Pith. sign in

REVIEW 1 major objections 106 references

Creatives often prefer self-experimentation over structured guidance for GenAI image tools because guidance can limit their creativity.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

T0 review · grok-4.3

2026-06-30 22:29 UTC pith:SC26MHC4

load-bearing objection The paper gathers original interview, survey, and probe data on creatives using GenAI image tools but the headline tension between guidance and autonomy rests on self-reports from n=17 without behavioral checks. the 1 major comments →

arxiv 2605.10898 v2 pith:SC26MHC4 submitted 2026-05-11 cs.HC

How Creatives Approach GenAI Image Generation: Tensions Between Structured Guidance, Self-Experimentation, and Creative Autonomy

Haidan Liu , Isabelle Kwan , Taiga Okuma , Jeffrey Loverock , Nicholas Vincent , Parmit K Chilana This is my paper
classification cs.HC
keywords generative AIcreative practiceuser studyAI literacyself-experimentationcreative autonomyHCI
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper investigates how artists and hobbyists approach generative AI image tools through interviews with eight creatives, a survey of 159 respondents, and a probe study with 17 participants. It finds that creatives typically turn to self-experimentation or tutorials yet often struggle with AI terminology. Even when structured guidance is described as helpful for building understanding, many participants still chose to explore independently. The work identifies a core tension in supporting AI literacy while safeguarding creative autonomy.

Core claim

Our user study with 17 creatives revealed that, even when creatives described the guidance as helpful for understanding AI, many still preferred self-experimentation, feeling that guidance could limit their creativity.

What carries the argument

The tension between structured guidance for AI literacy and self-experimentation to preserve creative autonomy.

Load-bearing premise

That the self-reported preferences from small samples of creatives in study settings accurately reflect broader behaviors and would persist in everyday tool use.

What would settle it

A follow-up observation of whether creatives given structured guidance in actual tools produce measurably less varied outputs or report lower creative satisfaction than those using only self-experimentation.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Guidance for GenAI tools should remain optional to avoid restricting user creativity.
  • Support materials need to address AI terminology without dictating specific workflows.
  • Tool interfaces should allow seamless shifts between guided steps and open exploration.
  • Designs that integrate guidance on demand may reduce the sense of limitation compared to fixed tutorials.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same preference pattern could appear in other creative domains such as text or audio generation.
  • Long-term tracking of actual tool usage might reveal whether self-experimentation leads to different creative results than guided paths.
  • Hybrid interfaces that adapt guidance level based on user signals could be tested as a practical response to the reported tension.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

1 major / 0 minor

Summary. The paper reports results from an interview study (n=8 artists/hobbyists), a survey (n=159), and a subsequent research probe deployed in a user study (n=17) examining how creatives learn and use GenAI image-generation tools. It identifies common reliance on self-experimentation or tutorials, difficulties with AI terminology, and a central tension: structured guidance is perceived as helpful for AI understanding yet many participants still prefer self-experimentation because they believe guidance risks limiting creative autonomy.

Significance. If the reported tension is robustly evidenced, the work contributes to HCI by surfacing a design challenge for AI-literacy tools in creative practice—how to provide support without eroding perceived creative freedom. The mixed-methods sequence (interviews informing survey and probe) is a positive feature.

major comments (1)
  1. [User study with 17 creatives (probe evaluation)] The headline claim that 'even when creatives described the guidance as helpful for understanding AI, many still preferred self-experimentation, feeling that guidance could limit their creativity' rests solely on post-probe self-reports from the n=17 user study. No usage logs, prompt histories, A/B deployment data, or other behavioral measures are described that would show whether these stated concerns actually alter tool adoption, prompt strategies, or creative output in practice.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback, including recognition of the mixed-methods design. We respond to the single major comment below.

read point-by-point responses

  1. Referee: [User study with 17 creatives (probe evaluation)] The headline claim that 'even when creatives described the guidance as helpful for understanding AI, many still preferred self-experimentation, feeling that guidance could limit their creativity' rests solely on post-probe self-reports from the n=17 user study. No usage logs, prompt histories, A/B deployment data, or other behavioral measures are described that would show whether these stated concerns actually alter tool adoption, prompt strategies, or creative output in practice.

    Authors: We agree that the reported tension is evidenced exclusively through post-probe self-reports and interview data from the n=17 study. The probe was intentionally designed as a short-term elicitation tool to surface perceptions of structured guidance, not as a deployment or A/B study that would capture behavioral logs, prompt histories, or changes in output. This scope is consistent with the paper's overall qualitative focus, which triangulates the probe findings with the earlier interview (n=8) and survey (n=159) results. We will revise the discussion and limitations sections to explicitly qualify the claim, note the absence of behavioral measures, and clarify that the work identifies a perceived tension rather than demonstrating causal effects on practice. revision: yes

Circularity Check

0 steps flagged

Empirical qualitative study with no derivations or self-referential reductions

full rationale

The paper reports results from interviews (n=8), a survey (n=159), and a probe study (n=17) on creatives' use of GenAI tools. No equations, fitted parameters, predictions derived from inputs, or load-bearing self-citations appear in the provided text. Central claims rest on participant self-reports presented directly as findings, with no reduction of outputs to inputs by construction. This is a standard empirical HCI study whose evidence chain is external to the paper itself.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the validity of self-reported qualitative data from small samples and the interpretation that stated preferences indicate a general design tension.

axioms (1)
  • domain assumption Self-reported preferences from interviews and probe studies accurately reflect participants' actual practices and attitudes toward creative autonomy.
    Standard assumption in HCI qualitative work but unverified in the provided abstract.

pith-pipeline@v0.9.1-grok · 5715 in / 1212 out tokens · 31062 ms · 2026-06-30T22:29:04.359351+00:00 · methodology

0 comments
read the original abstract

As generative AI tools increasingly influence creative practice, they raise longstanding HCI questions about how creatives learn complex software and how they can be better supported. We conducted an interview study with artists and hobbyists (n=8) and a follow-up survey (n=159) to understand how this population approaches and seeks guidance for GenAI image tools. We found that creatives commonly use either self-experimentation or tutorials to explore GenAI tools, yet many struggle with confusing AI terminology. To gain further insight into creatives' learning experiences, we developed a research probe to elicit creatives' perceptions of structured guidance. Our user study with 17 creatives revealed that, even when creatives described the guidance as helpful for understanding AI, many still preferred self-experimentation, feeling that guidance could limit their creativity. Our findings highlight a central tension in supporting AI literacy for creatives: balancing guidance and promoting literacy while preserving creative freedom.

Figures

Figures reproduced from arXiv: 2605.10898 by Haidan Liu, Isabelle Kwan, Jeffrey Loverock, Nicholas Vincent, Parmit K Chilana, Taiga Okuma.

Figure 1
Figure 1. Figure 1: Overview of our staged inquiry across three studies. The formative interviews and survey studies (left) identified [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Leonardo AI interface used in the interview study. Participants were given a sketch image provided by the researchers [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: (a) Responses to the survey question (single-select): What is your most preferred way to learn about GenAI image [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Helpfulness ratings of four GenAI tutorial types among respondents who preferred tutorials, shown all tutorial [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Early design exploration of our research probe, where users could click on red dots to reveal object attributes in a [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: An example of the Text-to-Image tutorial. The interactive design allows users to click through the explanation [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Openart AI interface: (a) Interface where participants interacted with the text-to-image tasks; (b) Interface for sketch [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: User study session procedure. Participants worked through two text-to-image tasks and two image-to-image tasks, [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Images used in the study tasks. (a) and (b) for text-to-image, (c) and (d) for style transfer, and (e) for sketch-to-image [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗

Review history (2 revisions) →

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

106 extracted references · 106 canonical work pages

  1. [1]

    [n. d.]. DALL·E 3. https://openai.com/index/dall-e-3/

    work page

  2. [2]

    Abuzuraiq and Philippe Pasquier

    Ahmed M. Abuzuraiq and Philippe Pasquier. 2025. Explainability-in-Action: En- abling Expressive Manipulation and Tacit Understanding by Bending Diffusion Models in ComfyUI. arXiv:2508.07183 [cs] https://arxiv.org/abs/2508.07183

    work page arXiv 2025

  3. [3]

    Adobe. 2025. Make stunning updates to your images with text prompts using Generative Fill. https://helpx.adobe.com/content/help/ca/en/photoshop/using/ generative-fill.html

    work page 2025

  4. [4]

    Leonardo AI. n.d.. AI Image Generator - Create Art, Images & Video | Leonardo AI. https://leonardo.ai/

    work page

  5. [5]

    Jay Alammar. 2022. The Illustrated Stable Diffusion. https://jalammar.github. io/illustrated-stable-diffusion/

    work page 2022

  6. [6]

    Bennett, Kori Inkpen, Jaime Teevan, Ruth Kikin-Gil, and Eric Horvitz

    Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N. Bennett, Kori Inkpen, Jaime Teevan, Ruth Kikin-Gil, and Eric Horvitz. 2019. Guidelines for Human- AI Interaction. InProceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, Glasgow Scotland Uk, 1–13. https:...

    work page arXiv 2019

  7. [7]

    1986.Social foundations of thought and action: A social cognitive theory

    Albert Bandura. 1986.Social foundations of thought and action: A social cognitive theory. Prentice-Hall, Inc, Englewood Cliffs, NJ, US

    work page 1986

  8. [8]

    Lasecki, Daniel S

    Gagan Bansal, Besmira Nushi, Ece Kamar, Walter S. Lasecki, Daniel S. Weld, and Eric Horvitz. 2019. Beyond Accuracy: The Role of Mental Models in Human-AI Team Performance.Proceedings of the AAAI Conference on Human Computation and Crowdsourcing7 (Oct. 2019), 2–11. https://doi.org/10.1609/hcomp.v7i1.5285

    work page doi:10.1609/hcomp.v7i1.5285 2019

  9. [9]

    LENS: Learning to segment anything with unified reinforced reasoning

    Gagan Bansal, Besmira Nushi, Ece Kamar, Daniel S. Weld, Walter S. Lasecki, and Eric Horvitz. 2019. Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff.Proceedings of the AAAI Conference on Artificial Intelligence33, 01 (July 2019), 2429–2437. https://doi.org/10.1609/aaai. v33i01.33012429

    work page doi:10.1609/aaai 2019

  10. [10]

    Ronald A Beghetto and Maciej Karwowski. 1945. Creative agency unbound. American history1861, 1900 (1945)

    work page 1945

  11. [11]

    Lucia Benaquisto. 2008. Open Coding. InThe SAGE Encyclopedia of Qual- itative Research Methods. SAGE Publications, Inc. https://doi.org/10.4135/ 9781412963909

    work page 2008

  12. [12]

    Kirsten Boehner, Janet Vertesi, Phoebe Sengers, and Paul Dourish. 2007. How HCI interprets the probes. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems 2007, CHI 2007 (Conference on Human Factors in Computing Systems - Proceedings). Association for Computing Machinery, 1077–1086. https://doi.org/10.1145/1240624.1240789 SIGCHI C...

    work page doi:10.1145/1240624.1240789 2007

  13. [13]

    Stephen Brade, Bryan Wang, Mauricio Sousa, Sageev Oore, and Tovi Grossman

    work page

  14. [14]

    https://doi.org/10.48550/arXiv.2304.09337

    Promptify: Text-to-Image Generation through Interactive Prompt Explo- ration with Large Language Models. https://doi.org/10.48550/arXiv.2304.09337

    work page doi:10.48550/arxiv.2304.09337

  15. [15]

    Virginia Braun, , and Victoria Clarke. 2006. Using thematic analysis in psy- chology.Qualitative Research in Psychology3, 2 (Jan. 2006), 77–101. https: //doi.org/10.1191/1478088706qp063oa

    work page doi:10.1191/1478088706qp063oa 2006

  16. [16]

    Nick Bryan-Kinns, Berker Banar, Corey Ford, Courtney N Reed, Yixiao Zhang, Simon Colton, and Jack Armitage. 2023. Exploring XAI for the Arts: Explaining Latent Space in Generative Music. (2023). https://arxiv.org/pdf/2308.05496

    work page arXiv 2023

  17. [17]

    Harald Burgsteiner, Martin Kandlhofer, and Gerald Steinbauer. 2016. IRobot: Teaching the Basics of Artificial Intelligence in High Schools.Proceedings of the AAAI Conference on Artificial Intelligence30, 1 (March 2016). https: //doi.org/10.1609/aaai.v30i1.9864

    work page doi:10.1609/aaai.v30i1.9864 2016

  18. [18]

    John Carroll and Mary Beth Rosson. 1987. Paradox of the active user. 80–111

    work page 1987

  19. [19]

    CBC. 2025. How a book on climate became an international bestseller.CBC News(May 2025). https://www.cbc.ca/news/science/what-on-earth-climate- comic-1.7546104

    work page 2025

  20. [20]

    Fiannaca, Pedro Vergani, Chinmay Kulkarni, Carrie J Cai, and Michael Terry

    Minsuk Chang, Stefania Druga, Alexander J. Fiannaca, Pedro Vergani, Chinmay Kulkarni, Carrie J Cai, and Michael Terry. 2023. The Prompt Artists. InCreativity and Cognition. ACM, Virtual Event USA, 75–87. https://doi.org/10.1145/3591196. 3593515

    work page doi:10.1145/3591196 2023

  21. [21]

    John Joon Young Chung and Eytan Adar. 2023. PromptPaint: Steering Text- to-Image Generation Through Paint Medium-like Interactions. InProceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. ACM, San Francisco CA USA, 1–17. https://doi.org/10.1145/3586183.3606777

    work page doi:10.1145/3586183.3606777 2023

  22. [22]

    Chiara Di Lodovico, Federico Torrielli, Luigi Di Caro, and Amon Rapp. 2025. How Do People Develop Folk Theories of Generative AI Text-to-Image Models? A Qualitative Study on How People Strive to Explain and Make Sense of GenAI. International Journal of Human–Computer Interaction41, 23 (Dec. 2025), 14846– 14870. https://doi.org/10.1080/10447318.2025.2491009

    work page doi:10.1080/10447318.2025.2491009 2025

  23. [23]

    Measuring the carbon intensity of ai in cloud instances,

    Jesse Dodge, Taylor Prewitt, Remi Tachet Des Combes, Erika Odmark, Roy Schwartz, Emma Strubell, Alexandra Sasha Luccioni, Noah A. Smith, Nicole DeCario, and Will Buchanan. 2022. Measuring the Carbon Intensity of AI in Cloud Instances. https://doi.org/10.48550/arXiv.2206.05229

    work page doi:10.48550/arxiv.2206.05229 2022

  24. [24]

    Stefania Druga, Fee Lia Christoph, and Amy J Ko. 2022. Family as a Third Space for AI Literacies: How do children and parents learn about AI together?. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). Association for Computing Machinery, New York, NY, USA, 1–17. https://doi.org/10.1145/3491102.3502031

    work page doi:10.1145/3491102.3502031 2022

  25. [25]

    Vu, Eesh Likhith, and Tammy Qiu

    Stefania Druga, Sarah T. Vu, Eesh Likhith, and Tammy Qiu. 2019. Inclusive AI literacy for kids around the world. InProceedings of FabLearn 2019 (FL2019). Association for Computing Machinery, New York, NY, USA, 104–111. https: //doi.org/10.1145/3311890.3311904

    work page doi:10.1145/3311890.3311904 2019

  26. [26]

    Noyan Evirgen, Ruolin Wang, and Xiang ’Anthony Chen. 2024. From Text to Pix- els: Enhancing User Understanding through Text-to-Image Model Explanations. InProceedings of the 29th International Conference on Intelligent User Interfaces. ACM, Greenville SC USA, 74–87. https://doi.org/10.1145/3640543.3645173

    work page doi:10.1145/3640543.3645173 2024

  27. [27]

    Matteo Farinella. 2018. The potential of comics in science communication. Journal of Science Communication17, 1 (Jan. 2018), Y01. https://doi.org/10. How Creatives Approach GenAI Image Generation C&C ’26, July 13–16, 2026, London, United Kingdom 22323/2.17010401

    work page 2018

  28. [28]

    Rahel Flechtner and Aeneas Stankowski. 2023. AI Is Not a Wildcard: Challenges for Integrating AI into the Design Curriculum. InProceedings of the 5th Annual Symposium on HCI Education. ACM, Hamburg Germany, 72–77. https://doi. org/10.1145/3587399.3587410

    work page doi:10.1145/3587399.3587410 2023

  29. [29]

    Katja Fleischmann. 2024. Generative Artificial Intelligence in Graphic Design Education: A Student Perspective.Canadian Journal of Learning and Technology 50, 1 (Aug. 2024), 1–17. https://doi.org/10.21432/cjlt28618

    work page doi:10.21432/cjlt28618 2024

  30. [30]

    Katja Fleischmann. 2025. Preparing Creative Arts and Design Students for the New World of Generative Artificial Intelligence in the Workplace. In11th International Conference on Higher Education Advances (HEAd’25). Editorial Universitat Politècnica de València (edUPV), 206–213. https://doi.org/10.4995/ HEAd25.2025.19958

    work page arXiv 2025

  31. [31]

    Corey Ford, Elizabeth Wilson, Shuoyang Zheng, Gabriel Vigliensoni, Jeba Rezwana, Lanxi Xiao, Michael Paul Clemens, Makayla Lewis, Drew Hemment, Alan Chamberlain, Helen Kennedy, and Nick Bryan-Kinns. 2025. Explainable AI for the Arts 3 (XAIxArts3). InProceedings of the 2025 Conference on Creativity and Cognition (C&C ’25). Association for Computing Machine...

    work page doi:10.1145/3698061.3728392 2025

  32. [32]

    Glassman

    Katy Ilonka Gero, Meera Desai, Carly Schnitzler, Nayun Eom, Jack Cushman, and Elena L. Glassman. 2025. Creative Writers’ Attitudes on Writing as Training Data for Large Language Models. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems. ACM, Yokohama Japan, 1–16. https: //doi.org/10.1145/3706598.3713287

    work page doi:10.1145/3706598.3713287 2025

  33. [33]

    Malik Ghallab. 2019. Responsible AI: requirements and challenges.AI Perspec- tives1, 1 (Sept. 2019), 3. https://doi.org/10.1186/s42467-019-0003-z

    work page doi:10.1186/s42467-019-0003-z 2019

  34. [34]

    Frederic Gmeiner, Humphrey Yang, Lining Yao, Kenneth Holstein, and Nikolas Martelaro. 2023. Exploring Challenges and Opportunities to Support Designers in Learning to Co-create with AI-based Manufacturing Design Tools. InPro- ceedings of the 2023 CHI Conference on Human Factors in Computing Systems. ACM, Hamburg Germany, 1–20. https://doi.org/10.1145/3544...

    work page doi:10.1145/3544548.3580999 2023

  35. [35]

    Yuhan Guo, Hanning Shao, Can Liu, Kai Xu, and Xiaoru Yuan. 2024. PrompTHis: Visualizing the Process and Influence of Prompt Editing during Text-to-Image Creation.IEEE Transactions on Visualization and Computer Graphics(2024), 1–12. https://doi.org/10.1109/TVCG.2024.3408255

    work page doi:10.1109/tvcg.2024.3408255 2024

  36. [36]

    Angel Hsing-Chi Hwang. 2022. Too Late to be Creative? AI-Empowered Tools in Creative Processes. InCHI Conference on Human Factors in Computing Systems Extended Abstracts. ACM, New Orleans LA USA, 1–9. https://doi.org/10.1145/ 3491101.3503549

    work page arXiv 2022

  37. [37]

    Nanna Inie, Jeanette Falk, and Steve Tanimoto. 2023. Designing Participatory AI: Creative Professionals’ Worries and Expectations about Generative AI. InEx- tended Abstracts of the 2023 CHI Conference on Human Factors in Computing Sys- tems. ACM, Hamburg Germany, 1–8. https://doi.org/10.1145/3544549.3585657

    work page doi:10.1145/3544549.3585657 2023

  38. [38]

    Instagram. [n. d.]. Use AI generated stickers on Instagram | Instagram Help Center. https://help.instagram.com/231732506511219/?helpref=related_articles

    work page arXiv

  39. [39]

    Amir Jahanlou and Parmit K Chilana. 2024. How Example-Based Authoring of Motion Graphics Impacts Creative Expression: Differences in Perceptions of Professional and Casual Motion Designers. InCreativity and Cognition. ACM, Chicago IL USA, 347–357. https://doi.org/10.1145/3635636.3656197

    work page doi:10.1145/3635636.3656197 2024

  40. [40]

    Amir Jahanlou, William Odom, and Parmit Chilana. 2021. Challenges in Getting Started in Motion Graphic Design: Perspectives from Casual and Professional Motion Designers. (2021). https://graphicsinterface.org/wp-content/uploads/ gi2021-06.pdf

    work page 2021

  41. [41]

    Manu Kaur. 2021. Using productive failure to activate deeper learn- ing. https://www.timeshighereducation.com/campus/using-productive-failure- activate-deeper-learning

    work page 2021

  42. [42]

    Reishiro Kawakami and Sukrit Venkatagiri. 2024. The Impact of Generative AI on Artists. InCreativity and Cognition. ACM, Chicago IL USA, 79–82. https: //doi.org/10.1145/3635636.3664263

    work page doi:10.1145/3635636.3664263 2024

  43. [43]

    Patrick Gage Kelley and Allison Woodruff. 2023. Advancing Explainability Through AI Literacy and Design Resources.Interactions30, 5 (Sept. 2023), 34–38. https://doi.org/10.1145/3613249

    work page doi:10.1145/3613249 2023

  44. [44]

    Anjali Khurana, Hariharan Subramonyam, and Parmit K Chilana. 2024. Why and When LLM-Based Assistants Can Go Wrong: Investigating the Effectiveness of Prompt-Based Interactions for Software Help-Seeking. InProceedings of the 29th International Conference on Intelligent User Interfaces. ACM, Greenville SC USA, 288–303. https://doi.org/10.1145/3640543.3645200

    work page doi:10.1145/3640543.3645200 2024

  45. [45]

    One-Size-Fits-All

    Kimia Kiani, George Cui, Andrea Bunt, Joanna McGrenere, and Parmit K. Chi- lana. 2019. Beyond "One-Size-Fits-All": Understanding the Diversity in How Software Newcomers Discover and Make Use of Help Resources. InProceed- ings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, Glasgow Scotland Uk, 1–14. https://doi.org/10.1145/3290605.3300570

    work page doi:10.1145/3290605.3300570 2019

  46. [46]

    Correction

    Soomin Kim, Jinsu Eun, Changhoon Oh, and Joonhwan Lee. 2025. “Journey of Finding the Best Query”: Understanding the User Experience of AI Image Generation System.International Journal of Human–Computer Interaction41, 2 (Jan. 2025), 951–969. https://doi.org/10.1080/10447318.2024.2307670

    work page doi:10.1080/10447318.2024.2307670 2025

  47. [47]

    Howard J Klein. 1989. An integrated control theory model of work motivation. Academy of management review14, 2 (1989), 150–172

    work page 1989

  48. [48]

    Hyung-Kwon Ko, Gwanmo Park, Hyeon Jeon, Jaemin Jo, Juho Kim, and Jinwook Seo. 2023. Large-scale Text-to-Image Generation Models for Visual Artists’ Creative Works. InProceedings of the 28th International Conference on Intelligent User Interfaces. 919–933. https://doi.org/10.1145/3581641.3584078

    work page doi:10.1145/3581641.3584078 2023

  49. [49]

    Rafal Kocielnik, Saleema Amershi, and Paul N. Bennett. 2019. Will You Accept an Imperfect AI?: Exploring Designs for Adjusting End-user Expectations of AI Systems. InProceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, Glasgow Scotland Uk, 1–14. https://doi.org/10.1145/ 3290605.3300641

    work page arXiv 2019

  50. [50]

    David A. Kolb. 2014.Experiential learning: Experience as the source of learning and development. FT press. https://books.google.com/books?hl=en&lr= &id=jpbeBQAAQBAJ&oi=fnd&pg=PR7&dq=info:cBzPyc88vIgJ:scholar.google. com&ots=Vp8RmSWXMe&sig=oFUwjFrgLxfURMKrb5afKXj6Ttk

    work page 2014

  51. [51]

    Todd Kulesza, Margaret Burnett, Weng-Keen Wong, and Simone Stumpf. 2015. Principles of Explanatory Debugging to Personalize Interactive Machine Learn- ing. InProceedings of the 20th International Conference on Intelligent User In- terfaces (IUI ’15). Association for Computing Machinery, New York, NY, USA, 126–137. https://doi.org/10.1145/2678025.2701399

    work page doi:10.1145/2678025.2701399 2015

  52. [52]

    1991.Situated Learning: Legitimate Peripheral Participation

    Jean Lave and Etienne Wenger. 1991.Situated Learning: Legitimate Peripheral Participation. Cambridge University Press

    work page 1991

  53. [53]

    Dow, and Jane L

    Mingyi Li, Mengyi Chen, Sarah Luo, Yining Cao, Haijun Xia, Maitraye Das, Steven P. Dow, and Jane L. E. 2026. VizCrit: Exploring Strategies for Displaying Computational Feedback in a Visual Design Tool. InProceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI ’26). Association for Computing Machinery, New York, NY, USA, Article 8...

    work page doi:10.1145/3772318.3791579 2026

  54. [54]

    Lim and Anind K

    Brian Y. Lim and Anind K. Dey. 2010. Toolkit to support intelligibility in context- aware applications. InProceedings of the 12th ACM international conference on Ubiquitous computing (UbiComp ’10). Association for Computing Machinery, New York, NY, USA, 13–22. https://doi.org/10.1145/1864349.1864353

    work page doi:10.1145/1864349.1864353 2010

  55. [55]

    Haidan Liu, Poorvi Bhatia, Nicholas Vincent, and Parmit K Chilana. 2026. Trac- ing Everyday AI Literacy Discussions at Scale: How Online Creative Com- munities Make Sense of Generative AI. InProceedings of the 2026 CHI Con- ference on Human Factors in Computing Systems (CHI ’26). Association for Computing Machinery, New York, NY, USA, Article 488, 28 page...

    work page doi:10.1145/3772318.3791001 2026

  56. [56]

    Tania Lombrozo. 2006. The structure and function of explanations.Trends in Cognitive Sciences10, 10 (Oct. 2006), 464–470. https://doi.org/10.1016/j.tics. 2006.08.004

    work page doi:10.1016/j.tics 2006

  57. [57]

    Duri Long and Brian Magerko. 2020. What is AI Literacy? Competencies and Design Considerations. InProceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM, Honolulu HI USA, 1–16. https://doi.org/ 10.1145/3313831.3376727

    work page doi:10.1145/3313831.3376727 2020

  58. [58]

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

    work page

  59. [59]

    ISBN 9781450367080

    Novice-AI Music Co-Creation via AI-Steering Tools for Deep Generative Models. InProceedings of the 2020 CHI Conference on Human Factors in Comput- ing Systems. ACM, Honolulu HI USA, 1–13. https://doi.org/10.1145/3313831. 3376739

    work page doi:10.1145/3313831 2020

  60. [60]

    Mackay and Joanna McGrenere

    Wendy E. Mackay and Joanna McGrenere. 2025. Comparative Structured Ob- servation.ACM Transactions on Computer-Human Interaction32, 2 (April 2025), 1–27. https://doi.org/10.1145/3711838

    work page doi:10.1145/3711838 2025

  61. [61]

    Atefeh Mahdavi Goloujeh, Anne Sullivan, and Brian Magerko. 2024. Is It AI or Is It Me? Understanding Users’ Prompt Journey with Text-to-Image Generative AI Tools. InProceedings of the CHI Conference on Human Factors in Computing Sys- tems. ACM, Honolulu HI USA, 1–13. https://doi.org/10.1145/3613904.3642861

    work page doi:10.1145/3613904.3642861 2024

  62. [62]

    Damien Masson, Jo Vermeulen, George Fitzmaurice, and Justin Matejka. 2022. Supercharging Trial-and-Error for Learning Complex Software Applications. In CHI Conference on Human Factors in Computing Systems. ACM, New Orleans LA USA, 1–13. https://doi.org/10.1145/3491102.3501895

    work page doi:10.1145/3491102.3501895 2022

  63. [63]

    Vera Liao, Mary Lou Maher, Charles Patrick Martin, and Greg Walsh

    Michael Muller, Lydia B Chilton, Anna Kantosalo, Q. Vera Liao, Mary Lou Maher, Charles Patrick Martin, and Greg Walsh. 2023. GenAICHI 2023: Generative AI and HCI at CHI 2023. InExtended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems(Hamburg, Germany)(CHI EA ’23). Association for Computing Machinery, New York, NY, USA, Article 3...

    work page doi:10.1145/3544549.3573794 2023

  64. [64]

    Clifford Nass, Jonathan Steuer, and Ellen R. Tauber. 1994. Computers are social actors. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, Boston Massachusetts USA, 72–78. https://doi.org/10.1145/ 191666.191703

    work page arXiv 1994

  65. [65]

    Humza Naveed, Asad Ullah Khan, Shi Qiu, Muhammad Saqib, Saeed Anwar, Muhammad Usman, Naveed Akhtar, Nick Barnes, and Ajmal Mian. 2025. A Comprehensive Overview of Large Language Models.ACM Trans. Intell. Syst. Technol.16, 5, Article 106 (Aug. 2025), 72 pages. https://doi.org/10.1145/3744746

    work page doi:10.1145/3744746 2025

  66. [66]

    Davy Tsz Kit Ng, Jac Ka Lok Leung, Samuel Kai Wah Chu, and Maggie Shen Qiao. 2021. Conceptualizing AI literacy: An exploratory review.Computers and Education: Artificial Intelligence2 (2021), 100041. https://doi.org/10.1016/j.caeai. 2021.100041 C&C ’26, July 13–16, 2026, London, United Kingdom Liu et al

    work page doi:10.1016/j.caeai 2021

  67. [67]

    Davy Tsz Kit Ng, Jiahong Su, Jac Ka Lok Leung, and Samuel Kai Wah Chu

    work page

  68. [68]

    2024), 6204–6224

    Artificial intelligence (AI) literacy education in secondary schools: a review.Interactive Learning Environments32, 10 (Nov. 2024), 6204–6224. https: //doi.org/10.1080/10494820.2023.2255228

    work page doi:10.1080/10494820.2023.2255228 2024

  69. [69]

    NightCafe. 2022. How Does NightCafe AI Work? https://nightcafe.studio/ blogs/info/how-does-nightcafe-ai-work

    work page 2022

  70. [70]

    Donald Norman. 1987. Some Observations on Mental Models. InMental Models. Lawrence Erlbaum Associates, Inc., New Jersey, 7–14

    work page 1987

  71. [71]

    NVIDIA. [n. d.]. What is Generative AI? https://www.nvidia.com/en-us/ glossary/generative-ai/

    work page

  72. [72]

    OpenAI. [n. d.]. Editing your images with ChatGPT Images. https: //help.openai.com/en/articles/9055440-editing-your-images-with-chatgpt- images?utm_source=chatgpt.com

    work page arXiv

  73. [73]

    OpenAI. 2025. Production bias vs paradox

    work page 2025

  74. [74]

    OpenArt. [n. d.]. Create Art or Modify Images with AI. https://openart.ai/home

    work page

  75. [75]

    Jonas Oppenlaender. 2022. The Creativity of Text-to-Image Generation. InPro- ceedings of the 25th International Academic Mindtrek Conference. ACM, Tampere Finland, 192–202. https://doi.org/10.1145/3569219.3569352

    work page doi:10.1145/3569219.3569352 2022

  76. [76]

    Jonas Oppenlaender. 2024. A Taxonomy of Prompt Modifiers for Text-To-Image Generation.Behaviour & Information Technology43, 15 (Nov. 2024), 3763–3776. https://doi.org/10.1080/0144929X.2023.2286532

    work page doi:10.1080/0144929x.2023.2286532 2024

  77. [77]

    Jonas Oppenlaender, Rhema Linder, and Johanna Silvennoinen. 2025. Prompt- ing AI Art: An Investigation into the Creative Skill of Prompt Engineer- ing.International Journal of Human–Computer Interaction0, 0 (2025), 1–23. https://doi.org/10.1080/10447318.2024.2431761

    work page doi:10.1080/10447318.2024.2431761 2025

  78. [78]

    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. InCHI Conference on Human Factors in Computing Systems. ACM, New Orleans LA USA, 1–18. https://doi. org/10.1145/3491102.3501933

    work page doi:10.1145/3491102.3501933 2022

  79. [80]

    Srishti Palani and Gonzalo Ramos. 2024. Evolving Roles and Workflows of Creative Practitioners in the Age of Generative AI. InCreativity and Cognition. ACM, Chicago IL USA, 170–184. https://doi.org/10.1145/3635636.3656190

    work page doi:10.1145/3635636.3656190 2024

  80. [81]

    Xiaohan Peng, Janin Koch, and Wendy E. Mackay. 2024. DesignPrompt: Us- ing Multimodal Interaction for Design Exploration with Generative AI. In Proceedings of the 2024 ACM Designing Interactive Systems Conference (DIS ’24). Association for Computing Machinery, New York, NY, USA, 804–818. https://doi.org/10.1145/3643834.3661588

    work page doi:10.1145/3643834.3661588 2024

Showing first 80 references.