Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

PromptCanvas: Composable Prompting Workspaces Using Dynamic Widgets for Exploration and Iteration in Creative Writing

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read PromptCanvas turns prompts into persistent, adjustable widgets on an infinite canvas, and in a lab study this interface outperformed a conversational UI on the Creativity Support Index while reducing mental demand and frustration.

desk verdict A solid system-level evaluation of dynamic widgets for creative writing, with an under-specified baseline and overclaimed causal framing; deserves peer review. read the letter →

arxiv 2506.03741 v1 pith:XMFKM3X6 submitted 2025-06-04 cs.HC cs.CL

Rifat Mehreen Amin , Oliver Hans Kühle , Daniel Buschek , Andreas Butz This is my paper
classification cs.HCcs.CL
keywords dynamicwidgetspromptinglargelanguagemodelshuman-AIco-creationcreativitysupportcognitiveloadcreativewritingconversationalUI
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper aims to show that how writers prompt a large language model is itself a design problem: prompts that are turned into persistent, adjustable widgets on an infinite canvas support creativity and reduce cognitive load compared with ordinary chat turns. In a within-subject lab study with 18 participants, PromptCanvas beat a ChatGPT-style conversational UI on every factor of the Creativity Support Index, with an overall score of 82.09 versus 61.65 (p = 0.005), and on NASA-TLX it produced significantly lower mental demand and frustration. A two-week field study with 10 participants yielded a similar overall creativity score (79.73) and a System Usability Scale average of 86.50, suggesting the effect is not limited to a single lab session. A sympathetic reader would take this as evidence that widget-based decomposition of prompts is a better interface pattern for human-AI co-writing than free-form chat.

What carries the argument

The load-bearing object is the dynamic widget: a small interactive panel with a title, a current value, and a list of alternative values, tied to one attribute of the text under revision. It carries the argument by converting an ephemeral prompt into a persistent, manipulable interface object: the label-value pairs of all active widgets are sent together with the draft to the same language model that generates the text, so the user's specifications become reusable state rather than one-shot chat history. The surrounding mechanism is the infinite canvas and the widget panel, which let users generate, arrange, cluster, and discard widgets without losing the workflow, and the rephrasing pipeline that streams revised text back into the editor.

What would settle it

Run a larger preregistered within-subject study in which the chat baseline is augmented with persistent prompt history, editable previous messages, and one-click suggestion options; if the roughly 20-point Creativity Support Index gap and the NASA-TLX differences disappear or reverse, the claim that dynamic widgets themselves improve creative writing support would be falsified.

Watch

Extended reading notes

Core claim

PromptCanvas's central claim is that dynamic widgets make prompting composable: each widget represents one attribute of the text (such as tone, length, or a character name), carries a current value and a set of alternatives, and can be created from system suggestions, a user prompt, or an empty double-click on the canvas. Once placed on the canvas, active widgets are converted into label-value pairs and sent to the language model to rephrase the draft, so the prompt does not evaporate after one exchange but remains visible and editable as the writing evolves. The paper reports that this design outperformed a conversational baseline in the lab, with an overall Creativity Support Index of 82.09 against 61.65, significant differences favoring PromptCanvas on mental demand (1.89 vs 3.06, p = 0.02) and frustration (1.28 vs 2.17, p = 0.03), and far fewer prompts needed (4.0 vs 11.1, p = 0.0006). Eighty-nine percent of participants preferred PromptCanvas, and the two-week field study echoed the creativity results, with participants also using the canvas for programming and multilingual writing.

Load-bearing premise

The comparative claim rests on the 18-person lab study and on the assumption that the ChatGPT-style chat interface built as the baseline is a fair, representative control rather than a deliberately plain one; the two-week field study had no baseline, so it cannot independently support the relative advantage.

Editorial extensions

If this is right

  • If PromptCanvas is right, chat is not the default interface for LLM writing support; persistent widgetized prompts are a more effective pattern for open-ended creative tasks.
  • Users needing roughly one-third the number of prompts (4.0 vs 11.1) implies widget canvases can compress iterative refinement into fewer, more targeted model calls.
  • Lower mental demand and frustration on NASA-TLX implies the visual persistence of prompt variables reduces metacognitive load, not just user preference.
  • The field-study use of widgets for programming and non-English writing suggests the pattern generalizes beyond creative writing to other LLM-assisted workflows.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A factorial follow-up could separate the effects of persistence, spatial layout, and suggestion content; this study does not isolate which component of the widget format carries the creativity gain.
  • The widget pattern could be extended to code generation and image generation, where attributes such as model, seed, or style could become widgets on a canvas instead of arguments buried in a prompt.
  • Because widget values and layouts are structured, they could be logged and shared as reusable prompt workflows, turning prompt engineering from throwaway utterances into compositional, named components.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper introduces PromptCanvas, a workspace for LLM-based creative writing in which prompts are decomposed into interactive 'dynamic widgets' placed on an infinite canvas. Widgets can be system-suggested, user-prompted, or manually created, and each widget controls an attribute of the generated text (tone, names, plot elements, etc.). The authors report a within-subject lab study (N=18) comparing PromptCanvas with a custom chat-based 'conversational UI', finding significantly higher Creativity Support Index scores and lower NASA-TLX mental demand and frustration for PromptCanvas, alongside qualitative reports of improved exploration and control. A two-week field study (N=10) without a baseline reports high CSI and SUS scores. The paper frames these results as answering RQ2/RQ3, i.e., that dynamic widgets improve creativity support and reduce cognitive load relative to conversational UIs.

Significance. If the comparative claims held, PromptCanvas would be a useful design pattern for LLM writing tools: modular, persistent widgets that make prompt attributes visible and editable could support decomposition of tasks, iterative exploration, and a greater sense of control. The paper's strengths include a within-subject, counterbalanced lab design using the same underlying LLM (gpt-4o-2024-08-06) in both conditions, standardized instruments (CSI, NASA-TLX, SUS), Bonferroni-Holm correction, and a clearly described system implementation. The qualitative analysis is rich and gives concrete insight into how users appropriate widget-based interfaces. However, the central comparative claim is undermined by confounds between conditions and by an insufficiently specified baseline, so the significance of the specific RQ2/RQ3 conclusions is currently provisional.

major comments (4)
  1. [§5.2 and §4.2] The baseline conversational UI is not specified to the same level as PromptCanvas. For PromptCanvas the paper states a sampling temperature of 1.06 (§4.2) and gives the full system prompts for each backend service (Tables 4–8 referenced in §4.2.1–4.2.5), including an explicit instruction that natural-language editing must return the complete modified text (§4.2.5). For the baseline, the paper reports neither the temperature nor the system prompt, nor how regeneration/rephrasing handles the previous message. Without matched parameters, the significant differences in CSI and NASA-TLX (§6.2, §6.3) could be driven by backend configuration rather than by the dynamic-widget concept. Please report the baseline's prompts, temperature, and output-completeness behavior, or run a comparison with matched settings.
  2. [§5.2, §4.1, and §6.2–6.3] The two conditions differ in many features beyond dynamic widgets: PromptCanvas includes an infinite canvas, a directly editable text editor with history and word counts, a widget panel, and a dedicated 'rephrase based on widgets' action, while the baseline chat UI offers only copy and edit-message functionality. Consequently, RQ2 and RQ3 ('Do dynamic widgets ... improve creativity support / reduce cognitive load?') are not answered by this design; at best the study shows a system-level comparison. To support the widget-specific claim, the authors should either add a condition that isolates the widgets (e.g., a chat UI augmented with the same editor and history features, or a widget-based UI without the canvas), or explicitly reframe RQ2/RQ3 as comparisons of PromptCanvas as a whole against a chat UI and temper the causal language accordingly.
  3. [Abstract and §5.4] The abstract states that the field study (N=10) 'confirmed these results', but the field study has no baseline condition and only measures PromptCanvas itself (CSI and SUS, §5.4.1, Table 3). It cannot confirm relative superiority over a conversational UI. The relative claim rests entirely on the 18-participant lab comparison. The authors should either add a comparative baseline to the field study or revise the abstract and §5.4 to state that the field study provided further qualitative and usability evidence for PromptCanvas, not confirmation of the comparative advantage.
  4. [§6.1.6] Participant P12 is quoted as saying that in the static UI 'some of my previous texts were being cut off' when regenerating text. This suggests a functional deficiency or different regeneration behavior in the baseline, rather than an inherent property of chat UIs. If the baseline's regeneration replaced only part of the message or truncated it, this would bias the mental-demand and frustration ratings in favor of PromptCanvas. The manuscript should clarify how the baseline handled regeneration and, if the behavior was not the intended one, treat this as a confound rather than as evidence about the interface paradigm.
minor comments (5)
  1. [§6.3.1] For the significant NASA-TLX differences, only p-values are reported. Please add effect sizes (e.g., Cohen's d or rank-biserial correlation) and confidence intervals for the CSI and TLX comparisons, especially given the modest sample size.
  2. [§5.2 and Figure 12] The paper describes the baseline as 'designed according to the design and interaction principles of ChatGPT' but does not provide a screenshot annotation of its regeneration/edit behavior. A short description of what happens when a user edits a message or regenerates would help readers assess the fairness of the baseline.
  3. [§7.1] The limitations section mentions sample size and generalizability but does not acknowledge the confounds between the two conditions or the lack of a field-study baseline. Consider adding these as explicit limitations.
  4. [References] Reference [1] is formatted incorrectly ('Philip T. Kortum Aaron Bangor' should be 'Aaron Bangor, Philip T. Kortum, and James T. Miller'). Also, references [37] and [38] both list the same Luminate paper; one duplicate should be removed.
  5. [Figures 14 and 16] The stacked percentage bars in Figures 14 and 16 sum to over 100% within some rows (e.g., 'Which tool made you feel hurried or rushed' shows 39% + 39% + 33% = 111%). Please clarify the response format or adjust the visualization so readers can correctly interpret the preference data.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the comparative claims rest on external instruments and a separate baseline UI, not on the paper's own definitions.

full rationale

This paper is an empirical HCI study, not a derivation or modeling paper. The central claims, that PromptCanvas outperforms a conversational UI on the Creativity Support Index and reduces perceived cognitive load, are supported by standardized, external measurement instruments (CSI, NASA-TLX, SUS) and by a direct lab comparison against a separately implemented baseline chat interface (Section 5.2, 5.3, 6.2, 6.3). There is no fitted parameter, no mathematical derivation, and no self-referential definition in which a predicted quantity is constructed from the same data used to fit it. The few self-citations in the related-work section (e.g., Buschek's 'Collage is the New Writing' and Dang et al.'s writing tools) are used to motivate design goals and relate prior work, not to establish the empirical result, so they are not load-bearing circularity. The field study's claim that it 'confirmed these results' is a validity concern because it lacks a baseline condition, but it is not a circularity concern: it is a separate deployment with its own measurements, not a reduction of the target claim to its own inputs. Concerns about the fairness or representativeness of the baseline UI are methodological threats to validity, not instances of circular reasoning. Accordingly, no circular step can be quoted, and the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

No fitted numerical parameters or invented entities. The central claim rests on the fairness of the control condition, the validity of standardized self-report measures, the adequacy of counterbalancing at N=18, and the assumption that the same LLM performs comparably in both conditions.

assumptions (5)
  • domain assumption Self-report instruments (CSI, NASA-TLX, SUS) validly measure creativity support, workload, and usability in this context.
    The paper uses these instruments as primary outcome measures without external validation of the constructs in this specific setting (Sections 5.3.5 and 5.4.1).
  • domain assumption The conversational UI is a fair, representative baseline.
    Section 5.2 states it was designed according to ChatGPT interaction principles, but any mismatch in feature polish or novelty could bias the comparison.
  • domain assumption Counterbalanced within-subject design with 18 participants adequately controls learning and order effects.
    Order was randomized, but with N=18 the statistical power to detect order interactions is limited (Section 5.3.3).
  • domain assumption The same gpt-4o model produces comparable generation quality across both UIs.
    Both conditions use gpt-4o-2024-08-06, but PromptCanvas adds structured rephrasing prompts and temperature 1.06, so prompt-level variation is possible (Section 4.2 and Section 5.2).
  • domain assumption Open coding by the first author, refined with co-authors, captures user experience faithfully.
    Section 5.3.5 describes the coding process but not inter-rater reliability or a released codebook.

how reviews work

0 comments
Cite this review

Pith. "Pith review of PromptCanvas: Composable Prompting Workspaces Using Dynamic Widgets for Exploration and Iteration in Creative Writing." pith.science (2026). https://pith.science/paper/XMFKM3X6

@misc{pith2026250603741,
  author       = {Pith},
  title        = {Pith review of: PromptCanvas: Composable Prompting Workspaces Using Dynamic Widgets for Exploration and Iteration in Creative Writing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XMFKM3X6}},
  note         = {Machine review of arXiv:2506.03741}
}
read the original abstract

We introduce PromptCanvas, a concept that transforms prompting into a composable, widget-based experience on an infinite canvas. Users can generate, customize, and arrange interactive widgets representing various facets of their text, offering greater control over AI-generated content. PromptCanvas allows widget creation through system suggestions, user prompts, or manual input, providing a flexible environment tailored to individual needs. This enables deeper engagement with the creative process. In a lab study with 18 participants, PromptCanvas outperformed a traditional conversational UI on the Creativity Support Index. Participants found that it reduced cognitive load, with lower mental demand and frustration. Qualitative feedback revealed that the visual organization of thoughts and easy iteration encouraged new perspectives and ideas. A follow-up field study (N=10) confirmed these results, showcasing the potential of dynamic, customizable interfaces in improving collaborative writing with AI.

Figures

Figures reproduced from arXiv: 2506.03741 by the authors.

Figure 1
Figure 1. User interface of PromptCanvas. (A) Canvas-like workspace where users can place and freely organize widgets to create a [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Comparison of the important UI changes. In version 2, the [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. System flow for generating control widgets, detailed in Section [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (14 more)
Figure 4
Figure 4. Figure 4: System flow for generating control widget options, detailed in Section [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: System flow for extracting values, detailed in Section [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: System flow for rephrasing the text based on control widgets, detailed in Section [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: System flow of applying a user-prompt to the text, detailed in Section [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Marina writes a prompt for the short story generation on "The Three Little Pigs". [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]
Figure 9
Figure 9. Figure 9: (2) PromptCanvas generates widgets for Marina. (3) She chooses a widget from the widget panel and drags and drops it onto [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]
Figure 10
Figure 10. Figure 10: (5) Marina creates an empty widget on the canvas. (6) She gets more suggestions within the widget for [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]
Figure 11
Figure 11. Figure 11: Marina applies the widgets and receives the rephrased text. [PITH_FULL_IMAGE:figures/full_fig_p013_11.png]
Figure 12
Figure 12. Figure 12: The baseline conversational UI. Our lab experiment included two conditions. The baseline condition used a conversational user interface as illustrated in [PITH_FULL_IMAGE:figures/full_fig_p014_12.png]
Figure 13
Figure 13. Figure 13: One screenshot per participant while they were using PromptCanvas in the lab study, revealing how participants individually [PITH_FULL_IMAGE:figures/full_fig_p017_13.png]
Figure 14
Figure 14. Figure 14: Self-reported creativity support scores and preferences comparing PromptCanvas and the Conversational UI (N=18). [PITH_FULL_IMAGE:figures/full_fig_p022_14.png]
Figure 15
Figure 15. Figure 15: Self-reported NASA-TLX scores and ease-of-use ratings from participants in our lab study (N=18). [PITH_FULL_IMAGE:figures/full_fig_p023_15.png]
Figure 16
Figure 16. Figure 16: Self-reported cognitive load and preference scores comparing PromptCanvas and the conversational UI (N=18). [PITH_FULL_IMAGE:figures/full_fig_p024_16.png]
Figure 17
Figure 17. Figure 17: System Usability Scale results (𝑀 = 86.50, 𝑆𝐷 = 8.96) from our field-study (N=10). 6.5 Which Tool Do Users Prefer (PromptCanvas Vs. Conversational UI)? The results of the lab study indicate a strong preference for PromptCanvas compared to the baseline conversational U…

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From Words to Widgets for Controllable LLM Generation

    cs.HC 2026-04 unverdicted novelty 6.5 of 10

    Reifying ad-hoc preference phrases as GUI widgets, steered by log-probability modulation at decode time, improves precision and perceived control of LLM writing over natural-language prompting alone.

Reference graph

Works this paper leans on

41 extracted references · 11 canonical work pages · cited by 1 Pith paper

  1. [1]

    Kortum Aaron Bangor and James T

    Philip T. Kortum Aaron Bangor and James T. Miller. 2008. An Empirical Evaluation of the System Usability Scale.International Journal of Human–Computer Interaction24, 6 (2008), 574–594. doi:10.1080/10447310802205776 arXiv:https://doi.org/10.1080/10447310802205776

    doi:10.1080/10447310802205776 2008

  2. [2]

    Christopher Ahlberg and Ben Shneiderman. 1994. Visual information seeking: tight coupling of dynamic query filters with starfield displays. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems(Boston, Massachusetts, USA)(CHI ’94). Association for Computing Machinery, New York, NY, USA, 313–317. doi:10.1145/191666.191775

    work page arXiv 1994

  3. [3]

    Belle Dang Andy Nguyen, Yvonne Hong and Xiaoshan Huang. 2024. Human-AI collaboration patterns in AI-assisted academic writing.Studies in Higher Education49, 5 (2024), 847–864. doi:10.1080/03075079.2024.2323593

    arXiv 2024

  4. [5]

    Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, and Charles Sutton. 2021. Program Synthesis with Large Language Models. doi:10.48550/arXiv.2108.07732 arXiv:2108.07732

    arXiv doi:10.48550/arxiv.2108.07732 2021

  5. [6]

    James, and Nadia Polikarpova

    Shraddha Barke, Michael B. James, and Nadia Polikarpova. 2023. Grounded Copilot: How Programmers Interact with Code-Generating Models.Proc. ACM Program. Lang.7, OOPSLA1, Article 78 (apr 2023), 27 pages. doi:10.1145/3586030

    doi:10.1145/3586030 2023

  6. [7]

    Michel Beaudouin-Lafon and Wendy E. Mackay. 2000. Reification, polymorphism and reuse: three principles for designing visual interfaces. In Proceedings of the Working Conference on Advanced Visual Interfaces(Palermo, Italy)(A VI ’00). Association for Computing Machinery, New York, NY, USA, 102–109. doi:10.1145/345513.345267

    arXiv 2000

  7. [8]

    Advait Bhat, Saaket Agashe, Parth Oberoi, Niharika Mohile, Ravi Jangir, and Anirudha Joshi. 2023. Interacting with Next-Phrase Suggestions: How Suggestion Systems Aid and Influence the Cognitive Processes of Writing. InProceedings of the 28th International Conference on Intelligent User Interfaces(Sydney, NSW, Australia)(IUI ’23). Association for Computin...

    arXiv 2023

  8. [9]

    Michael Mose Biskjaer, Jonas Frich, Lindsay MacDonald Vermeulen, Christian Remy, and Peter Dalsgaard. 2019. How Time Constraints in a Creativity Support Tool Affect the Creative Writing Experience. InProceedings of the 31st European Conference on Cognitive Ergonomics(BELFAST, United Kingdom)(ECCE ’19). Association for Computing Machinery, New York, NY, US...

    work page arXiv 2019

Show all 41 references
  1. [11]

    Daniel Buschek. 2024. Collage is the New Writing: Exploring the Fragmentation of Text and User Interfaces in AI Tools. InProceedings of the 2024 ACM Designing Interactive Systems Conference(Copenhagen, Denmark)(DIS ’24). Association for Computing Machinery, New York, NY, USA, ...

    2024

  2. [12]

    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. InProceedings of the 2021 CHI Conference on Human Factors in Computing Systems(Yokohama, J...

    2021

  3. [13]

    Carroll, Celine Latulipe, Richard Fung, and Michael Terry

    Erin A. Carroll, Celine Latulipe, Richard Fung, and Michael Terry. 2009. Creativity factor evaluation: towards a standardized survey metric for creativity support. InProceedings of the Seventh ACM Conference on Creativity and Cognition(Berkeley, California, USA)(C&C ’09). Asso...

    2009

  4. [15]

    Hai Dang, Sven Goller, Florian Lehmann, and Daniel Buschek. 2023. Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Prompting. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems(Hamburg, Germany)(CHI ’23...

    2023

  5. [16]

    Leah Findlater and Krzysztof Z. Gajos. 2009. Design Space and Evaluation Challenges of Adaptive Graphical User Interfaces.AI Magazine30, 4 (Sep. 2009), 68. doi:10.1609/aimag.v30i4.2268

    2009 doi

  6. [17]

    Iona Gilburt. 2024. A machine in the loop: the peculiar intervention of artificial intelligence in writer’s block.New Writing21, 1 (2024), 26–37

    2024

  7. [18]

    Charles Goodwin. 2015. Professional vision. InAufmerksamkeit: Geschichte-theorie-empirie. Springer, Cham, Switzerland, 387–425

    2015

  8. [19]

    John R. Hayes. 2012. Modeling and Remodeling Writing.Written Communication29, 3 (2012), 369–388. doi:10.1177/0741088312451260

    2012 doi

  9. [20]

    Daphne Ippolito, Ann Yuan, Andy Coenen, and Sehmon Burnam. 2022. Creative Writing with an AI-Powered Writing Assistant: Perspectives from Professional Writers. doi:10.48550/ARXIV.2211.05030 Version Number: 1

    2022 doi arXiv

  10. [22]

    Abdulrahman Khamaj and Abdulelah M. Ali. 2024. Adapting user experience with reinforcement learning: Personalizing interfaces based on user behavior analysis in real-time.Alexandria Engineering Journal95 (2024), 164–173. doi:10.1016/j.aej.2024.03.045

    2024 doi

  11. [23]

    2016.Track changes: A literary history of word processing

    Matthew G Kirschenbaum. 2016.Track changes: A literary history of word processing. Harvard University Press, Cambridge, MA

    2016

  12. [24]

    Max Kreminski. 2024. The Dearth of the Author in AI-Supported Writing. InProceedings of the Third Workshop on Intelligent and Interactive Writing Assistants(Honolulu, HI, USA)(In2Writing ’24). Association for Computing Machinery, New York, NY, USA, 48–50. doi:10.1145/3690712.3690725

    2024

  13. [26]

    Zamfirescu-Pereira, Matthew G Lee, Sauhard Jain, Shanqing Cai, Piyawat Lertvittayakumjorn, Michael Xuelin Huang, Shumin Zhai, Bjoern Hartmann, and Can Liu

    Susan Lin, Jeremy Warner, J.D. Zamfirescu-Pereira, Matthew G Lee, Sauhard Jain, Shanqing Cai, Piyawat Lertvittayakumjorn, Michael Xuelin Huang, Shumin Zhai, Bjoern Hartmann, and Can Liu. 2024. Rambler: Supporting Writing With Speech via LLM-Assisted Gist Manipulation. InProcee...

    2024

  14. [27]

    Damien Masson, Young-Ho Kim, and Fanny Chevalier. 2024. Textoshop: Interactions Inspired by Drawing Software to Facilitate Text Editing. arXiv:2409.17088

    2024 arXiv

  15. [29]

    Mathewson, Jaylen Pittman, and Richard Evans

    Piotr Mirowski, Kory W. Mathewson, Jaylen Pittman, and Richard Evans. 2023. Co-Writing Screenplays and Theatre Scripts with Language Models: Evaluation by Industry Professionals. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems(Hamburg, Germany)(C...

    2023

  16. [30]

    Arpit Narechania, Kaustubh Odak, Mennatallah El-Assady, and Alex Endert. 2025. ProvenanceWidgets: A Library of UI Control Elements to Track and Dynamically Overlay Analytic Provenance.IEEE Transactions on Visualization and Computer Graphics31, 1 (2025), 1235–1245. doi:10.1109/...

    2025

  17. [31]

    Zhenhui Peng, Xingbo Wang, Qiushi Han, Junkai Zhu, Xiaojuan Ma, and Huamin Qu. 2023. Storyfier: Exploring Vocabulary Learning Support with Text Generation Models. InProceedings of the 36th Annual ACM Symposium on User Interface Software and Technology(San Francisco, CA, USA) (...

    2023

  18. [32]

    Hua Xuan Qin, Shan Jin, Ze Gao, Mingming Fan, and Pan Hui. 2024. CharacterMeet: Supporting Creative Writers’ Entire Story Character Construction Processes Through Conversation with LLM-Powered Chatbot Avatars. InProceedings of the CHI Conference on Human Factors in Computing S...

    2024

  19. [33]

    Emily Reif, Crystal Qian, James Wexler, and Minsuk Kahng. 2024. Automatic Histograms: Leveraging Language Models for Text Dataset Exploration. InExtended Abstracts of the CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA)(CHI EA ’24). Association for Compu...

    2024

  20. [34]

    Battersby, Melanie Tory, Rich Gossweiler, and Angel X

    Vidya Setlur, Sarah E. Battersby, Melanie Tory, Rich Gossweiler, and Angel X. Chang. 2016. Eviza: A Natural Language Interface for Visual Analysis. InProceedings of the 29th Annual Symposium on User Interface Software and Technology(Tokyo, Japan)(UIST ’16). Association for Com...

    2016

  21. [35]

    Ben Shneiderman. 1983. Direct manipulation: A step beyond programming languages.Computer16, 08 (1983), 57–69. Manuscript submitted to ACM 30 Amin et al

    1983

  22. [36]

    Arjun Srinivasan and Vidya Setlur. 2023. BOLT: A Natural Language Interface for Dashboard Authoring. InEuroVis 2023 - Short Papers. The Eurographics Association, Eindhoven, The Netherlands, 5. doi:10.2312/evs.20231035

    2023 doi

  23. [38]

    Sangho Suh, Meng Chen, Bryan Min, Toby Jia-Jun Li, and Haijun Xia. 2024. Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creation. InProceedings of the CHI Conference on Human Factors in Computing Systems(Honolulu, HI,...

    2024

  24. [39]

    Sangho Suh, Jian Zhao, and Edith Law. 2022. CodeToon: Story Ideation, Auto Comic Generation, and Structure Mapping for Code-Driven Storytelling. InProceedings of the 35th Annual ACM Symposium on User Interface Software and Technology(Bend, OR, USA)(UIST ’22). Association for C...

    2022

  25. [40]

    Lev Tankelevitch, Viktor Kewenig, Auste Simkute, Ava Elizabeth Scott, Advait Sarkar, Abigail Sellen, and Sean Rintel. 2024. The Metacognitive Demands and Opportunities of Generative AI. InProceedings of the CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA...

    2024

  26. [41]

    Leiva, and Antti Oulasvirta

    Kashyap Todi, Gilles Bailly, Luis A. Leiva, and Antti Oulasvirta. 2021. Adapting User Interfaces with Model-based Reinforcement Learning. arXiv:2103.06807

    2021 arXiv

  27. [42]

    Glassman, Jeevana Priya Inala, and Chenglong Wang

    Priyan Vaithilingam, Elena L. Glassman, Jeevana Priya Inala, and Chenglong Wang. 2024. DynaVis: Dynamically Synthesized UI Widgets for Visualization Editing. InProceedings of the CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA)(CHI ’24). Association for ...

    2024

  28. [43]

    Priyan Vaithilingam and Philip J. Guo. 2019. Bespoke: Interactively Synthesizing Custom GUIs from Command-Line Applications By Demonstration. InProceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology(New Orleans, LA, USA)(UIST ’19). Association f...

    2019

  29. [44]

    Zhijie Wang, Yuheng Huang, Da Song, Lei Ma, and Tianyi Zhang. 2024. PromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinement. InProceedings of the CHI Conference on Human Factors in Computing Systems. ACM, Honolulu HI USA, 1–21. doi:10.1145/3613904.3642803

    2024

  30. [45]

    Jason Wu, Kashyap Todi, Joannes Chan, Brad A Myers, and Ben Lafreniere. 2024. FrameKit: A Tool for Authoring Adaptive UIs Using Keyframes. In Proceedings of the 29th International Conference on Intelligent User Interfaces(Greenville, SC, USA)(IUI ’24). Association for Computin...

    2024

  31. [46]

    Ann Yuan, Andy Coenen, Emily Reif, and Daphne Ippolito. 2022. Wordcraft: Story Writing With Large Language Models. InProceedings of the 27th International Conference on Intelligent User Interfaces(Helsinki, Finland)(IUI ’22). Association for Computing Machinery, New York, NY, ...

    2022

  32. [47]

    Zamfirescu-Pereira, Richmond Y

    J.D. Zamfirescu-Pereira, Richmond Y. Wong, Bjoern Hartmann, and Qian Yang. 2023. Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems(Hamburg, Germany)(CHI ’23). Associa...

    2023

  33. [48]

    Zheng Zhang, Jie Gao, Ranjodh Singh Dhaliwal, and Toby Jia-Jun Li. 2023. VISAR: A Human-AI Argumentative Writing Assistant with Visual Programming and Rapid Draft Prototyping. InProceedings of the 36th Annual ACM Symposium on User Interface Software and Technology(San Francisc...

    2023

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.