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A Design Space for Intelligent and Interactive Writing Assistants
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In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to address this challenge by proposing a design space as a structured way to examine and explore the multidimensional space of intelligent and interactive writing assistants. Through community collaboration, we explore five aspects of writing assistants: task, user, technology, interaction, and ecosystem. Within each aspect, we define dimensions and codes by systematically reviewing 115 papers, while leveraging the expertise of researchers in various disciplines. Our design space aims to offer researchers and designers a practical tool to navigate, comprehend, and compare the various possibilities of writing assistants, and aid in the design of new writing assistants.
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“Based on this review and prior literature [35], we identified three interface metaphor archetypes that were the most prevalent: tool-like, agentic, and magic.”
From Where Do I 'Add the Egg'?: Exploring Agency and Ownership in AI Creative Co-Writing Systems (2026) · cites this paper as [35]
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
“Lee et al. [74] identified a direct correlation between self-reported ownership and the proportion of user-written versus AI-generated text.”
From Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process (2025) · cites this paper as [74]
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
“Support for planning takes the form of generative ideation, usually presented as suggestions [74,135], although some systems have a more equal and collaborative storytelling focus that weaves the AI ideas into the story text [70,92].”
From Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process (2025) · cites this paper as [74]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Previous work has identified agency and ownership as key factors in how writers experience AI co-writing [35]”
From Where Do I 'Add the Egg'?: Exploring Agency and Ownership in AI Creative Co-Writing Systems (2026) · cites this paper as [35]
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“Based on a review of prior literature, Lee et al. described three interaction metaphors for AI co-writing systems.”
From Where Do I 'Add the Egg'?: Exploring Agency and Ownership in AI Creative Co-Writing Systems (2026) · cites this paper as [35]
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“In the domain of AI co-writing systems, interface metaphors have also been found to impact the types of interactions that users expect to encounter.”
From Where Do I 'Add the Egg'?: Exploring Agency and Ownership in AI Creative Co-Writing Systems (2026) · cites this paper as [35]
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“Magic-like interfaces , present in nine systems, did not fall along the Tool-Hybrid-Agent spectrum described by Lee et al. [35].”
From Where Do I 'Add the Egg'?: Exploring Agency and Ownership in AI Creative Co-Writing Systems (2026) · cites this paper as [35]
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“Lee et al. (2024) emphasized that the effectiveness of a writing assistant often depends on how well it aligns with a user's specific needs.”
From Ink and algorithm: Exploring temporal dynamics in generative AI ‐assisted writing (2026) · cites this paper as Lee et al., 2024
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“Lee et al. (2024) categorized the outputs of writing assistants into three types: analysis, generation and proposal.”
From Ink and algorithm: Exploring temporal dynamics in generative AI ‐assisted writing (2026) · cites this paper as Lee et al., 2024
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“However, how writers' relationships to systems evolve through extended use (Lee et al., 2024), especially in the context of interactions with GAI tools, remains underexplored.”
From Ink and algorithm: Exploring temporal dynamics in generative AI ‐assisted writing (2026) · cites this paper as Lee et al., 2024
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Cites
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RoBERTa: A Robustly Optimized BERT Pretraining Approach
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Orchid-Creator: An Authoring Tool Supporting LLM-Driven Interactive Narrative Creation
Wu, Z., Kumyol, S., Ma, Z., & Braud, T. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791426 · Cited by 1
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A Design Space for Live Music Agents
Kim, Y., Brade, S., Wang, A., Zhou, D., Kim, H., Wang, B., Lee, S.-J., García, H. F., Huang, A., & Donahue, C. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791291 · Cited by 4
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“Large language models (LLMs) are currently at the forefront of writing assistance research in HCI [25].”
From Fragmentation to Focus: How AI Can Assist Researchers in Academic Writing
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“Upon processing, the information is then systematically propagated back, culminating in the fulfillment of user requirements, thereby completing the cohesive interaction loop between the user and the system [6].”
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Deshmukh, P. V., & Shahade, A. K. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-1267-3_16
Scaling Analysis of Creative Activity Traces via Fuzzy Linkography
Smith, A., Anderson, B. R., Otto, J., Karth, I., Sun, Y., Chung, J. J. Y., Roemmele, M., & Kreminski, M. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3720184
“Recent years have seen especially strong growth in the adoption of AI-based CSTs—in creative domains as wide-ranging as writing [36], visual art [7, 45], game design [23], fashion design [28], worldbuilding [12], and music [40].”
Self-citationDeshmukh, P. V., & Shahade, A. K. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-2700-4_12
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CapTune: Adapting Non-Speech Captions With Anchored Generative Models
Huang, J. Z., Pataca, C. D. L., Wu, L.-Y., & Jain, D. (2025) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3663547.3746346 · Cited by 1
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Faceless AI - Powered Localized Advertisement Creation Platform for Rural Communities
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“Lee et al. [36] proposed a comprehensive design space for intelligent and interactive writing assistants, providing a valuable foundation for exploring system design.”
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Zindulka, T., Richter, S., & Buschek, D. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3798711 · Cited by 1
“Survey [Lee et al., 2024] (n = 115) LLM "deep research" tools Datasets for analysis 1.”
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Corvite, S., Matz, R. L., Mills, M., & Hui, J. S. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790915
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Wettstein, L., Neshaei, S. P., Rietsche, R., & Wambsganß, T. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790599 · Cited by 1
“This builds upon prior work that has already demonstrated the benefits of traditional writing assistants, which feature a text area and feedback module as their primary interface elements [39, 77].”
Self-citationWriting with AI Can Reduce Gender Bias in Hiring Evaluations
Liu, A. T. H., Lee, M., & Bai, X. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791136
“Our interface provides three autocomplete suggestions in a pop-up box style, following prior work [127, 128] and our pilots, which confirmed three was a balance between providing enough options to choose and not causing information fatigue.”
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“The HCI community has a long-standing interest in intelligent writing tools [51], supporting writers across various stages, such as brainstorming ideas [12, 27, 77], planning outlines [74, 87], drafting content [20, 33, 43, 47, 48], and refning text [1, 42, 52, 72, 85].”
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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
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Yeh, C., Truong, A., Dontcheva, M., & Wang, B. K. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791337
“LLMs are widely used for story writing assistance [48, 81, 83].”
iRULER: Intelligible Rubric-Based User-Defined LLM Evaluation for Revision
Bai, J., Cheong, W. S., Müller, P., & Lim, B. Y. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790539 · Cited by 4
Yu, Y., Corino, G., & Arrivabene, R. M. C. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2658245
Liu, Y. Q., Bai, H., & Li, Y. (2026) · Journal Of Big Data · doi:10.1186/s40537-026-01453-1
“The source of motivation to this research is that it enables AI to be an ally in co-creation that can enable infinite visual exploration, expressive versatility and engaging human-machine interaction [11].”
Creative Reading: Scaffolding Reading for Transformation
Liu, S., Abowitz, S., Liu, Y., Sterman, S., Almeda, S. G., & Kreminski, M. (2026) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/3800935.3830891
Self-citationBeyond 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
Jang, Y., Lyu, Z., Arsenault, A. C., Kreps, S. E., & Yang, Q. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3813085
“Research in HCI has witnessed rapid expansion in the exploration of LLMs for writing assistance [11, 21, 24, 36, 45, 46, 49, 54].”
Hwang, S., Hwang, S., Lee, S., Kim, D.-J., & Lee, K. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3812859 · Cited by 3
“Prior work shows that visually distinguishing AI-generated text can clarify authorship and contribution [47], and that lacking such cues can affect writing flow, autonomy, and transparency [45].”
He, J., Akella, A., Do, H. J., & Weisz, J. D. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3806737
What Influences Readers' and Writers' Perceived Necessity of AI Disclosure?
Fang, J., Wen, V., & Lee, M.-N. (2026) · Conference on Fairness, Accountability and Transparency · doi:10.1145/3805689.3806724 · Cited by 1
“Many people are turning to AI to assist with their writing process [11, 70, 129], with goals ranging from grammar check to automated content generation, as well as practices that span using AI-generated text as a reference to adopting it verbatim [63, 109, 128].”
Self-citationTimelyAI: When Should Generative AI Assistants Intervene?
Zhao, Y., Qing, Rechkemmer, A., Xing, Y., Stork, L., Meroño-Peñuela, A., Whiting, M. E., Sarkar, A., Brumby, D. P., & Simperl, E. P. B. (2026) · Annual Symposium on Human-Computer Interaction for Work (CHIWORK) · doi:10.1145/3805029.3818270
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
“Surveys of such tools have guided researchers towards understudied areas of writing, such as Zhao et al.’s recommendations for tools which support pre-writing and visualization [68] and Lee et al’s comprehensive design space for intelligent writing assistants [36].”
The First Reflection in Creative Experience (RiCE) Workshop
Ford, C., Sutskova, O., Cox, S. R., Sterman, S., Kreminski, M., van Koningsbruggen, R., Wang, A., Otenen, E., Ross, K., Fede, G. D., et al. (2026) · Creativity and Cognition · doi:10.1145/3803784.3804465 · Cited by 1
“…Surveys) where attendees would be invited as co-authors to develop codes for the initial categories established in the workshop and apply them to literature; this builds on the organisers’ previous successes in related domains, including the taxonomy for intelligent writing assistants [68].”
Self-citationCo-Designing an AI Feedback Tool for Visual Artists
Johnston, H., Pappin, N., & Thue, D. (2026) · Creativity and Cognition · doi:10.1145/3803784.3807569
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
“In this study, we employed the Participatory Design approach, widely recognized in the HCI community [14, 51, 108], to collaboratively design GenAI concepts supporting effective collaboration among creators with different expertise.”
Broader Impacts of GenAI in Communication: Building Agendas for Research, Design, and Policymaking
Zhi, J., Gao, L., Hwang, A. H., He, J., Hiniker, A., Choudhury, M. D., Lee, M., & Chetty, M. (2026) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3785651.3816118
Self-citationSong, J., Wei, Z., He, C., Liao, J., Xu, X., & Yip, D. K. M. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2745545
Writing as a testbed for open ended agents
Gooding, S., Lopez-Rivilla, L., & Grefenstette, E. (2025) · arXiv · doi:10.48550/arxiv.2503.19711
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
Performance and Metacognition Disconnect when Reasoning in Human-AI Interaction
Fernandes, D., Villa, S., Nicholls, S., Haavisto, O., Buschek, D., Schmidt, A., Kosch, T., Shen, C., & Welsch, R. (2024) · arXiv · doi:10.48550/arxiv.2409.16708 · Cited by 71
Self-citation
Metadata
- Authors
- Lee, M., Gero, K. I., Chung, J. J. Y., Shum, S. B., Raheja, V., Shen, H., Venugopalan, S., Wambsganss, T., Zhou, D., Alghamdi, E. A., August, T., Bhat, A., Choksi, M. Z., Dutta, S., Guo, J. L. C., Hoque, M. N., Kim, Y., Knight, S., Neshaei, S. P., Shibani, A., Shrivastava, D., Shroff, L., Sergeyuk, A., Stark, J., Sterman, S., Wang, S., Bosselut, A., Buschek, D., Chang, J. C., Chen, S., Kreminski, M., Park, J., Pea, R., Rho, E. H. R., Shen, Z., & Siangliulue, P.
- Affiliations
- Microsoft (United States) (US)
- Harvard University (US)
- Midjourney, United States
- University of Technology Sydney (AU)
- Grammarly, United States
- University of Michigan (US)
- Google (United States) (US)
- Bern University of Applied Sciences (CH)
- University of Illinois Urbana-Champaign (US)
- King Abdulaziz University (SA)
- University of Washington (US)
- McGill University (CA)
- Cornell Tech (US)
- University of Tennessee at Knoxville (US)
- University of Maryland, College Park (US)
- Korea Advanced Institute of Science and Technology (KR)
- École Polytechnique Fédérale de Lausanne (CH)
- Google DeepMind (United Kingdom) (GB)
- Stanford University (US)
- JetBrains Research, Serbia and Montenegro
- University of Toronto (CA)
- Columbia University (US)
- University of Bayreuth (DE)
- Allen Institute for Artificial Intelligence (US)
- Department of Computer Science, University of Richmond, United States
- Virginia Tech (US)
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- B12, United States
- ORCID
- Lee, Mina
- Gero, Katy Ilonka
- Chung, John Joon Young
- Shum, Simon Buckingham
- Raheja, Vipul
- Shen, Hua
- Venugopalan, Subhashini
- Wambsganss, Thiemo
- Zhou, David
- Alghamdi, Emad A.
- August, Tal
- Bhat, Avinash
- Choksi, Madiha Zahrah
- Dutta, Senjuti
- Guo, Jin L.C.
- Hoque, Md Naimul
- Kim, Yewon
- Knight, Simon
- Neshaei, Seyed Parsa
- Shibani, Antonette
- Shrivastava, Disha
- Shroff, Lila
- Sergeyuk, Agnia
- Stark, Jessi
- Sterman, Sarah
- Wang, Sitong
- Bosselut, Antoine
- Buschek, Daniel
- Chang, Joseph Chee
- Chen, Sherol
- Kreminski, Max
- Park, Joonsuk
- Pea, Roy
- Rho, Eugenia Ha Rim
- Shen, Zejiang
- Siangliulue, Pao
- Venue
- Proceedings of the CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2024-05-11
- Pages
- 1-35
- OpenAlex
- W4393118307
- Cited by
- 136
- References
- 129
- Citations by year
- 2026: 47 · 2025: 71 · 2024: 18
- FWCI
- 43.82
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- ARBOR - Bern University of Applied Sciences Repository (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- University of Technology Sydney
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
- Keywords
- writing assistants, design space, interactive writing, writing support systems, task dimensions, technology dimension
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.
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