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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]
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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]
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“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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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
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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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Items 201–240 of 240
Empirical Research Methods for Human-Computer Interaction
MacKenzie, I. S., & Castellucci, S. J. (2016) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2851581.2856671 · Cited by 9 · 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”
Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing
Afrin, T., & Litman, D. J. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.193 · Cited by 9
Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks
Kim, Z. M., Du, W., Raheja, V., Kumar, D., & Kang, D. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.678 · Cited by 8
Self-citationALTER: Auxiliary Text Rewriting Tool for Natural Language Generation
Xu, Q., Xu, C., & Qu, L. (2019) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/d19-3003 · Cited by 8
Machine-in-the-Loop Rewriting for Creative Image Captioning
Padmakumar, V., & He, H. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.42 · Cited by 7 · cited 4×
“The most common mechanism is to use text formatting such”
InfluentialTEASPN: Framework and Protocol for Integrated Writing Assistance Environments
Hagiwara, M., Ito, T., Kuribayashi, T., Suzuki, J., & Inui, K. (2019) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/d19-3039 · Cited by 7 · cited 1×
“The most common mechanism is to use text formatting such”
Fiction-Writing Mode: An Effective Control for Human-Machine Collaborative Writing
Zhong, W., Naradowsky, J., Takamura, H., Kobayashi, I., & Miyao, Y. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.128 · Cited by 7 · 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”
Effidit: An Assistant for Improving Writing Efficiency
Shi, S., Zhao, E., Bi, W., Cai, D., Cui, L., Huang, X., Jiang, H., Tang, D., Song, K., Wang, L., et al. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-demo.49 · Cited by 6 · cited 3×
“Outputs that are novel and diverse, providing inspiration or surprises to the user Explainability Additional information for outputs that explains the rationale behind system outputs Bias Outputs that exhibit various forms of bias, such as skewed perspectives on topics and societal stereotypes…”
Huang, Y., Wang, H., & Hsu, J. Y.-J. (2018) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3272973.3274069 · Cited by 6 · cited 1×
“Analytical writing provides in-depth examination, which benefits from reflection and iteration [57, 113].”
Modeling the Relationship between User Comments and Edits in Document Revision
Zhang, X., Rajagopal, D., Gamon, M., Jauhar, S. K., & Lu, C. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1505 · Cited by 6 · cited 2×
“…(team member), Pao Siangliu-lue (advisor), Joseph Chee Chang (advisor), Max Kreminski (advisor), Shannon Zejiang Shen (advisor), Daniel Buschek (advisor) • Ecosystem : Simon Buckingham Shum (team lead), Madiha Zahrah Choksi (team member), Antonette Shibani (team member), Simon Knight (team member)”
Towards Explainable AI Writing Assistants for Non-native English SpeakersPreprint
Kim, Y., Lee, M., Kim, D., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2304.02625 · Cited by 5
Self-citationMori, Y., Yamane, H., Shimizu, R., & Harada, T. (2022) · Workshop on Intelligent and Interactive Writing Assistants (In2Writing) · doi:10.18653/v1/2022.in2writing-1.6 · Cited by 5 · cited 1×
“…(team member), Pao Siangliu-lue (advisor), Joseph Chee Chang (advisor), Max Kreminski (advisor), Shannon Zejiang Shen (advisor), Daniel Buschek (advisor) • Ecosystem : Simon Buckingham Shum (team lead), Madiha Zahrah Choksi (team member), Antonette Shibani (team member), Simon Knight (team member)”
DRAG: Director-Generator Language Modelling Framework for Non-Parallel Author Stylized Rewriting
Singh, H., Verma, G., Garimella, A., & Srinivasan, B. V. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.73 · Cited by 5 · 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”
SongRewriter: A Chinese Song Rewriting System with Controllable Content and Rhyme Scheme
Sun, Y., Li, L., Liu, Q., & Yeung, D. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.814 · Cited by 5
Semi-Automated Approach for Evaluating Severe Weather Risk Communication
Jit, S. S., Spinney, J. A., Chandra, P., & Soden, R. (2023) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3544549.3585753 · Cited by 5 · 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”
Common sense assistant for writing stories that teach social skills
Kim, K.-H., Picard, R. W., & Lieberman, H. A. (2008) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/1358628.1358765 · Cited by 5 · cited 3×
“Accessibility describes tasks that aim to support inclusivity; for example helping neurodivergent individuals explain and write about common social situations [134].”
Context-Aware Abbreviation Expansion Using Large Language Models
Cai, S., Venugopalan, S., Tomanek, K., Narayanan, A., Morris, M. R., & Brenner, M. P. (2022) · arXiv · doi:10.48550/arxiv.2205.03767 · Cited by 4
Self-citationTo Revise or Not to Revise: Learning to Detect Improvable Claims for Argumentative Writing Support
Skitalinskaya, G., & Wachsmuth, H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.880 · Cited by 4
Supporting Spanish Writers using Automated Feedback
Cahill, A., Bruno, J. V., Ramey, J., Meneses, G. A., Blood, I., Tolentino, F., Lavee, T., & Andreyev, S. (2021) · doi:10.18653/v1/2021.naacl-demos.14 · Cited by 4 · cited 5×
“They provide traditional GUI elements (e.g., checkboxes, buttons) that spread out system capabilities rather than centralizing them into one“agent.”
InfluentialSmart Word Suggestions for Writing Assistance
Wang, C., Mao, S., Ge, T., Wu, W., Wang, X., Xia, Y., Tien, J., & Zhao, D. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.712 · Cited by 4
Motivating expressive writing with a text-to-sound application
Gonzales, A. L., Ng, T., Zhao, O., & Gay, G. K. (2010) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1753326.1753618 · Cited by 4 · cited 2×
“Another common purpose is telling a story; narrative applies to tasks whose goal is to convey an account of real [90] and imagined experiences [88, 109, 211].”
ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews
D’Arcy, M., Ross, A., Bransom, E., Kuehl, B., Bragg, J., Hope, T., & Downey, D. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.377 · Cited by 4
Automatic Document Sketching: Generating Drafts from Analogous Texts
Wu, Z., Galley, M., Brockett, C., Zhang, Y., & Dolan, B. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.185 · Cited by 3 · cited 1×
“…(team member), Pao Siangliu-lue (advisor), Joseph Chee Chang (advisor), Max Kreminski (advisor), Shannon Zejiang Shen (advisor), Daniel Buschek (advisor) • Ecosystem : Simon Buckingham Shum (team lead), Madiha Zahrah Choksi (team member), Antonette Shibani (team member), Simon Knight (team member)”
CroVeWA: Crosslingual Vector-Based Writing Assistance
Soyer, H., Topić, G., Stenetorp, P., & Aizawa, A. (2015) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/v1/n15-3019 · Cited by 3 · cited 1×
“Finally, there are custom designs: for example, a tangible UI that triggers the system by lifting a coffee cup [12], or an exploratory visualization for referencing information which has no user text input [131].”
Demonstrating Par4Sem - A Semantic Writing Aid with Adaptive Paraphrasing
Yimam, S. M., & Biemann, C. (2018) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/d18-2009 · Cited by 3 · cited 3×
“…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 may currently not be enough consideration of the economic and…”
Content Explorer: Recommending Novel Entities for a Document Writer
Łukasik, M., & Zens, R. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1374 · Cited by 2 · cited 2×
“Finally, most ecosystem dimensions are, as previously noted, under-represented, representing a rich area of future study as writing assistants become more widely adopted and the circumstance and context of their adoption becomes increasingly important.”
First demonstration of the intelligent reviewer's assistant
Dong, R., McCarthy, K., O’Mahony, M. P., Schaal, M., & Smyth, B. (2012) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/2166966.2167041 · Cited by 2 · cited 1×
“Lastly, feedback describes tasks that provide evaluative comments, such as customer reviews [13, 63] and constructive responses [220].”
Non-uniform Language Detection in Technical Writing
Wang, W., Moh’d, A., Islam, A., Soto, A. J., & Milios, E. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1194 · Cited by 2 · 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”
Using Vocabularies to Collaboratively Create Better Plans for Writing Tasks
Kaur, H., Williams, A. C., Thompson, A. L., Lasecki, W. S., Iqbal, S. T., & Teevan, J. B. (2018) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3170427.3188640 · Cited by 1 · cited 1×
“Rather, our writing stages resemble (yet are not the same as) the stage models of writing, such as Britton et al. [21], Rohman [213], that model the growth of the written artifact rather than the inner workings of the writer.”
ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews
D'Arcy, M., Ross, A., Bransom, E., Kuehl, B., Bragg, J., Hope, T., & Downey, D. (2023) · arXiv · doi:10.48550/arxiv.2306.12587 · Cited by 1 · cited 1×
“…(team member), Pao Siangliu-lue (advisor), Joseph Chee Chang (advisor), Max Kreminski (advisor), Shannon Zejiang Shen (advisor), Daniel Buschek (advisor) • Ecosystem : Simon Buckingham Shum (team lead), Madiha Zahrah Choksi (team member), Antonette Shibani (team member), Simon Knight (team member)”
One Document, Many Revisions: A Dataset for Classification and Description of Edit Intents
Rajagopal, D., Zhang, X., Gamon, M., Jauhar, S. K., Yang, D., & Hovy, E. H. (2022) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/554i2asrmmsw · Cited by 1 · cited 1×
“…(team member), Pao Siangliu-lue (advisor), Joseph Chee Chang (advisor), Max Kreminski (advisor), Shannon Zejiang Shen (advisor), Daniel Buschek (advisor) • Ecosystem : Simon Buckingham Shum (team lead), Madiha Zahrah Choksi (team member), Antonette Shibani (team member), Simon Knight (team member)”
THE CRITICAL INCIDENT METHOD IN MANAGER EDUCATION AND DEVELOPMENT.
James, J. (1975) · Academy of Management Proceedings · doi:10.5465/ambpp.1975.4975820 · Cited by 1
OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.08774
Self-citationBLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Workshop, B., :, Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ilić, S., Hesslow, D., Castagné, R., Luccioni, A. S., et al. (2022) · arXiv · doi:10.48550/arxiv.2211.05100
Self-citationHomogenization Effects of Large Language Models on Human Creative Ideation
Anderson, B. R., Shah, J. H., & Kreminski, M. (2024) · arXiv · doi:10.48550/arxiv.2402.01536 · cited 1×
“The most common mechanism is to use text formatting such”
Self-citationGraphologue: Exploring Large Language Model Responses with Interactive Diagrams
Jiang, P., Rayan, J., Dow, S. P., & Xia, H. (2023) · arXiv · doi:10.48550/arxiv.2305.11473 · cited 1×
“This is especially important in sensitive contexts, such as education, counseling, or healthcare.”
Pre-Writing: The Stage of Discovery in the Writing Process
Rohman, D. G. (1965) · College Composition & Communication · doi:10.58680/ccc196521081
AMesure: A Web Platform to Assist the Clear Writing of Administrative Texts
François, T., Müller, A., Rolin, E., & Norré, M. (2020) · Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.aacl-demo.1 · cited 2×
“The most common mechanism is to use text formatting such”
Document similarity measures to distinguish native vs. non-native essay writers
Gurevich, O., & Deane, P. (2007) · Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers on XX - NAACL '07 · doi:10.3115/1614108.1614121
Emotional Intensity Estimation based on Writer’s Personality
Suzuki, H., Tarumoto, S., Kajiwara, T., Ninomiya, T., Nakashima, Y., & Nagahara, H. (2022) · Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: Student Research Workshop · doi:10.18653/v1/2022.aacl-srw.1 · cited 1×
“In particular, we consider data, models, learning problems and techniques, and evaluation, all of which play a crucial role in determining the quality and degree of intelligence in the writing assistants.”
Flagged first, then by citations
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 ›
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