The DOI matches its Crossref record; 238 of the 240 references it cites were found in a registry or an index, and 1 are not checked yet.
A Design Space for Intelligent and Interactive Writing Assistants
Open accessAbstract
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.
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
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
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
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173 · cited 1×
“…for a task Availability User’s expectation of the writing assistant being at hand when one needs or wants to use it Privacy User’s concerns about how their data is handled by the writing assistant Transparency User’s understanding of the writing assistant’s mechanism, capabilities, and limitations”
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325 · cited 1×
“…for a task Availability User’s expectation of the writing assistant being at hand when one needs or wants to use it Privacy User’s concerns about how their data is handled by the writing assistant Transparency User’s understanding of the writing assistant’s mechanism, capabilities, and limitations”
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568 · cited 1×
“To highlight a few notable trends, we see audience is under-represented compared to the other task dimensions, suggesting future work may want to more explicitly consider who is the audience of a piece of writing. overall, as well as relative to other technology dimensions, suggesting that there…”
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 · cited 1×
“…for a task Availability User’s expectation of the writing assistant being at hand when one needs or wants to use it Privacy User’s concerns about how their data is handled by the writing assistant Transparency User’s understanding of the writing assistant’s mechanism, capabilities, and limitations”
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344 · cited 1×
“…for a task Availability User’s expectation of the writing assistant being at hand when one needs or wants to use it Privacy User’s concerns about how their data is handled by the writing assistant Transparency User’s understanding of the writing assistant’s mechanism, capabilities, and limitations”
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515 · cited 1×
“…for a task Availability User’s expectation of the writing assistant being at hand when one needs or wants to use it Privacy User’s concerns about how their data is handled by the writing assistant Transparency User’s understanding of the writing assistant’s mechanism, capabilities, and limitations”
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929 · cited 1×
“…for a task Availability User’s expectation of the writing assistant being at hand when one needs or wants to use it Privacy User’s concerns about how their data is handled by the writing assistant Transparency User’s understanding of the writing assistant’s mechanism, capabilities, and limitations”
Scaling Laws for Neural Language Models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., & Amodei, D. (2020) · arXiv · doi:10.48550/arxiv.2001.08361 · Cited by 9,550 · cited 1×
“…for a task Availability User’s expectation of the writing assistant being at hand when one needs or wants to use it Privacy User’s concerns about how their data is handled by the writing assistant Transparency User’s understanding of the writing assistant’s mechanism, capabilities, and limitations”
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058 · cited 1×
“To highlight a few notable trends, we see audience is under-represented compared to the other task dimensions, suggesting future work may want to more explicitly consider who is the audience of a piece of writing. overall, as well as relative to other technology dimensions, suggesting that there…”
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022) · arXiv · doi:10.48550/arxiv.2201.11903 · Cited by 4,263
Self-citation
Research works printed but not linked: 86
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. arXiv preprint arXiv:2005.14165 (2020). · arXiv
- Daniel Buschek, Martin Zürn, and Malin Eiband. 2021. The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English Writers. In Conference on Human Factors in Computing Systems (CHI).
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- 10.3115/v1 · DOI
- 10.3115/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- Susan D’Agostino. 2023. ChatGPT Advice Academics Can Use Now. https://www.insidehighered.com/news/2023/01/12/academic-experts-offer-advice-chatgpt Accessed: Jan 26, 2024.
- 10.3115/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- Michael Gamon. 2010. Using Mostly Native Data to Correct Errors in Learners’ Writing. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, Los Angeles, California, 163–171. https://aclanthology.org/N10-1019
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- Tobias Moritz Guggenberger, Frederik Möller, Tim Haarhaus, Inan Gür, and Boris Otto. 2020. Ecosystem Types in Information Systems. In Twenty-Eighth European Conference on Information Systems (ECIS2020). https://aisel.aisnet.org/ecis2020_rp/45
- Harry Halpin, Johanna D. Moore, and Judy Robertson. 2004. Automatic Analysis of Plot for Story Rewriting. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Barcelona, Spain, 127–133. https://aclanthology.org/W04-3217
- 10.18653/v1 · DOI
- J.R. Hayes. 1996. A new framework for understanding cognition and affect in writing. In The Science of Writing: Theories, Methods, Individual Differences, and Applications, C.M. Levy and S. Randall (Eds.). Lawrence Erlbaum Associates, Mahwah, NJ, 6–44.
- Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean. 2015. Distilling the Knowledge in a Neural Network. In NIPS Deep Learning and Representation Learning Workshop.
- Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022. LoRA: Low-Rank Adaptation of Large Language Models. In International Conference on Learning Representations (ICLR).
- Chung-chi Huang, Ping-che Yang, Keh-jiann Chen, and Jason S. Chang. 2012. TransAhead: A Computer-Assisted Translation and Writing Tool. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Montréal, Canada, 352–356. https://aclanthology.org/N12-1036
- Chung-chi Huang, Ping-che Yang, Mei-hua Chen, Hung-ting Hsieh, Ting-hui Kao, and Jason S. Chang. 2012. TransAhead: A Writing Assistant for CAT and CALL. In Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, Avignon, France, 16–19. https://aclanthology.org/E12-2004
- Justin Hutchens. 2023. The Language of Deception: Weaponizing Next Generation AI. John Wiley & Sons.
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- Juho Kim. 2022. Interaction-Centric AI (NeurIPS 2022 Keynote). https://slideslive.com/38996064/interactioncentric-ai Accessed: Jan 26, 2024.
- 10.18653/v1 · DOI
- Tomi Kinnunen, Henri Leisma, Monika Machunik, Tuomo Kakkonen, and Jean-Luc LeBrun. 2012. SWAN - Scientific Writing AssistaNt. A Tool for Helping Scholars to Write Reader-Friendly Manuscripts. In Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, Avignon, France, 20–24. https://aclanthology.org/E12-2005
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Alexander Cosgrove, Christopher D Manning, Christopher Re, Diana Acosta-Navas, Drew Arad Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue WANG, Keshav Santhanam, Laurel Orr, Lucia Zheng, Mert Yuksekgonul, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Andrew Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, and Yuta Koreeda. 2023. Holistic Evaluation of Language Models. Transactions on Machine Learning Research (2023). https://openreview.net/forum?id=iO4LZibEqW Featured Certification, Expert Certification.
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- Charles A MacArthur. 2006. The effects of new technologies on writing and writing processes. Handbook of writing research (2006), 248–262.
- 10.1080/07370024.1991.9667168 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- Writers Guild of America. 2023. What We Won. https://www.wgacontract2023.org/the-campaign/what-we-won Accessed: Dec. 12, 2023.
- David R Olson. 2002. What writing does to the mind. Language, literacy, and cognitive development: The development and consequences of symbolic communication (2002), 153–166.
- 10.18653/v1 · DOI
- Vishakh Padmakumar and He He. 2024. Does Writing with Language Models Reduce Content Diversity?. In The Twelfth International Conference on Learning Representations. https://openreview.net/forum?id=Feiz5HtCD0
- 10.3102/0091732X014001277 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- Denise Schmandt-Besserat. 1992. Before writing, vol. I: from counting to cuneiform. Vol. 1. University of Texas press.
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- 10.3115/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- Elizabeth Sulzby, William H Teale, and George Kamberelis. 1989. Emergent writing in the classroom: Home and school connections. Emerging literacy: Young children learn to read and write (1989), 63–79.
- 10.18653/v1 · DOI
- Ilya Sutskever Oriol Vinyals and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems (NeurIPS). 3104–3112.
- 10.18653/v1 · DOI
- Rohan Taori and Tatsunori Hashimoto. 2023. Data feedback loops: Model-driven amplification of dataset biases. In International Conference on Machine Learning. PMLR, 33883–33920.
- Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. LLaMA: Open and Efficient Foundation Language Models. arXiv (2023).
- Eric L Trist. 1981. The evolution of socio-technical systems. Vol. 2. Ontario Quality of Working Life Centre Toronto.
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- Florian Weber, Thiemo Wambsganss, Dominic Rüttimann, and Matthias Söllner. 2021. Pedagogical agents for interactive learning: A taxonomy of conversational agents in education. In Forty-Second International Conference on Information Systems. Austin, Texas.
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- Chin yew Lin and Marina Rey. 2004. Looking for a Few Good Metrics: ROUGE and its Evaluation. In NTCIR Workshop.
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
- 10.18653/v1 · DOI
Cited by
Sorted by citations
Homogenization Effects of Large Language Models on Human Creative Ideation
Anderson, B. R., Shah, J. H., & Kreminski, M. (2024) · Creativity and Cognition · doi:10.1145/3635636.3656204 · Cited by 188
Self-citationBuilding machines that learn and think with people
Collins, K. M., Sucholutsky, I., Bhatt, U., Chandra, K., Wong, L., Lee, M., Zhang, C. E., Zhi‐Xuan, T., Ho, M. K., Mansinghka, V. K., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01991-9 · Cited by 125
“This sociotechnical ecosystem may be dictated by external regulations, organizational practices, or other principles 73,212–215 Article 14 of the EU AI Act requires users of high-risk AI systems “to correctly interpret the high-risk AI system’s output” and “to remain aware of the possible tendency…”
Self-citationHalkiopoulos, C., Gkintoni, E., Aroutzidis, A., & Antonopoulou, H. (2025) · Diagnostics · doi:10.3390/diagnostics15040456 · Cited by 109
Quantifying large language model usage in scientific papers
Liang, W., Zhang, Y., Wu, Z., Lepp, H., Ji, W., Zhao, X., Cao, H., Liu, S., He, S., Huang, Z., et al. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02273-8 · Cited by 80
“A technology that can generate dominant varieties of scholarly English could hypo-thetically lower barriers to entry 14,15 .”
Writer-Defined AI Personas for On-Demand Feedback Generation
Benharrak, K., Zindulka, T., Lehmann, F., Heuer, H., & Buschek, D. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642406 · Cited by 63
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.
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
