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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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Citations
Cites
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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
“Dhillon et al. emphasized the importance of personalized systems to accommodate the diverse experiences of writers [28], and Lee et al. provided broad recommendations for the development of future AI tools [57].”
Cai, Z., Han, A., Zhou, X., Gazulla, E. D., & Peppler, K. (2025) · Proceedings of the 24th Interaction Design and Children · doi:10.1145/3713043.3731506 · Cited by 17
Creative Writers' Attitudes on Writing as Training Data for Large Language Models
Gero, K. I., Desai, M., Schnitzler, C., Eom, N., Cushman, J., & Glassman, E. L. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713287 · Cited by 16
Self-citationChakrabarty, T., Laban, P., & Wu, C.-S. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713559 · Cited by 55
Generative AI Uses and Risks for Knowledge Workers in a Science Organization
Wagman, K. B., Dearing, M. T., & Chetty, M. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713827 · Cited by 23
“Another research direction has investigated creative knowledge work tasks (rather than professions) that include writing [13, 25] or music composition [51].”
The X Factor: On the Relationship between User eXperience and eXplainability
Deters, H., Droste, J., Heß, A., Klös, V., Schneider, K. A., Speith, T., & Vogelsang, A. (2024) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3679318.3685352 · Cited by 15
“InfluencesofExplainability on User Experience Several sources also describe how explainability can improve aspects such as trust [22, 38] or user engagement [27].”
Advances in artificial intelligence: a review for the creative industries
Anantrasirichai, N., Zhang, F., & Bull, D. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11494-w · Cited by 62
You’ve Got AI Friend in Me: LLMs as Collaborative Learning Partners
Oppenheimer, D. M., Cash, T. N., & Pensky, A. C. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-025-00521-6 · Cited by 21
“The development of argumentative reasoning skills can be enhanced through the use of educational technology (Lee et al., 2024).”
Do LLMs Meet the Needs of Software Tutorial Writers? Opportunities and Design Implications
Bhat, A., Shrivastava, D., & Guo, J. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3660692 · Cited by 13
“However, existing work on designing tools to support writing activities lacks a discussion on tutorial authoring [17, 33].”
BackgroundSelf-citationThe Dearth of the Author in AI-Supported Writing
Kreminski, M. (2024) · Workshop on Intelligent and Interactive Writing Assistants (In2Writing) · doi:10.1145/3690712.3690725 · Cited by 13
Self-citationSARD: A Human-AI Collaborative Story Generation
Radwan, A. Y., Alasmari, K. M., Abdulbagi, O. A., & Alghamdi, E. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-76806-4_7 · Cited by 17
Self-citationToyteller: 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 23
Self-citationMaking 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 21
“Previous research [35, 61, 115] has explored the landscape of WST through literature reviews.”
InfluentialSelf-citationMaking AI-Enhanced Videos: Analyzing Generative AI Use Cases in YouTube Content Creation
Anderson, T., & Niu, S. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719991 · Cited by 19
Orchid: A Creative Approach for Authoring LLM-Driven Interactive Narratives
Wu, Z., Kumyol, S., Wong, S., Hu, X., Tong, X., & Braud, T. (2025) · Creativity and Cognition · doi:10.1145/3698061.3726906 · Cited by 13
MindMate: Exploring the Effect of Conversational Agents on Reflective Writing
Neshaei, S. P., Wambsganß, T., Bouchrifi, H. E., & Käser, T. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3720029 · Cited by 14
Self-citationPhraselette: A Poet’s Procedural Palette
Calderwood, A., Chung, J. J. Y., Sun, Y., Roemmele, M., & Kreminski, M. (2025) · Designing Interactive Systems Conference · doi:10.1145/3715336.3735832 · Cited by 11
Self-citationScriptViz: A Visualization Tool to Aid Scriptwriting based on a Large Movie Database
Rao, A., Chou, J.-P., & Agrawala, M. (2024) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3654777.3676402 · Cited by 13
“The recent advancement in language models has ushered in a new era of intelligent writing assistants [38].”
Ladak, A., Wilks, M., Loughnan, S., & Anthis, J. R. (2025) · International Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713130 · Cited by 19
“…replicated in the context of robots [29], and its dimensions have been shown to affect important HCI outcomes, such as the willingness to interact with a robot [52], the perceived helpfulness of a chatbot [33], and the “uncanny valley,” the feeling of unease when robots seem too human-like [17].”
Understanding Socio-technical Factors Configuring AI Non-Use in UX Work Practices
Cha, I., & Wong, R. Y. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713140 · Cited by 26
Fuzzy Linkography: Automatic Graphical Summarization of Creative Activity Traces
Smith, A., Anderson, B. R., Otto, J., Karth, I., Sun, Y., Chung, J. J. Y., Roemmele, M., & Kreminski, M. (2025) · Creativity and Cognition · doi:10.1145/3698061.3726915 · Cited by 8
“Recent years have seen especially strong growth in the adoption of AI-based CSTs—in creative domains as wide-ranging as writing [45], visual art [10, 54, 71], game design [29, 40], fashion design [35], worldbuilding [15], and music [49].”
Self-citationZheng, C., Yu, Z., Jiang, Y., Zhang, M., Lu, X., Jin, J., & Gao, L. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713274 · Cited by 7
“To address these challenges, our study draws inspiration from Mina Lee’s works [34, 35], particularly her focus on evaluating the writing capabilities of LLMs (GPT-3) through HCI datasets at CHI 2022, and her subsequent proposal of a design space for systematically exploring intelligent interactive…”
User-centric Reflective Writing Assistance: Leveraging RAG for Enhanced Personalized Support
Neshaei, S. P., Tashkovska, M., Mejia‐Domenzain, P., Wambsganß, T., & Käser, T. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719899 · Cited by 7
“We followed the standards established in prior writing assistants [13] to implement the suggestions in our design.”
InfluentialSelf-citationLLMs as Research Tools: A Large Scale Survey of Researchers' Usage and Perceptions
Liao, Z., Antoniak, M., Cheong, I., Cheng, E. Y.-Y., Lee, A.-H., Lo, K., Chang, J. C., & Zhang, A. X. (2024) · arXiv · doi:10.48550/arxiv.2411.05025 · Cited by 7
Self-citationNabokov's Cards: An AI Assisted Prewriting System to Support Bottom-Up Creative Writing
Carrera, D., Zhao, Z., Ajin Thomas, A., & Wigdor, D. (2025) · Proceedings of the 2025 Conference on Creativity and Cognition · doi:10.1145/3698061.3726916 · Cited by 6
Visual Story-Writing: Writing by Manipulating Visual Representations of Stories
Masson, D., Zhao, Z., & Chevalier, F. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747758 · Cited by 6
“Instead, most writing support tools ask users to manipulate narratives using text, typically through prompts or suggestions [15, 16, 24, 42, 65, 80].”
A Design Space for Intelligent Dialogue Augmentation
Chan, R. S. M., Marx, A., Kim, A., & El‐Assady, M. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712096 · Cited by 6
“Lee et al. [81] refer to this approach as value-sensitive de-sign , which can be guided by a comprehensive design space for dialogue augmentation such as the one we propose.”
InfluentialNavigating Generative AI Disclosure, Ownership, and Accountability in Co-Creative Domains
Do, H. J., Feldman, M. Q., He, J., Hwang, A. H.-C., & Kim, S. (2025) · Symposium on Human-Computer Interaction for Work · doi:10.1145/3707640.3729212 · Cited by 8
Story Forge: A Card-Based Framework for AI-Assisted Interactive Storytelling
Yu, Y., Corino, G., & Phillips, M. (2025) · Electronics · doi:10.3390/electronics14152955 · Cited by 9
“Many NLP-focused tools emphasize AI-driven autonomous generation, often at the expense of user interaction, which leads to limited user control and narratives lacking emotional and cultural depth [5–7].”
InfluentialAI in the Writing Process: How Purposeful AI Support Fosters Student Writing
Siddiqui, M. N., Feliciano, V. A., Pea, R. D., & Subramonyam, H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98459-4_14 · Cited by 11
Self-citationDing, Z., Brachman, M., Chan, J., & Geyer, W. (2025) · Creativity and Cognition · doi:10.1145/3698061.3726935 · Cited by 6
“In AI-assisted data analysis, shared representations have been developed to support subtasks such as data cleaning [18, 26] and data visualization [36, 43, 44].”
Neshaei, S. P., Tolzin, A., Berkle, Y., Leuchter, M., Leimeister, J. M., Janson, A., & Wambsganß, T. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711023 · Cited by 7
“Similar to the findings of previous intelligent assistants using LLMs [48, 52, 56, 71], we believe the better results in ALure B come from the higher versatility and expression power of the responses from text generation model, prompting the students to reflect on their argumentative writing.”
InfluentialSelf-citationYuan, C., Wei, T., Li, C., Yi, X., Liu, S., Zhang, Z., Cai, Y., & Du, X. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00708-4 · Cited by 6
Toyteller: Toy-Playing with Character Symbols for AI-Powered Visual Storytelling
Chung, J. J. Y., Roemmele, M., & Kreminski, M. (2024) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3672539.3686781 · Cited by 5
Self-citationInk 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
Self-citationHuman-AI Interaction Patterns in Creative Domains and Their Time-Based Visualization
Leger, R., & Buschek, D. (2025) · Mensch und Computer (MuC) · doi:10.1145/3743049.3743081 · Cited by 5
Self-citationStorySage: 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
Xu, M., Li, X., Lan, T., Zeng, X., Wang, T., Sheng, S., Deng, X., Sun, J., Fiebrink, R. A., Bryan–Kinns, N., et al. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2025.2604649 · Cited by 5
Reactive Writers: How Co-Writing with AI Changes How We Engage with Ideas
Bhat, A., Quéré, M. A. L., Naaman, M., & Naaman, M. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791529 · Cited by 12
Self-citationEbrahimzadeh, M., Shibani, A., & Shum, S. B. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100609 · Cited by 5
Self-citationEndless forms most similar: the dearth of the author in AI-supported art
Kreminski, M. (2026) · AI & SOCIETY · doi:10.1007/s00146-025-02326-6 · Cited by 4
Self-citationInvestigating How Computer Science Researchers Design Their Co-Writing Experiences With AI
Roffarello, A. M., Calò, T., Scibetta, L., & Russis, L. D. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713205 · Cited by 5
Kim, Y., Le, T.-L. V., Kim, D., Lee, M., & Lee, S.-J. (2025) · Designing Interactive Systems Conference · doi:10.1145/3715336.3735740 · Cited by 13
Self-citationSynthia: Visually Interpreting and Synthesizing Feedback for Writing Revision
Zhang, C., Ju, K., Han, Z., Yen, Y., & Rzeszotarski, J. M. (2025) · ACM Symposium on User Interface Software and Technology · doi:10.1145/3746059.3747703 · Cited by 13
Exploring Mobile Touch Interaction with Large Language Models
Zindulka, T., Sekowski, J. M., Lehmann, F., & Buschek, D. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713554 · Cited by 4
“As a consequence, mobile users have to engage in cumbersome context switches , such as switching between their writing app and a browser or dedicated AI app (e.g. the ChatGPT app 3 ) to prompt the model.”
InfluentialSelf-citationCorpusStudio: Surfacing Emergent Patterns In A Corpus Of Prior Work While Writing
Dang, H., Swoopes, C., Buschek, D., & Glassman, E. L. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713974 · Cited by 7
Self-citationFanfiction in the Age of AI: Community Perspectives on Creativity, Authenticity and Adoption
Alfassi, R., Cooper, A., Mitchell, Z., Calabro, M., Shaer, O., & Mokryn, O. (2026) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2025.2531272 · Cited by 4
“Most importantly, we explore here the community’s perspective on the future of human-GenAI collaboration.”
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
Chen, Y., & Xu, Y. (2025) · Journal of Engineering Design · doi:10.1080/09544828.2025.2546840 · Cited by 3
Tracing Creativity: A Design Space For Creative Activity Traces in HCI
Hammad, N., Lin, D. C.-E., Smith, A., Kreminski, M., Harpstead, E., & Hammer, J. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791263 · Cited by 10
Self-citationNishal, S., Lee, M., Diakopoulos, N. A., & Vaughan, J. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790372 · Cited by 3
Self-citationDeceptive Patterns of Intelligent and Interactive Writing Assistants
Benharrak, K., Zindulka, T., & Buschek, D. (2024) · Workshop on Intelligent and Interactive Writing Assistants (In2Writing) · doi:10.1145/3690712.3690728 · Cited by 3
Self-citationAI-Assisted Writing in Education: Ecosystem Risks and Mitigations
Shibani, A., & Shum, S. B. (2024) · Workshop on Intelligent and Interactive Writing Assistants (In2Writing) · doi:10.1145/3690712.3690714 · Cited by 2
“In a recent review that mapped the design space of IIWAs [5], the ecosystem dimensions were identified as important, and involve: “the overarching sociotechnical context in which the writer and the tool are situated, encompassing a range of complex, interdependent actors that frequently play a role…”
InfluentialSelf-citationNews Futures: (Re-)Designing Socio-technical Systems for News Production and Consumption
Nishal, S., Quéré, M. A. L., McInnis, B. J., Jones, B., Vaccaro, K., Aitamurto, T., Naaman, M., & Diakopoulos, N. A. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3706733 · Cited by 2
Designing Human-AI Collaboration to Support Learning in Counterspeech Writing
Ding, X.-H., Ping, K.-K., Gunturi, U. S., Çarık, B., Stil, S., Wilhelm, L. T., Daryanto, T., Hawdon, J., Lee, S. W., & Rho, E. (2024) · IEEE Symposium on Visual Languages / Human-Centric Computing Languages and Environments · doi:10.1109/vl-hcc65237.2025.00052 · Cited by 2
“In the field of co-writing with AI, researchers have shed light on aspects of user engagement, adaptation, and reliance on AI-generated content [30], [35], [37].”
Self-citationHerding CATs: Making Sense of Creative Activity Traces
Kreminski, M., Smith, A., Chung, J. J. Y., Son, K., Yang, Q., Lee, S. W., Hammad, N., Rawn, E., Almeda, S. G., & Zamfirescu-Pereira, J. D. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3778743 · Cited by 2
Self-citationFu, 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
“Note that we used the sense of ownership to refer to the writer’s “sense of possession” over the resulting narratives within the system, even the AI-generated artifacts [32, 43, 51, 89].”
InfluentialFrom 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
Self-citationFrom 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
“Many studies have used the planning-drafting-revision framework to investigate AI writing assistance [23, 35, 48, 57], suggesting its utility as an analytical framework.”
Self-citationFitzsimons, A. (2024) · Creativity and Cognition · doi:10.1145/3635636.3664625 · Cited by 2
“We also plan to contribute an important theoretical contribution: empirical bounds of what some have termed an “interaction metaphor” [11] (explored in RQ3).”
BackgroundDr. Ping and Dr. Pong: Rethinking Writing and Work with Playful Embodied AIs
Börütecene, A., & Buruk, O. (2024) · Halfway to the Future Symposium · doi:10.1145/3686169.3686214 · Cited by 2
Design of Knowledge Graphs for Interactive Digital Narratives Authoring
Abhilash, M., & Nack, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78450-7_18 · 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
Chen, Y., Xiao, Y., Zinat, K. T., Yamashita, N., Gao, G., & Liu, Z. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713693 · Cited by 2
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
“Dhillon et al. emphasized the importance of personalized systems to accommodate the diverse experiences of writers [28], and Lee et al. provided broad recommendations for the development of future AI tools [57].”
Structuring GenAI-assisted Hypotheses Exploration with an Interactive Shared Representation
Ding, Z., Brachman, M., Chan, J., & Geyer, W. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708557.3716364 · Cited by 1
“For instance, DirectGPT [12] uses a shared representation of text, code, and images that both the AI and human user can interact through a direct manipulation interface, transforming user actions into engineered prompts.”
SYNthia: An Interface Concept for Writing With Large Language Models
Liang, I., & Gero, K. I. (2025) · Creativity and Cognition · doi:10.1145/3698061.3734401 · Cited by 3
Self-citationUnderstanding How Paper Writers Use AI-Generated Captions in Figure Caption Writing
Ng, H. Y., Hsu, T.-Y., Min, J., Kim, S., Rossi, R. A., Yu, T., Jung, H., & Huang, T.-H. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-8912-5_8 · Cited by 1
Supporting Material Writing Practice with Phraselette, a Palette of Phrases
Calderwood, A., Kim, T., Sun, Y., Roemmele, M., Chung, J. J. Y., & Kreminski, M. (2025) · Designing Interactive Systems Conference · doi:10.1145/3715668.3735608 · Cited by 1
“…[13, 22, 32, 36]; new media scholarship on the materiality of digital text [4, 17, 21], computational text criticism [18, 30, 33], from prior computational writing support tools [5, 6, 8, 14–16, 24, 27, 35, 38, 39]; and from critical engagement with language models [1–3, 7, 10, 11, 23, 31].”
Self-citationLloyd, T., Nguyen, T., Levy, K. E. C., & Naaman, M. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791889 · Cited by 7
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
Preventing Accidental Sharing of Misinformation Using Large Language Models
Franco, M., Grimm, V., & Herder, E. (2025) · International Conference on Information Technology for Social Good (GoodIT) · doi:10.1145/3748699.3749798 · Cited by 1
“Lee et al. [23] provide an extensive discussion on designing intelligent and interactive writing assistants.”
Learning in the Loop: AI, Feedback, and the Future of Human Thought
Dingler, T. (2026) · Springer eBooks · doi:10.1007/978-981-95-1622-3_9 · Cited by 1
A Linguistic Negotiation for Consensus-Making among LLM Agents with Fuzzy Utility Functions
Yihan, D. O. N. G., & Itō, T. (2026) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2025edp7179 · Cited by 1
Chakrabarty, T., & Dhillon, P. S. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791276 · Cited by 6
“They have significantly changed how people write across various 3 https://github.com/tuhinjubcse/GoodWritingBeGenerative fields [37], with studies showing that 10-24% of content in areas like consumer complaints, business communications, job listings, and UN press statements now involves LLM…”
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.
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 ›
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