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FeedQUAC: Quick Unobtrusive AI-Generated Commentary

T0 review · 0 major / 8 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read An always-on, duck-shaped AI companion that critiques designers' screens in real time can provide inspiration, validation, and useful critique even with almost no knowledge of the project.

desk verdict An honest, scoped design probe showing that ambient persona-based AI feedback can feel useful and low-stakes for 3D designers; the self-report limitation is real but proportionate to the carefully hedged claims. read the letter →

arxiv 2504.16416 v1 pith:FFRJIWAF submitted 2025-04-23 cs.HC cs.AIcs.CYcs.MM

Tao Long , Kendra Wannamaker , Jo Vermeulen , George Fitzmaurice , Justin Matejka This is my paper · ORCID
classification cs.HCcs.AIcs.CYcs.MM
keywords ambientfeedbackAIdesigncompanioncreativitysupportpersonas3DCADreal-timecommentaryprobestudy
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 argues that an always-on, low-friction AI companion that watches a designer's screen and delivers short spoken critiques can provide real value—inspiration, validation, and useful critique—even though it knows little about the project's goals or stage. It introduces FeedQUAC, a floating duck icon that screenshots the design workspace and sends it with persona prompts to a vision-language model, then reads feedback aloud. A design probe with eight 3D CAD designers found the feedback low-stakes and stress-free compared with human feedback, and most participants rated it convenient and worth the effort. The paper treats this as evidence that continuous ambient AI feedback can complement human feedback, especially when human reviewers are unavailable.

What carries the argument

The central mechanism is an ambient feedback loop: a small floating duck icon sits over the design editor; pressing a hotkey or an automatic timer captures a screenshot of the workspace and sends it, together with a persona personality prompt and previously given feedback, to a vision-language model, which generates under-50-word textual feedback; text-to-speech then reads it aloud while a transcript appears beside the icon. Eight personas (Mentor, Cheerleader, Critic, Analyst, CEO, Designer, Friend, and No Persona) vary tone and focus to supply diverse perspectives. The loop operationalizes rubber duck debugging as an always-available companion.

What would settle it

A controlled experiment comparing design outcomes, such as final model quality, revision counts, time to completion, or expert blind ratings, between designers using FeedQUAC and designers working without it would settle whether the claimed benefits are real.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central claim is that a lightweight, ambient AI feedback agent can be useful to designers despite operating with minimal context: from screenshots alone, the model identifies the design and offers relevant, often actionable comments, and the low-pressure, playful format makes designers more willing to seek frequent feedback. In a design probe study, eight experienced 3D CAD designers used FeedQUAC on an ongoing project; all eight found receiving AI feedback low-stakes, six rated the overall experience positive, and five judged the tool useful or very useful. The paper concludes that continuous AI-provided feedback has merits and could complement higher-quality human feedback, especially when human feedback is unavailable.

Load-bearing premise

The load-bearing premise is that participants' self-reported feelings—convenience, confidence, inspiration, and low stakes—reflect genuine design benefit, since the study has no objective measure of design quality or behavior change.

Editorial extensions

If this is right

  • Designers can receive frequent feedback without interrupting their workflow or waiting for human reviewers, reducing the social and logistical costs of feedback gathering.
  • Ambient, low-pressure AI feedback may serve as a confidence-building warm-up before seeking human critique, potentially reducing anxiety in design education and professional settings.
  • The persona-based approach offers a cheap way to simulate multiple reviewer perspectives, compensating for the limited diversity of feedback available on forums and personal networks.
  • Future creativity support tools can be evaluated on ambient qualities such as minimal attention and low disruption, in addition to active engagement and user control.

Reading between the lines

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

  • A testable extension would measure whether repeated ambient feedback changes designers' revision behavior over weeks, not just a 25-minute session, to separate novelty effects from durable workflow changes.
  • Ablating personas—comparing fixed-tone feedback with diverse-tone feedback—would reveal whether diversity of voice or mere frequency drives the reported inspiration and validation.
  • The low-stakes finding hints that AI feedback may reshape feedback-seeking norms, encouraging designers to seek critique earlier and more often, but this implication remains implicit in the paper.
  • The ambient feedback model likely transfers to other reflective tasks such as writing, UI design, or data visualization, though the paper only asserts transferability rather than demonstrating it.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

0 major / 8 minor

Summary. The paper presents FeedQUAC, an ambient AI design companion that runs as a floating duck icon over a 3D CAD editor, captures screenshots of the designer's work, and generates short, read-aloud feedback from one of eight personas using gpt-4-vision-preview and ElevenLabs text-to-speech. The design is motivated by a formative analysis of feedback-seeking posts on 3D design forums, which yields six design guidelines (DG1-DG6). The authors report a design probe study with eight experienced Fusion 360 designers who used the tool on their own ongoing projects for roughly 25 minutes. Results are based on interaction logs, Likert-scale survey items, think-aloud comments, and post-study interviews. The paper reports that participants found the tool convenient, playful, low-stakes, and sometimes validating, while also noting issues with missing design-stage context and limited user control. The discussion positions the work as a first step toward evaluating ambient interaction as a core design value for creativity support tools, and the conclusion carefully hedges that FeedQUAC 'can still be useful' despite limited context.

Significance. If the findings hold, the main contribution is a reproducible design probe that operationalizes ambient, low-attention AI feedback for creative workflows, a direction that is underrepresented in the creativity support tools literature. The paper's strengths include the inclusion of full system prompts, voice IDs, survey questions, and interview protocol in the appendices, which supports replication; the grounding of the system design in a forum analysis with explicit design guidelines; and the honest design-probe framing that avoids overclaiming generality. The paper makes no fitted predictions or derivations, so circular reasoning is not a concern. The evidentiary base is small and self-reported, but for a design probe the methods are appropriate to the stated scope. The main risk is that the contribution list's word 'demonstrating' overstates what eight participants' self-reports can establish; this is fixable by rewording and by expanding the limitations discussion.

minor comments (8)
  1. [Section 1, Contributions] The contribution statement says the study is 'demonstrating that FeedQUAC is useful in providing inspiration and validation,' but the evidence consists of self-reported Likert ratings and interviews from eight participants in a single session; 'suggesting' or 'providing initial evidence for' would be more proportionate.
  2. [Section 7, Designing future ambient creativity support tools] The paragraph beginning 'These perspectives may be particularly valuable...' appears twice verbatim; the duplicate should be removed.
  3. [Section 8, Limitations] The Limitations section does not mention the absence of a baseline condition, the reliance on self-report as the sole outcome measure, or the demand-characteristic risks from the experimenter asking for reactions after each feedback instance and the $75 payment; these should be acknowledged explicitly.
  4. [Section 3, Exploration of Feedback on 3D Design Forums] The selection criteria for the forum analysis are under-specified: ten Polycount posts from a single day and 'over 60 top Reddit posts' are mentioned, but the inclusion criteria for the Reddit/Discord data and any coding reliability procedure are not reported; adding this detail would strengthen the derivation of DG1-DG6.
  5. [Section 6.1.1, Frequency] The reported means (13.25 total, 5.26 manual, 7.99 automatic) are difficult to interpret without variance or per-participant detail, especially because P8 requested 27 manual feedback instances while P2 and P4 requested none; a per-participant table or box plot would be more informative.
  6. [Figures 6-12] The diverging stacked barcharts omit neutral responses and 'blur' unselected options, which can make agreement look stronger than it is; providing exact counts or percentages for each Likert item would improve transparency.
  7. [Abstract and Section 3] There are copy-editing errors, including 'an valuable consideration' in the Abstract and Contributions, 'an slower turnover rate' in Section 3, and 'incorporative screen overlays' in Section 7; these should be corrected.
  8. [Section 5.2, Procedure] P2 did not complete the full 25-minute design task, but the paper asserts that their experience is 'well represented'; this should be supported or reframed, and P2 should be explicitly identified as a partial session in the results.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular reasoning chain: the paper is an empirical design probe whose claims rest on user self-reports, not on fitted predictions or self-citation chains.

full rationale

FeedQUAC is a systems and evaluation paper with no formal derivation, model fitting, or predictive claim that could reduce to its own inputs. The central conclusion—that even with limited context FeedQUAC 'can still be useful in offering inspiration, validation, and critique'—is supported by Likert-scale responses, think-aloud comments, and interview excerpts from eight participants. These are participant self-reports, and the paper openly acknowledges its design-probe scope and limited feature set in Section 8. The absence of objective design-quality metrics, a baseline condition, or long-term follow-up is a validity concern about demand characteristics and novelty effects, but it is not circularity: the outcome variable (perceived usefulness) is not defined in terms of the system's design guidelines, and no parameter is fitted to that outcome and then relabeled as a prediction. The paper does cite prior work by some of the same authors (e.g., Long and Chilton 2023; Long et al. 2024), but these citations are used as related work or as sources of established concepts such as effort-reward tradeoffs and creative workflow studies; they are not invoked as load-bearing proof of FeedQUAC's effectiveness. The design guidelines (DG1-DG6) are derived from a separate formative analysis of forum posts, and the study then evaluates an instantiation of those guidelines rather than claiming the guidelines are proven by the study results. No equation is presented, no fitted input is renamed as a prediction, and no uniqueness theorem or author-imported mathematical constraint is used to force a conclusion. Therefore, the derivation chain contains no circular step, and the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no fitted numerical parameters or invented physical entities. It relies on domain assumptions about sample representativeness, self-report validity, and screenshot-based context sufficiency that are common in design-probe research but are not independently verified.

assumptions (3)
  • domain assumption Experienced 3D CAD designer volunteers are representative of the broader designer population addressed by the conclusions.
    The study recruits eight Fusion 360 users via posters, Slack, and mailing lists (Section 5.1) and then generalizes to designers and creativity support tools in Section 7.5.
  • domain assumption Self-reported ratings and interview comments are valid evidence of design benefit and workflow value.
    The results are built on Likert-scale responses and thematic analysis (Sections 5.3 and 6.2); no objective measures of design quality or behavior change are reported.
  • domain assumption Screenshots and prior feedback provide sufficient context for the LLM to give useful design feedback.
    The system feeds encoded screenshots and previous feedback into gpt-4-vision-preview without project briefs (Section 4.1), and the study treats the resulting feedback as useful when participants say so (Section 6.2).

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Cite this review

Pith. "Pith review of FeedQUAC: Quick Unobtrusive AI-Generated Commentary." pith.science (2026). https://pith.science/paper/FFRJIWAF

@misc{pith2026250416416,
  author       = {Pith},
  title        = {Pith review of: FeedQUAC: Quick Unobtrusive AI-Generated Commentary},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FFRJIWAF}},
  note         = {Machine review of arXiv:2504.16416}
}
read the original abstract

Design thrives on feedback. However, gathering constant feedback throughout the design process can be labor-intensive and disruptive. We explore how AI can bridge this gap by providing effortless, ambient feedback. We introduce FeedQUAC, a design companion that delivers real-time AI-generated commentary from a variety of perspectives through different personas. A design probe study with eight participants highlights how designers can leverage quick yet ambient AI feedback to enhance their creative workflows. Participants highlight benefits such as convenience, playfulness, confidence boost, and inspiration from this lightweight feedback agent, while suggesting additional features, like chat interaction and context curation. We discuss the role of AI feedback, its strengths and limitations, and how to integrate it into existing design workflows while balancing user involvement. Our findings also suggest that ambient interaction is a valuable consideration for both the design and evaluation of future creativity support systems.

Figures

Figures reproduced from arXiv: 2504.16416 by the authors.

Figure 1
Figure 1. FeedQUAC is an ambient design companion tool that supports designers by providing real-time, read-aloud, AI [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Framework based on the analysis of design forums, illustrating the components involved in [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Annotation FeedQUAC User Interface Walkthrough: Tanya, a 23-year-old entry-level 3D CAD designer, is designing [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: An illustration of the eight personas used in FeedQUAC, along with their icons and corresponding keywords used in [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Visualization of feedback requests for each participant during the design sessions, showing the selected persona, [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 7
Figure 7. Figure 7: Distribution of Likert scale responses for [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 9
Figure 9. Figure 9: Distribution of Likert scale responses for [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]
Figure 8
Figure 8. Figure 8: Distribution of Likert scale responses for [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 10
Figure 10. Figure 10: Distribution of Likert scale responses for [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]
Figure 11
Figure 11. Figure 11: Distribution of Likert scale responses indicating [PITH_FULL_IMAGE:figures/full_fig_p011_11.png]
Figure 12
Figure 12. Figure 12: Distribution of Likert scale responses for partici [PITH_FULL_IMAGE:figures/full_fig_p012_12.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

79 extracted references · 34 canonical work pages

  1. [1]

    AI Voice Generator & Text to Speech

    2025. AI Voice Generator & Text to Speech. https://elevenlabs.io

    work page 2025

  2. [2]

    OpenAI Platform

    2025. OpenAI Platform. https://platform.openai.com

    work page 2025

  3. [3]

    Andrade, Joanna Hefferen, and Maria E

    Heidi L. Andrade, Joanna Hefferen, and Maria E. Palma. 2019. Formative As- sessment in the Arts. In Handbook of Formative Assessment in the Disciplines . Routledge. Num Pages: 20

    work page 2019

  4. [4]

    Tyler Angert, Miroslav Suzara, Jenny Han, Christopher Pondoc, and Hariharan Subramonyam. 2023. Spellburst: A Node-based Interface for Exploratory Creative Coding with Natural Language Prompts. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (San Francisco, CA, USA) (UIST ’23). Association for Computing Machinery, ...

    arXiv 2023

  5. [5]

    Frederik Anseel, Filip Lievens, and Eveline Schollaert. 2009. Reflection as a strategy to enhance task performance after feedback. Organizational Behavior and Human Decision Processes 110, 1 (2009), 23–35

    work page 2009

  6. [6]

    Glassman

    Ian Arawjo, Chelse Swoopes, Priyan Vaithilingam, Martin Wattenberg, and Elena L. Glassman. 2024. ChainForge: A Visual Toolkit for Prompt Engineer- ing and LLM Hypothesis Testing. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’24). Asso- ciation for Computing Machinery, New York, NY, USA, Article 3...

    arXiv 2024

  7. [7]

    Luke Bailey. 2025. What is Ambient Automation? The Future of Productivity. https://www.getmagical.com/blog/what-is-ambient-automation Accessed on February 4, 2025

    work page 2025

  8. [8]

    Karim Benharrak, Tim Zindulka, Florian Lehmann, Hendrik Heuer, and Daniel Buschek. 2024. Writer-Defined AI Personas for On-Demand Feedback Generation. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’24). Association for Computing Machinery, New York, NY, USA, Article 1049, 18 pages. https://doi.org...

    arXiv 2024

Show all 79 references
  1. [9]

    Iqbal, Andrew D

    Ananya Bhattacharjee, Jina Suh, Mahsa Ershadi, Shamsi T. Iqbal, Andrew D. Wilson, and Javier Hernandez. 2024. Understanding Communication Preferences of Information Workers in Engagement with Text-Based Conversational Agents. https://doi.org/10.48550/arXiv.2410.20468 arXiv:241...

    2024 doi arXiv

  2. [10]

    Virginia Braun and Victoria Clarke. 2012. Thematic analysis. In APA handbook of research methods in psychology, Vol 2: Research designs: Quantitative, qualitative, neuropsychological, and biological., Harris Cooper, Paul M. Camic, Debra L. Long, A. T. Panter, David Rindskopf, ...

    2012 doi

  3. [11]

    Victor Bursztyn, David Demeter, Doug Downey, and Larry Birnbaum. 2022. Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2022 (Abu Dhabi, United Arab Emirates, 2022-12), Y...

    2022

  4. [12]

    Alice Cai, Steven R Rick, Jennifer L Heyman, Yanxia Zhang, Alexandre Filipowicz, Matthew Hong, Matt Klenk, and Thomas Malone. 2023. DesignAID: Using Generative AI and Semantic Diversity for Design Inspiration. InProceedings of The ACM Collective Intelligence Conference (Delft,...

    2023

  5. [13]

    O’Reilly Media, Inc

    Amber Case. 2015. Calm Technology: Principles and Patterns for Non-Intrusive Design. "O’Reilly Media, Inc. ". Google-Books-ID: DZ88CwAAQBAJ

    2015

  6. [14]

    Tuhin Chakrabarty, Vishakh Padmakumar, Faeze Brahman, and Smaranda Mure- san. 2024. Creativity Support in the Age of Large Language Models: An Empirical Study Involving Professional Writers. InProceedings of the 16th Conference on Cre- ativity & Cognition (Chicago, IL, USA) (C...

    2024

  7. [15]

    Kim, and Nadir Weibel

    Chen Chen, Cuong Nguyen, Thibault Groueix, Vladimir G. Kim, and Nadir Weibel

  8. [16]

    Ruijia Cheng, Ziwen Zeng, Maysnow Liu, and Steven Dow. 2020. Critique me: exploring how creators publicly request feedback in an online critique community. Proceedings of the ACM on Human-Computer Interaction 4, CSCW2 (2020), 1–24

    2020

  9. [17]

    Erin Cherry and Celine Latulipe. 2014. Quantifying the Creativity Support of Digital Tools through the Creativity Support Index. ACM Trans. Comput.-Hum. Interact. 21, 4, Article 21 (jun 2014), 25 pages. https://doi.org/10.1145/2617588

    2014 doi

  10. [18]

    Yoonseo Choi, Eun Jeong Kang, Seulgi Choi, Min Kyung Lee, and Juho Kim. 2025. Proxona: Supporting Creators’ Sensemaking and Ideation with LLM-Powered Audience Personas. https://doi.org/10.48550/arXiv.2408.10937 arXiv:2408.10937 [cs]

    2025 doi arXiv

  11. [19]

    Diane J Cook, Juan C Augusto, and Vikramaditya R Jakkula. 2009. Ambient intelligence: Technologies, applications, and opportunities. Pervasive and mobile computing 5, 4 (2009), 277–298

    2009

  12. [20]

    Deanna Dannels, Amy Housley Gaffney, and Kelly Norris Martin. 2008. Beyond content, deeper than delivery: What critique feedback reveals about communica- tion expectations in design education. International Journal for the Scholarship of teaching and Learning 2, 2 (2008), 12

    2008

  13. [21]

    Boris de Ruyter and Emile Aarts. 2004. Ambient intelligence: visualizing the future. In Proceedings of the Working Conference on Advanced Visual Interfaces (Gallipoli, Italy) (A VI ’04). Association for Computing Machinery, New York, NY, USA, 203–208. https://doi.org/10.1145/9...

    2004

  14. [22]

    Zijian Ding, Alison Smith-Renner, Wenjuan Zhang, Joel R Tetreault, and Ale- jandro Jaimes. 2023. Harnessing the power of LLMs: Evaluating human-AI text co-creation through the lens of news headline generation. arXiv preprint arXiv:2310.10706 (2023)

    2023 arXiv

  15. [23]

    Doyle, Justin Edwards, Odile Dumbleton, Leigh Clark, and Benjamin R

    Philip R. Doyle, Justin Edwards, Odile Dumbleton, Leigh Clark, and Benjamin R. Cowan. 2019. Mapping Perceptions of Humanness in Speech-Based Intelligent Personal Assistant Interaction. In Proceedings of the 21st International Conference on Human-Computer Interaction with Mobil...

    2019

  16. [24]

    Peitong Duan, Chin-Yi Cheng, Gang Li, Bjoern Hartmann, and Yang Li. 2024. UICrit: Enhancing Automated Design Evaluation with a UI Critique Dataset. In Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology (Pittsburgh, PA, USA) (UIST ’24). Assoc...

    2024

  17. [25]

    Peitong Duan, Jeremy Warner, Yang Li, and Bjoern Hartmann. 2024. Generating Automatic Feedback on UI Mockups with Large Language Models. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’24). Association for Computing Mac...

    2024

  18. [27]

    Linda Flower and John R. Hayes. 1981. A Cognitive Process Theory of Writing. College Composition and Communication 32, 4 (1981), 365–387. https://eric.ed. gov/?id=EJ256235

    1981

  19. [28]

    Jonas Frich, Lindsay MacDonald Vermeulen, Christian Remy, Michael Mose Biskjaer, and Peter Dalsgaard. 2019. Mapping the Landscape of Creativity Support Tools in HCI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19...

    2019

  20. [29]

    Neil Jon Gallagher. [n. d.]. Embedded 3D Web Based Content for the Creation of an Interactive Medical Browser. ([n. d.])

  21. [30]

    Katy Ilonka Gero, Vivian Liu, and Lydia Chilton. 2022. Sparks: Inspiration for Science Writing Using Language Models. In Designing Interactive Systems Confer- ence (Virtual Event, Australia) (DIS ’22). Association for Computing Machinery, New York, NY, USA, 1002–1019. https://...

    2022

  22. [31]

    Katy Ilonka Gero, Tao Long, and Lydia B Chilton. 2023. Social Dynamics of AI Support in Creative Writing. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Artic...

    2023

  23. [32]

    Ferreira

    Frederica Gonçalves, Eduardo Fermé, and João C. Ferreira. 2017. Emerging Opportunities for Ambient Intelligence in Creativity Support Tools. In Intelligent Systems Design and Applications, Ana Maria Madureira, Ajith Abraham, Dorabela Gamboa, and Paulo Novais (Eds.). Springer I...

    2017

  24. [33]

    Sandra G Hart and Lowell E Staveland. 1988. Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. Human mental work- load 1, 3 (1988), 139–183

    1988

  25. [34]

    John R. Hayes. 1996. A new framework for understanding cognition and affect in writing. In The Science of Writing: Theories, Methods, Individual Differences, and Applications. Lawrence Erbaum Associates

    1996

  26. [35]

    Julie S Hui, Elizabeth M Gerber, and Steven P Dow. 2014. Crowd-based design activities: helping students connect with users online. In Proceedings of the 2014 conference on Designing Interactive Systems . 875–884

    2014

  27. [36]

    Angel Hsing-Chi Hwang. 2022. Too Late to be Creative? AI-Empowered Tools in Creative Processes. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, USA) (CHI EA ’22). Association for Computing Machinery, New York, NY, USA, A...

    2022

  28. [37]

    Hancock, and Michael S

    Pranav Khadpe, Ranjay Krishna, Li Fei-Fei, Jeffrey T. Hancock, and Michael S. Bernstein. 2020. Conceptual Metaphors Impact Perceptions of Human-AI Collab- oration. Proc. ACM Hum.-Comput. Interact. 4, CSCW2, Article 163 (Oct. 2020), 26 pages. https://doi.org/10.1145/3415234

    2020 doi

  29. [38]

    Jeongyeon Kim, Sangho Suh, Lydia B Chilton, and Haijun Xia. 2023. Metaphorian: Leveraging Large Language Models to Support Extended Metaphor Creation for Science Writing. In Proceedings of the 2023 ACM Designing Interactive Systems Conference (Pittsburgh PA USA, 2023-07-10). A...

    2023

  30. [39]

    Sneha R Krishna Kumaran. 2022. Setting, tracking and visualizing goals to seek feedback on in-progress creative work . Ph. D. Dissertation. University of Illinois at Urbana-Champaign

    2022

  31. [40]

    Emily Kuang, Minghao Li, Mingming Fan, and Kristen Shinohara. 2024. Enhanc- ing UX Evaluation Through Collaboration with Conversational AI Assistants: Effects of Proactive Dialogue and Timing. In Proceedings of the 2024 CHI Con- ference on Human Factors in Computing Systems (H...

    2024

  32. [41]

    Chinmay E Kulkarni, Michael S Bernstein, and Scott R Klemmer. 2015. Peer- Studio: rapid peer feedback emphasizes revision and improves performance. In Proceedings of the second (2015) ACM conference on learning@ scale . 75–84

    2015

  33. [42]

    Tiffany CK Kwok, Peter Kiefer, and Martin Raubal. 2024. Unobtrusive interaction: a systematic literature review and expert survey. Human–Computer Interaction 39, 5-6 (2024), 380–416

    2024

  34. [43]

    Cheng Li, Mingyang Zhang, Qiaozhu Mei, Yaqing Wang, Spurthi Amba Hombaiah, Yi Liang, and Michael Bendersky. 2023. Teach LLMs to Personalize – An Approach inspired by Writing Education. arXiv:2308.07968 [cs] http://arxiv.org/abs/2308. 07968

    2023 arXiv

  35. [44]

    Vivian Liu, Tao Long, Nathan Raw, and Lydia Chilton. 2023. Generative Disco: Text-to-Video Generation for Music Visualization. arXiv:2304.08551 (Apr 2023). https://doi.org/10.48550/arXiv.2304.08551 arXiv:2304.08551 [cs]

    2023 doi arXiv

  36. [45]

    Vivian Liu, Jo Vermeulen, George Fitzmaurice, and Justin Matejka. 2023. 3DALL- E: Integrating Text-to-Image AI in 3D Design Workflows. InProceedings of the 2023 ACM Designing Interactive Systems Conference (Pittsburgh, PA, USA) (DIS ’23). Association for Computing Machinery, N...

    2023

  37. [46]

    Tao Long and Lydia B. Chilton. 2023. Challenges and Opportunities for the Design of Smart Speakers. In AAAI Workshop on User-Centric Artificial Intelligence for Assistance in At-Home Tasks. Washington, DC. https://doi.org/10.48550/arXiv. 2306.05741 arXiv:2306.05741 [cs]

    2023 doi arXiv

  38. [47]

    Tao Long, Katy Ilonka Gero, and Lydia B Chilton. 2024. Not Just Novelty: A Lon- gitudinal Study on Utility and Customization of an AI Workflow. InProceedings of the 2024 ACM Designing Interactive Systems Conference (Copenhagen, Denmark) (DIS ’24). Association for Computing Mac...

    2024

  39. [48]

    Tao Long and Weirui Peng. 2023. PortfolioMentor: Multimodal Generative AI Companion for Learning and Crafting Interactive Digital Art Portfolios. https: //doi.org/10.48550/arXiv.2311.14091 arXiv:2311.14091 [cs]

    2023 doi arXiv

  40. [49]

    Tao Long, Dorothy Zhang, Grace Li, Batool Taraif, Samia Menon, Kynnedy Si- mone Smith, Sitong Wang, Katy Ilonka Gero, and Lydia B. Chilton. 2023. Tweeto- rial Hooks: Generative AI Tools to Motivate Science on Social Media. In Proceed- ings of the 14th Conference on Computation...

    2023

  41. [50]

    Ryan Louie, Andy Coenen, Cheng Zhi Huang, Michael Terry, and Carrie J. Cai

  42. [51]

    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. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany)...

    2023

  43. [52]

    Seyed Parsa Neshaei, Roman Rietsche, Xiaotian Su, and Thiemo Wambsganss

  44. [53]

    Zheng Ning, Zheng Zhang, Jerrick Ban, Kaiwen Jiang, Ruohong Gan, Yapeng Tian, and Toby Jia-Jun Li. 2024. MIMOSA: Human-AI Co-Creation of Computational Spatial Audio Effects on Videos. InProceedings of the 16th Conference on Creativity & Cognition (Chicago, IL, USA) (C&C ’24). ...

    2024

  45. [54]

    OpenAI. 2024. GPT-4 Technical Report. arXiv:2303.08774 [cs.CL] https://arxiv. org/abs/2303.08774

    2024 arXiv

  46. [55]

    Srishti Palani, David Ledo, George Fitzmaurice, and Fraser Anderson. 2022. ”I don’t want to feel like I’m working in a 1960s factory”: The Practitioner Perspective on Creativity Support Tool Adoption. In CHI Conference on Hu- man Factors in Computing Systems . ACM, New Orleans...

    2022

  47. [56]

    Jeongeon Park and DaEun Choi. 2023. AudiLens: Configurable LLM-Generated Audiences for Public Speech Practice. In Adjunct Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (San Francisco, CA, USA) (UIST ’23 Adjunct). Association for Computi...

    2023

  48. [58]

    Savvas Petridis, Nicholas Diakopoulos, Kevin Crowston, Mark Hansen, Keren Henderson, Stan Jastrzebski, Jeffrey V Nickerson, and Lydia B Chilton. 2023. AngleKindling: Supporting Journalistic Angle Ideation with Large Language Models. In Proceedings of the 2023 CHI Conference on...

    2023 doi

  49. [60]

    Timo Schick, Jane Dwivedi-Yu, Zhengbao Jiang, Fabio Petroni, Patrick Lewis, Gautier Izacard, Qingfei You, Christoforos Nalmpantis, Edouard Grave, and Se- bastian Riedel. 2022. PEER: A Collaborative Language Model. arXiv:2208.11663 [cs] http://arxiv.org/abs/2208.11663

    2022 arXiv

  50. [61]

    Kathrin Seßler, Tao Xiang, Lukas Bogenrieder, and Enkelejda Kasneci. 2023. Peer: Empowering writing with large language models. In European Conference on Technology Enhanced Learning. Springer, 755–761

    2023

  51. [62]

    Song, Xiaojuan Ma, and Juho Kim

    Jeongeon Park, Bryan Min, Kihoon Son, Jean Y. Song, Xiaojuan Ma, and Juho Kim

  52. [63]

    Lu Sun, Aaron Chan, Yun Seo Chang, and Steven P. Dow. 2024. ReviewFlow: Intelligent Scaffolding to Support Academic Peer Reviewing. InProceedings of the 29th International Conference on Intelligent User Interfaces (Greenville, SC, USA) (IUI ’24). Association for Computing Mach...

    2024

  53. [64]

    Lu Sun, Stone Tao, Junjie Hu, and Steven P. Dow. 2024. MetaWriter: Exploring the Potential and Perils of AI Writing Support in Scientific Peer Review. Proc. ACM Hum.-Comput. Interact. 8, CSCW1, Article 94 (April 2024), 32 pages. https: //doi.org/10.1145/3637371

    2024 doi

  54. [65]

    David Thomas and Andrew Hunt. 2019. The Pragmatic Programmer: your journey to mastery. Addison-Wesley Professional

    2019

  55. [66]

    Ullman, Stephen Wood, and David Craig

    David G. Ullman, Stephen Wood, and David Craig. 1990. The importance of drawing in the mechanical design process. Computers & Graphics 14, 2 (Jan. 1990), 263–274. https://doi.org/10.1016/0097-8493(90)90037-X

    1990 doi

  56. [67]

    Nickerson, and Lydia B

    Sitong Wang, Samia Menon, Tao Long, Keren Henderson, Dingzeyu Li, Kevin Crowston, Mark Hansen, Jeffrey V. Nickerson, and Lydia B. Chilton. 2024. Reel- Framer: Human-AI Co-Creation for News-to-Video Translation. InProceedings of the 2024 CHI Conference on Human Factors in Compu...

    2024

  57. [68]

    Anselm Strauss and Juliet Corbin. 1994. Grounded theory methodology: An overview. In Handbook of qualitative research . Sage Publications, Inc, Thousand Oaks, CA, US, 273–285

    1994

  58. [69]

    Mark Weiser. 1998. The Future of Ubiquitous Computing on Campus. Commun. ACM 41, 1 (jan 1998), 41–42. https://doi.org/10.1145/268092.268108

    1998

  59. [70]

    Wynn and Anja M

    David C. Wynn and Anja M. Maier. 2022. Feedback systems in the design and development process. Research in Engineering Design 33, 3 (July 2022), 273–306. https://doi.org/10.1007/s00163-022-00386-z

    2022 doi

  60. [71]

    Kevin Yang, Yuandong Tian, Nanyun Peng, and Dan Klein. 2022. Re3: Generating Longer Stories With Recursive Reprompting and Revision. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (Abu Dhabi, United Arab Emirates, 2022-12), Yoav Goldb...

    2022 doi

  61. [72]

    Yu-Chun Yen, Steven P Dow, Elizabeth Gerber, and Brian P Bailey. 2016. Social network, web forum, or task market? Comparing different crowd genres for design feedback exchange. InProceedings of the 2016 ACM Conference on Designing Interactive Systems. 773–784

    2016

  62. [73]

    Yu-Chun Grace Yen, Steven P Dow, Elizabeth Gerber, and Brian P Bailey. 2017. Listen to others, listen to yourself: Combining feedback review and reflection to improve iterative design. In Proceedings of the 2017 ACM SIGCHI Conference on Creativity and Cognition. 158–170

    2017

  63. [74]

    Mark Weiser. 1991. The Computer for the 21st Century. (1991), 8

    1991

  64. [75]

    The FeedQUAC system would be a useful addition to my creative workflow

    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. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (San Franci...

    2023

  65. [80]

    Zamfirescu-Pereira, Richmond Y

    J.D. Zamfirescu-Pereira, Richmond Y. Wong, Bjoern Hartmann, and Qian Yang

  66. [648]

    https://doi.org/10.1007/978-3-319-53480-0_63

    doi

  67. [1686]

    https://doi.org/10.18653/v1/2022.findings-emnlp.121

    2022 doi

  68. [2020]

    In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20)

    Novice-AI Music Co-Creation via AI-Steering Tools for Deep Generative Models. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/33...

    2020

  69. [2023]

    In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23)

    Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 437, 21 pages. https:...

    2023

  70. [2024]

    ACM Transactions on Computer-Human Interaction 31, 5 (Oct

    MemoVis: A GenAI-Powered Tool for Creating Companion Reference Im- ages for 3D Design Feedback. ACM Transactions on Computer-Human Interaction 31, 5 (Oct. 2024), 1–41. https://doi.org/10.1145/3694681

    2024 doi

  71. [2025]

    https://doi.org/10.48550/arXiv.2310.01331 arXiv:2310.01331 [cs]

    ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi- Agent Conversational Interactions. https://doi.org/10.48550/arXiv.2310.01331 arXiv:2310.01331 [cs]

    doi

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.