, not playwrights as a group, and our copy does not report that they found summarisation of relevant sources useful; the interview guide only asks about a hypothetical summarisation capability. (LLM, conf=0.85)
Experimental — accuracy not yet measured
Authenticity often determines the value of writers' work.
“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]
Section 5.2.3 says writers were concerned with producing work they felt was authentic, and section 4.4.2 describes concerns that computational help could affect authenticity. But the paper does not say authenticity determines the value of their work; section 5.1.3 emphasizes that writers differ in what they value. (LLM, conf=0.78)
Experimental — accuracy not yet measuredPerceived agency and perceived ownership can influence a writer's long-term satisfaction with AI co-writing systems.
“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]
Section 4.4.1 describes writers’ authority to accept or reject suggestions, and Section 5.2.3 discusses ownership and authenticity in AI-assisted writing. But Section 7.1 says most participants had limited experience with AI writing tools; the paper does not establish that perceived agency or ownership influences long-term satisfaction with them. (LLM, conf=0.85)
Experimental — accuracy not yet measuredPrior empirical studies on screenwriters' AI use provide only snapshots of human-AI collaboration, focusing on isolated stages rather than how screenwriters develop their strategies and practices over time.
“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]
The Methodology (§3.1; page unavailable) describes one-hour interviews with creative writers, so the study offers a snapshot rather than tracking how AI-use practices develop over time. But Related Work (§2.1; page unavailable) says the interviews were designed to cover a range of writing processes, not an isolated stage. The participants were writers across genres, not a study of screenwriters’ AI use specifically. (LLM, conf=0.85)
Experimental — accuracy not yet measuredScreenwriting faces similar challenges to creative writing, such as a lack of inspiration and guidance.
“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]
Section 4.2.2 (Translation; page unavailable) describes creative writers seeking help with writer’s block and inspiration. Section 4.2.4 (Motivation; page unavailable) includes a TV-script writer who needed validation, supporting a need for encouragement. The paper does not specifically show that screenwriters lack inspiration or guidance, so the claim extends findings about the broader group to screenwriting. (LLM, conf=0.81)
Experimental — accuracy not yet measuredPrior participants reported utility of LLMs for editorial tasks and acknowledged potential for idea generation.
“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]
LLMs provide non-judgemental support to human creativity at no cost to users.
“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]
A final barrier to LLMs for creative writing is the politics embedded in LLMs.
“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]
Co-writing with AI might potentially threaten the value of writers' work.
“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]
Metaphors can help users understand the capabilities of an AI agent by proposing an alternative mental model.
“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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Cultural Considerations in AI Systems for the Global South: A Systematic Review
Anuyah, O., Wan, R., Adejoro, C., Yeh, T., Metoyer, R. A., & Badillo-Urquiola, K. A. (2023) · African Conference on Human Computer Interaction · doi:10.1145/3628096.3629046 · Cited by 23 · see citing works
Authors' Values and Attitudes Towards AI-bridged Scalable Personalization of Creative Language Arts
Kim, T., Han, H., Adar, E., Kay, M., & Chung, J. J. Y. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642529 · Cited by 20 · see citing works
#PoetsOfInstagram: Navigating The Practices And Challenges Of Novice Poets On Instagram
De, A., & Lu, Z. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642173 · Cited by 23 · see citing works
Intelligent Support Engages Writers Through Relevant Cognitive Processes
Göldi, A., Wambsganß, T., Neshaei, S. P., & Rietsche, R. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642549 · Cited by 21 · see citing works
Xu, X. (., Konnova, A., Gao, B., Peng, C., Vo, D., & Dow, S. P. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713649 · Cited by 20 · see citing works
Wang, J., Huang, C., Yan, S., Xie, W., & He, D. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2352919 · Cited by 19 · see citing works
Siddiqui, M. N., Pea, R. D., & Subramonyam, H. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714119 · Cited by 19 · see citing works
Panchanadikar, R., Freeman, G., Li, L., Schulenberg, K., & Hu, Y. (2024) · Extended Abstracts of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613905.3650845 · Cited by 17 · see citing works
Understanding Screenwriters' Practices, Attitudes, and Future Expectations in Human-AI Co-Creation
Tang, Y., Li, H., Lan, M., Ma, X., & Qu, H. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714120 · Cited by 18 · see citing works
“As a result, it faces similar challenges to creative writing, such as a lack of in-spiration and guidance [34].”
GenAICHI 2025: Generative AI and HCI at CHI 2025
Muller, M. J., Chilton, L. B., Maher, M. L., Martin, C. P., Choi, M., Walsh, G., & Kantosalo, A. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3707213 · Cited by 18 · see citing works
Self-citationChallenges and Opportunities of LLM-Based Synthetic Personae and Data in HCI
Prpa, M., Troiano, G. M., Wood, M., & Coady, Y. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3636293 · Cited by 16 · see citing works
Playwriting with Large Language Models: Perceived Features, Interaction Strategies and Outcomes
Grigis, P., & De Angeli, A. (2024) · Proceedings of the 2024 International Conference on Advanced Visual Interfaces · doi:10.1145/3656650.3656688 · Cited by 13 · see citing works
Porquet, J., Wang, S., & Chilton, L. B. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713854 · Cited by 14 · see citing works
Self-citationQin, H. X., Zhu, G., Fan, M., & Hui, P. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713569 · Cited by 16 · see citing works
Interrogating AI: Characterizing Emergent Playful Interactions with ChatGPT
Nikghalb, M. R., & Cheng, J. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711015 · Cited by 19 · see citing works
An Exploration of Mental Models of AI in Human–AI Co-Creativity: A Framework and Insights
Rezwana, J., & Maher, M. L. (2025) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3769072 · Cited by 11 · see citing works
Toyteller: AI-powered Visual Storytelling Through Toy-Playing with Character Symbols
Chung, J. J. Y., Roemmele, M., & Kreminski, M. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713435 · Cited by 12 · see citing works
Making the Write Connections: Linking Writing Support Tools with Writer Needs
Zhao, Z., Masson, D., Kim, Y., Penn, G., & Chevalier, F. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713161 · Cited by 14 · see citing works
Challenges and Opportunities of LLM-Based Synthetic Personae and Data in HCI
Prpa, M., Troiano, G. M., Yao, B., Li, T. J.-J., Wang, D., & Gu, H. (2024) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3678884.3681826 · Cited by 10 · see citing works
Jelson, A., & Lee, S. W. (2024) · Workshop on Intelligent and Interactive Writing Assistants (In2Writing) · doi:10.1145/3690712.3690720 · Cited by 9 · see citing works
Exploring the Needs of Practising Musicians in Co-Creative AI Through Co-Design
Krol, S. J., Llano, M. T., & Paredes, M. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713894 · Cited by 8 · see citing works
Empowering Social Service with AI: Insights from a Participatory Design Study with Practitioners
Tan, Y., Soh, K. X., Zhang, R., Lee, J., Meng, H., Sen, B., & Lee, Y. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719736 · Cited by 11 · see citing works
Understanding the Potentials and Limitations of Prompt-based Music Generative AI
Choi, Y., Moon, J., Yoo, J., & Hong, J.-H. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713762 · Cited by 8 · see citing works
Li, S., Han, C., Yu, P., Edwards, C., Li, M., Wang, X., Fung, Y. R., Yu, C., Tetreault, J., Hovy, E. H., et al. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.799 · Cited by 8 · see citing works
Xiao, Y., Hancock, C., Agrawal, S., Mehandru, N., Salehi, N., Carpuat, M. J., & Gao, G. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713626 · Cited by 7 · see citing works
Emerging Data Practices: Data Work in the Era of Large Language Models
Garcia, A. A., Candello, H. C. D. S. P., Badillo-Urquiola, K. A., & Wong-Villacrés, M. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714069 · Cited by 8 · see citing works
LLMs as Writing Assistants: Exploring Perspectives on Sense of Ownership and Reasoning
Wasi, A. T., Islam, M. R., & Islam, R. (2024) · Workshop on Intelligent and Interactive Writing Assistants (In2Writing) · doi:10.1145/3690712.3690723 · Cited by 7 · see citing works
Preserving Writer Values in AI Writing Assistance Tools
Guo, A., Wang, L., Heer, J., & Zhang, A. X. (2024) · Workshop on Intelligent and Interactive Writing Assistants (In2Writing) · doi:10.1145/3690712.3690727 · Cited by 6 · see citing works
Does GenAI Deplete Us? The Effects of Using GenAI for Writing on Identity and Ego Depletion
Lee, S., Kim, T., & Moon, W.-K. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719875 · Cited by 6 · see citing works
RevTogether: Supporting Science Story Revision with Multiple AI Agents
Zhang, Y., Fu, K., & Lu, Z. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719888 · Cited by 6 · see citing works
Basoah, J., Chechelnitsky, D., Long, T., Reinecke, K., Zerva, C., Zhou, K., Díaz, M., & Sap, M. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732045 · Cited by 7 · see citing works
Self-citationFeedQUAC: Quick Unobtrusive AI-Generated Commentary
Long, T., Wannamaker, K., Vermeulen, J., Fitzmaurice, G. W., & Matejka, J. (2025) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3765766.3765861 · Cited by 6 · see citing works
Self-citationHow 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) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790300 · Cited by 5 · see citing works
“Building on this shift, prior studies have examined how writers interact with AI during co-creation, describing their active strategies, roles, and workflow patterns [19, 60].”
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 5 · see citing works
Ink and algorithm: Exploring temporal dynamics in generative <scp>AI</scp> ‐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 4 · see citing works
Zhou, D., Gallagher, J. R., & Sterman, S. (2025) · Creativity and Cognition · doi:10.1145/3698061.3726907 · Cited by 4 · see citing works
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 4 · see citing works
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 · see citing works
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 6 · see citing works
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 · see citing works
“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 3 · see citing works
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 · see citing works
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 · see citing works
Wu, M., Liu, G., Wang, B., & Ma, Z. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2633738 · Cited by 2 · see citing works
Fu, K., Huang, J.-F., Ling, L., Hong, S.-M., Zuo, Y., Lc, R., & Li, T. J.-J. (2026) · International Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790400 · Cited by 5 · see citing works
Hu, Y., & Freeman, G. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791077 · Cited by 2 · see citing works
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 · see citing works
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 2 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Fitzsimons, A., Gerber, E. M., & Long, D. (2024) · Workshop on Intelligent and Interactive Writing Assistants (In2Writing) · doi:10.1145/3690712.3690719 · Cited by 1 · see citing works
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 · see citing works
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 · see citing works
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 1 · see citing works
Chakrabarty, T., & Dhillon, P. S. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791276 · Cited by 1 · see citing works
“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 1 · see citing works
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 · see citing works
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 2 · see citing works
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 · see citing works
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 · see citing works
Designing for Posthuman Critical Literacy
Calderwood, A. (2024) · Workshop on Intelligent and Interactive Writing Assistants (In2Writing) · doi:10.1145/3690712.3690729 · Cited by 1 · see citing works
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 · see citing works
Mapping the Transformative Effects of Artificial Intelligence on Journalistic Practices
Aziz, S. (2025) · Komunikator · doi:10.18196/jkm.v17i1.25112 · Cited by 2 · see citing works
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 · see citing works
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
- 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 › Sociology and Political Science › 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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