pith. machine review for the scientific record. sign in

arxiv: 2604.02760 · v1 · submitted 2026-04-03 · 💻 cs.HC

Recognition: no theorem link

AI Disclosure with DAISY

Yoana Ahmetoglu , Marios Constantinides , Anna Cox
Authors on Pith no claims yet

Pith reviewed 2026-05-13 19:01 UTC · model grok-4.3

classification 💻 cs.HC
keywords AI disclosuretransparencyDAISYresearch practicesAI toolshuman-computer interactiondisclosure statements
0
0 comments X

The pith

A form-based tool helps authors create more complete disclosures of AI use in research.

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

Growing use of AI in research creates a need for transparent disclosure, yet statements remain inconsistent due to various barriers. DAISY provides a structured form to guide authors in detailing where and how they used AI tools. A user study showed that authors using DAISY included more complete information and clearer breakdowns across research stages. Importantly, this structured approach did not make authors less comfortable with disclosing their AI use. The findings point to structured tools as a way to support better transparency practices in academic writing.

Core claim

The paper introduces DAISY as a form-based tool for generating AI disclosure statements, developed from literature-derived requirements and co-design sessions. In a study with 31 authors, DAISY-supported disclosures satisfied more completeness criteria and offered clearer breakdowns of AI use across research and writing tasks than disclosures created without support. Authors using the tool maintained similar comfort levels with their statements despite potential concerns about perceptions of AI use.

What carries the argument

DAISY, a structured form that prompts for specific details on AI tool use in different phases of research and writing.

If this is right

  • Disclosures become more standardized and informative for readers.
  • Authors face fewer cognitive barriers when reporting AI contributions.
  • Similar tools could be adopted in academic publishing platforms.
  • Transparency in AI use becomes easier to achieve without added discomfort.

Where Pith is reading between the lines

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

  • Structured disclosure forms might improve reporting in other areas like data sharing or methodology details.
  • Integration of DAISY-like prompts into writing assistants could automate part of the process.
  • Future work could examine long-term effects on how readers interpret AI-assisted research.

Load-bearing premise

The study assumes that the literature-derived completeness criteria truly measure meaningful transparency and that findings from a small group of 31 authors extend to other researchers and real-world submissions.

What would settle it

Observing no significant difference in disclosure completeness between DAISY users and non-users in a larger, more diverse sample of research submissions.

Figures

Figures reproduced from arXiv: 2604.02760 by Anna Cox, Marios Constantinides, Yoana Ahmetoglu.

Figure 1
Figure 1. Figure 1: Overview of the design and user study of DAISY, a form-based tool for AI disclosure in academic writing, showing [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Speculative AI disclosure artefacts created by 11 participants (P1-11) during four co-design sessions, sorted by [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The DAISY interface. The left panel shows the default, unfilled form. The right panel shows a completed form and the [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: (Left) Mean character count in disclosure statements by condition. Error bars indicate [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: (Left) Mean comfort ratings (0–10) by condition ( [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: A speculative conceptual design space for AI disclo [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Post-It Notes Created During the Co-Design Sessions [PITH_FULL_IMAGE:figures/full_fig_p022_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Thematic Analysis Used for Research Group Discussions and Refinement [PITH_FULL_IMAGE:figures/full_fig_p023_8.png] view at source ↗
read the original abstract

The use of AI tools in research is becoming routine, alongside growing consensus that such use should be transparently disclosed. However, AI disclosure statements remain rare and inconsistent, with policies offering limited guidance and authors facing social, cognitive, and emotional barriers when reporting AI use. To explore how structured disclosure shapes what authors report and how they experience disclosure, we present DAISY (Disclosure of AI-uSe in Your Research), a form-based tool for generating AI disclosure statements. DAISY was developed from literature-derived requirements and co-design (N =11), and deployed in a user study with authors (N=31). DAISY-supported disclosures met more completeness criteria, offering clearer breakdowns of AI use across research and writing than unsupported disclosures. Surprisingly, despite concerns about how transparently disclosed AI use might be perceived, the use of DAISY did not reduce author comfort with the disclosure statements. We discuss design implications and a research agenda for AI disclosure as a sociotechnical practice.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces DAISY, a form-based tool for generating structured AI disclosure statements in research. Developed from literature-derived requirements and a co-design phase (N=11), it was evaluated in a user study (N=31) comparing DAISY-supported disclosures against unsupported ones. The central finding is that DAISY disclosures met more completeness criteria, offered clearer breakdowns of AI use across research and writing phases, and did not reduce author comfort with disclosure.

Significance. If the empirical results hold under fuller reporting, the work provides a concrete design contribution to HCI on supporting transparent AI use in academic workflows. It addresses documented barriers to disclosure and supplies initial evidence that structured tooling can improve completeness without the expected comfort penalty, while outlining a sociotechnical research agenda. The co-design and comparative evaluation approach is a strength for grounding the tool in user needs.

major comments (2)
  1. [User Study] User Study section: The statistical reporting for the completeness comparison (e.g., exact tests, effect sizes, confidence intervals, and handling of the N=31 sample) is incomplete. This is load-bearing because the central claim of improved completeness rests on this difference; without these details it is impossible to rule out small-sample artifacts or post-hoc selection of criteria.
  2. [Methods / Completeness Criteria] Completeness Criteria derivation (likely §2 or Methods): The criteria are presented as literature-derived but receive no validation showing they predict downstream outcomes such as reviewer comprehension or perceived legitimacy. This assumption underpins the interpretation that higher scores equal meaningfully better transparency.
minor comments (2)
  1. [Abstract] Abstract: The word 'surprisingly' when describing unchanged comfort levels introduces interpretive tone that could be replaced with a neutral statement of the result.
  2. [Discussion] Discussion: The proposed research agenda would benefit from one or two concrete, falsifiable follow-up metrics (e.g., reviewer comprehension scores or submission-time disclosure rates) rather than remaining at a high level.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback on our manuscript. We address each major comment below and describe the revisions we will make to strengthen the paper.

read point-by-point responses

  1. Referee: [User Study] User Study section: The statistical reporting for the completeness comparison (e.g., exact tests, effect sizes, confidence intervals, and handling of the N=31 sample) is incomplete. This is load-bearing because the central claim of improved completeness rests on this difference; without these details it is impossible to rule out small-sample artifacts or post-hoc selection of criteria.

    Authors: We agree that the statistical reporting requires additional detail to support the central claim. In the revised manuscript we will expand the User Study section to report the exact statistical tests (including whether chi-square, Fisher's exact, or another test was used for the categorical completeness comparison), effect sizes, confidence intervals, and a clear account of how the N=31 sample was handled, including any adjustments for multiple comparisons or power considerations. These additions will allow readers to assess the robustness of the findings directly. revision: yes

  2. Referee: [Methods / Completeness Criteria] Completeness Criteria derivation (likely §2 or Methods): The criteria are presented as literature-derived but receive no validation showing they predict downstream outcomes such as reviewer comprehension or perceived legitimacy. This assumption underpins the interpretation that higher scores equal meaningfully better transparency.

    Authors: The completeness criteria were derived from a synthesis of existing literature on AI disclosure guidelines and academic publishing standards. We acknowledge that empirical validation against downstream outcomes such as reviewer comprehension would provide stronger evidence of their practical value. However, conducting such validation would require a separate study involving reviewers and lies outside the scope of the current work, which focuses on the tool's impact on authors' disclosure completeness and comfort. In the revision we will clarify the literature basis for the criteria and explicitly identify downstream validation as an important direction for future research. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical tool evaluation relies on external literature criteria and participant data

full rationale

The paper presents an empirical study of the DAISY disclosure tool. It derives requirements from prior literature and a separate co-design session (N=11), then evaluates via a user study (N=31) comparing DAISY-supported vs. unsupported disclosures against those literature-derived completeness criteria. No equations, fitted parameters, predictions, or derivations appear. Central claims rest on direct participant outputs and external benchmarks rather than any self-referential reduction or self-citation chain. This is a standard self-contained empirical evaluation with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that transparent AI disclosure is desirable and that structured forms can overcome reported author barriers. No free parameters or invented entities are introduced.

axioms (1)
  • domain assumption AI use in research should be transparently disclosed and structured guidance improves reporting completeness
    Stated in the abstract as growing consensus and motivation for the tool.

pith-pipeline@v0.9.0 · 5462 in / 1250 out tokens · 28151 ms · 2026-05-13T19:01:08.281626+00:00 · methodology

discussion (0)

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

Forward citations

Cited by 1 Pith paper

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

  1. A Faceted Proposal for Transparent Attribution of AI-Assisted Text Production

    cs.CY 2026-04 unverdicted novelty 5.0

    A faceted model based on Form, Generation, Evaluation with extensions for Intent, Control, and Traceability is proposed as a minimal baseline for representing AI-assisted text production at multiple document scales.

Reference graph

Works this paper leans on

63 extracted references · 63 canonical work pages · cited by 1 Pith paper

  1. [1]

    2025.AI Usage Label Generator

    Abacus.ai. 2025.AI Usage Label Generator. Abacus.ai. https://aiusagefacts. abacusai.app AI usage labeling tool for manuscripts (accessed January 20, 2026)

    work page 2025

  2. [2]

    Asmaa T Aldulaijan and Shatha M Almalky. 2025. The impact of generative AI tools on postgraduate students’ learning experiences: New insights into usage patterns.Journal of Information Technology Education: Research24 (2025), 003. doi:10.28945/5428

    work page doi:10.28945/5428 2025

  3. [3]

    In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

    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(Glasgow, Scotland Uk)(CHI ’19). Associa...

    work page doi:10.1145/3290605.3300233 2019

  4. [4]

    Mike Ananny and Kate Crawford. 2018. Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability.New Media & Society20, 3 (2018), 973–989. doi:10.1177/1461444816676645

    work page doi:10.1177/1461444816676645 2018

  5. [5]

    M Ans, L Maggio, H Algodi, J Costello, E Driessen, K Oswald, and L Lingard. 2025. The presence and nature of AI-use disclosure statements in medical education journals: a bibliometric study.medRxiv(2025), 2025–11. doi:10.1101/2025.11.11. 25340015

    work page doi:10.1101/2025.11.11 2025

  6. [6]

    Association for Computing Machinery. [n. d.]. ACM Policy on Authorship. https: //www.acm.org/publications/policies/new-acm-policy-on-authorship. Accessed: 2025-12-04

    work page 2025

  7. [7]

    Aorigele Bao and Yi Zeng. 2025. AI disclosure, moral shame, and the punishment of honesty.Accountability in Research(2025), 1–14. doi:10.1080/08989621.2025. 2542197

    work page doi:10.1080/08989621.2025 2025

  8. [8]

    Tigmanshu Bhatnagar, Maarya Omar, Davor Orlic, James Smith, Catherine Hol- loway, and Maria Kett. 2025. Bridging AI and Humanitarianism: An HCI-Informed Framework for Responsible AI Adoption. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing Ma- chinery, New York, NY, USA, Article 1098, 17 ...

    work page doi:10.1145/3706598.3713184 2025

  9. [9]

    Edyta Paulina Bogucka, Marios Constantinides, Sanja Šćepanović, and Daniele Quercia. 2025. Impact Assessment Card: Communicating Risks and Benefits of AI Uses.Proceedings of the ACM on Human-Computer Interaction9, 7 (2025), 1–42. doi:10.1145/3757482

    work page doi:10.1145/3757482 2025

  10. [10]

    Richard Brown, Elizabeth Sillence, and Dawn Branley-Bell. 2025. AcademAI: Investigating AI usage, attitudes, and literacy in higher education and research. Journal of Educational Technology Systems54, 1 (2025), 6–33. doi:10.1177/ 00472395251347304

    work page 2025

  11. [11]

    Juliane Busboom, Nina Boulus-Rødje, and Susanne Bødker. 2025. Tracing Trans- formations of the Modern Workplace and Imagining its Future. InProceedings of the 4th Annual Symposium on Human-Computer Interaction for Work. 1–14. doi:10.1145/3729176.3729186

    work page doi:10.1145/3729176.3729186 2025

  12. [12]

    Cecilia Ka Yuk Chan. 2025. Understanding AI guilt: the development, pilot- testing, and validation of an instrument for students.Education and Information Technologies(2025), 1–20. doi:10.1007/s10639-025-13629-y

    work page doi:10.1007/s10639-025-13629-y 2025

  13. [13]

    Jilin Chen and Joseph A Konstan. 2010. Conference paper selectivity and impact. Commun. ACM53, 6 (2010), 79–83

    work page 2010

  14. [14]

    Clarivate. 2023. Clarivate Unveils Journal Citation Reports 2023: A Trusted Resource to Support Research Integrity and Promote Accurate Journal Evaluation. https://clarivate.com/news/clarivate-unveils-journal-citation-reports-2023- a-trusted-resource-to-support-research-integrity-and-promote-accurate- journal-evaluation/. Accessed: 2025-12-01

    work page 2023

  15. [15]

    Jérémie F Cohen and David Moher. 2025. Generative artificial intelligence and academic writing: friend or foe?Journal of Clinical Epidemiology179 (2025), 111646. doi:10.1016/j.jclinepi.2024.111646

    work page doi:10.1016/j.jclinepi.2024.111646 2025

  16. [16]

    Marios Constantinides, Edyta Bogucka, Daniele Quercia, Susanna Kallio, and Mohammad Tahaei. 2024. RAI Guidelines: Method for Generating Responsible AI Guidelines Grounded in Regulations and Usable by (Non-)Technical Roles. Proc. ACM Hum.-Comput. Interact.8, CSCW2, Article 388 (Nov. 2024), 28 pages. doi:10.1145/3686927

    work page doi:10.1145/3686927 2024

  17. [17]

    Marios Constantinides, Himanshu Verma, Shadan Sadeghian, and Abdallah El Ali

    work page

  18. [18]

    InProceedings of the 4th Annual Symposium on Human-Computer Interaction for Work (CHIWORK ’25)

    The Future of Work is Blended, Not Hybrid. InProceedings of the 4th Annual Symposium on Human-Computer Interaction for Work (CHIWORK ’25). Association for Computing Machinery, New York, NY, USA, Article 28, 13 pages. doi:10.1145/3729176.3729202

    work page doi:10.1145/3729176.3729202

  19. [19]

    COPE Council. 2024. Authorship and AI tools.COPE Position. Disponibil la https://publicationethics. org/guidance/cope-position/authorship-and-ai-tools#:˜: text= Ethics% 20publicationethics, legal. Accesat în16 (2024). doi:10.24318/ cCVRZBms

    work page 2024

  20. [20]

    Hyo Jin Do, Molly Q Feldman, Jessica He, Angel Hsing-Chi Hwang, and Seyun Kim. 2025. Navigating Generative AI Disclosure, Ownership, and Accountability in Co-Creative Domains. InAdjunct Proceedings of the 4th Annual Symposium on Human-Computer Interaction for Work (CHIWORK ’25 Adjunct). Association for Computing Machinery, Article 2, 4 pages. doi:10.1145/...

    work page doi:10.1145/3707640.3729212 2025

  21. [21]

    Kirstin Early, Jessica Hammer, Megan Kelly Hofmann, Jennifer A Rode, Anna Wong, and Jennifer Mankoff. 2018. Understanding gender equity in author order assignment.Proceedings of the ACM on Human-Computer Interaction2, CSCW (2018), 1–21. doi:10.1145/3274315

    work page doi:10.1145/3274315 2018

  22. [22]

    Abdallah El Ali, Karthikeya Puttur Venkatraj, Sophie Morosoli, Laurens Naudts, Natali Helberger, and Pablo Cesar. 2024. Transparent AI Disclosure Obligations: Who, What, When, Where, Why, How. InExtended Abstracts of the CHI Confer- ence on Human Factors in Computing Systems(Honolulu, HI, USA)(CHI EA ’24). Association for Computing Machinery, New York, NY...

    work page doi:10.1145/3613905.3650750 2024

  23. [23]

    Elsevier. [n. d.]. Publishing Ethics and Author Guidelines. https://www.elsevier. com/about/policies-and-standards/publishing-ethics. Accessed: 2025-12-04

    work page 2025

  24. [24]

    Ziv Epstein, Aaron Hertzmann, Investigators of Human Creativity, Memo Akten, Hany Farid, Jessica Fjeld, Morgan R Frank, Matthew Groh, Laura Herman, Neil Leach, et al. 2023. Art and the science of generative AI.Science380, 6650 (2023), 1110–1111

    work page 2023

  25. [25]

    Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé Iii, and Kate Crawford. 2021. Datasheets for datasets. Commun. ACM64, 12 (2021), 86–92

    work page 2021

  26. [26]

    Tom Gross and Mirko Fetter. 2010. Disclosure Templates for Selective Information Disclosure.Proceedings of IADIS CT(2010), 101–108

    work page 2010

  27. [27]

    Hilda Hadan, Derrick M Wang, Reza Hadi Mogavi, Joseph Tu, Leah Zhang- Kennedy, and Lennart E Nacke. 2024. The great AI witch hunt: Reviewers’ perception and (Mis) conception of generative AI in research writing.Computers in Human Behavior: Artificial Humans2, 2 (2024), 100095. doi:10.1016/j.chbah. 2024.100095

    work page doi:10.1016/j.chbah 2024

  28. [28]

    Eric Horvitz. 1999. Principles of mixed-initiative user interfaces. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems(Pittsburgh, Pennsylvania, USA)(CHI ’99). Association for Computing Machinery, New York, NY, USA, 159–166. doi:10.1145/302979.303030

    work page doi:10.1145/302979.303030 1999

  29. [29]

    International Science Council. 2025. AI Disclosure in Research: Towards a Global Reporting Standard. https://council.science/our-work/ai-disclosure-in-research/. Accessed: 2026-04-01

    work page 2025

  30. [30]

    Maurice Jakesch, Jeffrey T Hancock, and Mor Naaman. 2023. Human heuristics for AI-generated language are flawed.Proceedings of the National Academy of Sciences120, 11 (2023), e2208839120. doi:10.1073/pnas.2208839120

    work page doi:10.1073/pnas.2208839120 2023

  31. [31]

    Johnson, Suzanne B

    Eric J. Johnson, Suzanne B. Shu, Benedict G. C. Dellaert, Craig Fox, Daniel G. Goldstein, Gerald Häubl, Richard P. Larrick, John W. Payne, Ellen Peters, David Schkade, Brian Wansink, and Elke U. Weber. 2012. Beyond Nudges: Tools of a Choice Architecture.Marketing Letters23, 2 (June 2012), 487–504. doi:10.1007/ s11002-012-9186-1

    work page 2012

  32. [32]

    Gregory E Kaebnick, David Christopher Magnus, Audiey Kao, Mohammad Hos- seini, David Resnik, Veljko Dubljević, Christy Rentmeester, Bert Gordijn, and Mark J Cherry. 2023. Editors’ statement on the responsible use of generative AI technologies in scholarly journal publishing.Medicine, Health Care and Philoso- phy26, 4 (2023), 499–503. doi:10.1007/s11019-02...

    work page doi:10.1007/s11019-023-10176-6 2023

  33. [33]

    Shivani Kapania, Ruiyi Wang, Toby Jia-Jun Li, Tianshi Li, and Hong Shen. 2025. ’I’m Categorizing LLM as a Productivity Tool’: Examining Ethics of LLM Use in HCI Research Practices.Proc. ACM Hum.-Comput. Interact.9, 2, Article CSCW102 (May 2025), 26 pages. doi:10.1145/3711000

    work page doi:10.1145/3711000 2025

  34. [34]

    Harold H Kelley. 1967. Attribution theory in social psychology. InNebraska Symposium on Motivation. University of Nebraska Press

    work page 1967

  35. [35]

    2025.Nurturing Capabilities: Unpacking the Gap in Human-Centered Evaluations of AI-Based Systems

    Aman Khullar, Nikhil Nalin, Abhishek Prasad, Ann John Mampilli, and Neha Kumar. 2025.Nurturing Capabilities: Unpacking the Gap in Human-Centered Evaluations of AI-Based Systems. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3706598.3713278

    work page doi:10.1145/3706598.3713278 2025

  36. [36]

    Weixin Liang, Zachary Izzo, Yaohui Zhang, Haley Lepp, Hancheng Cao, Xuan- dong Zhao, Lingjiao Chen, Haotian Ye, Sheng Liu, Zhi Huang, et al. 2024. Moni- toring ai-modified content at scale: A case study on the impact of chatgpt on ai conference peer reviews.arXiv preprint arXiv:2403.07183(2024)

    work page arXiv 2024

  37. [37]

    Zhehui Liao, Maria Antoniak, Inyoung Cheong, Evie Yu-Yen Cheng, Ai-Heng Lee, Kyle Lo, Joseph Chee Chang, and Amy X Zhang. 2025. LLMs as Research Tools: A Large Scale Survey of Researchers’ Usage and Perceptions. InSecond Conference on Language Modeling

    work page 2025

  38. [38]

    Xiaochen Luo, Zixuan Wang, Jacqueline L Tilley, Sanjeev Balarajan, Ukeme- Abasi Bassey, and Choi Ieng Cheang. 2025. Seeking Emotional and Mental Health Support From Generative AI: Mixed-Methods Study of ChatGPT User Experiences.JMIR Mental Health12, 1 (2025), e77951. doi:10.2196/77951

    work page doi:10.2196/77951 2025

  39. [39]

    Miro. 2025. Miro Board Used During Co-Design Sessions. https://miro.com/ app/board/uXjVGe3PStg=/?share_link_id=406570345427. Online collaborative whiteboard used for participatory co-design activities

    work page 2025

  40. [40]

    Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019. Model cards for model reporting. InProceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT). 220–229. doi:10.1145/3287560.3287596

    work page doi:10.1145/3287560.3287596 2019

  41. [41]

    Andrew S Nelson, Paola V Santamaría, Josephine S Javens, and Marvin Ricaurte

    work page

  42. [42]

    doi:10.3390/educsci15050611

    Students’ Perceptions of Generative Artificial Intelligence (GenAI) Use in Academic Writing in English as a Foreign Language.Education Sciences15, 5 (2025), 611. doi:10.3390/educsci15050611

    work page doi:10.3390/educsci15050611 2025

  43. [43]

    Guy Paré, Marie-Claude Trudel, Mirou Jaana, and Spyros Kitsiou. 2015. Synthesiz- ing information systems knowledge: A typology of literature reviews.Information AI Disclosure with DAISY CHIWORK ’26, June 22–25, 2026, Linz, Austria & Management52, 2 (2015), 183–199. doi:10.1016/j.im.2014.08.008

    work page doi:10.1016/j.im.2014.08.008 2015

  44. [44]

    Inioluwa Deborah Raji, Andrew Smart, Rebecca N White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes. 2020. Closing the AI accountability gap: Defining an end-to-end frame- work for internal algorithmic auditing. InProceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT)....

    work page doi:10.1145/3351095 2020

  45. [45]

    Pooja SB Rao, Sanja Šćepanović, Ke Zhou, Edyta Paulina Bogucka, and Daniele Quercia. 2025. RiskRAG: A Data-Driven Solution for Improved AI Model Risk Re- porting. InProceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI). 1–26. doi:10.1145/3706598.3713979

    work page doi:10.1145/3706598.3713979 2025

  46. [46]

    Jessica A Reif, Richard P Larrick, and Jack B Soll. 2025. Evidence of a social evaluation penalty for using AI.Proceedings of the National Academy of Sciences 122, 19 (2025), e2426766122. doi:10.1073/pnas.2426766122

    work page doi:10.1073/pnas.2426766122 2025

  47. [47]

    David B Resnik and Mohammad Hosseini. 2025. Disclosing artificial intelligence use in scientific research and publication: When should disclosure be mandatory, optional, or unnecessary?Accountability in Research(2025), 1–13. doi:10.1080/ 08989621.2025.2481949

    work page arXiv 2025

  48. [48]

    Yvonne Rogers. 2012. HCI Theory: Classical, Modern, and Contemporary.Syn- thesis Lectures on Human-Centered Informatics(2012)

    work page 2012

  49. [49]

    Advait Sarkar. 2025. AI Could Have Written This: Birth of a Classist Slur in Knowledge Work. InProceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. 1–12. doi:10.1145/3706599.3716239

    work page doi:10.1145/3706599.3716239 2025

  50. [50]

    Alexander Skulmowski. 2024. Placebo or assistant? Generative AI between externalization and anthropomorphization.Educational Psychology Review36, 2 (2024), 58. doi:10.1007/s10648-024-09894-x

    work page doi:10.1007/s10648-024-09894-x 2024

  51. [51]

    Pragati Sontake. 2025. A Review on Artificial Intelligence (AI) Tools in Research Writing.International Journal of Scientific Research and Technology(2025)

    work page 2025

  52. [52]

    Springer Nature. [n. d.]. Springer Nature Authorship Principles. https://www. springer.com/gp/editorial-policies/authorship-principles. Accessed: 2025-12-04

    work page 2025

  53. [53]

    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 2024 CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA)(CHI ’24). Association for Computing Machinery, New York, NY, USA, Art...

    work page doi:10.1145/3613904.3642400 2024

  54. [54]

    Gareth Terry, Nikki Hayfield, Victoria Clarke, Virginia Braun, et al. 2017. The- matic analysis.The SAGE handbook of qualitative research in psychology2, 17-37 (2017), 25

    work page 2017

  55. [55]

    Roger Tourangeau and Ting Yan. 2007. Sensitive questions in surveys.Psycho- logical bulletin133, 5 (2007), 859. doi:doi/10.1037/0033-2909.133.5.859

    work page doi:10.1037/0033-2909.133.5.859 2007

  56. [56]

    UC Merced Library. 2025. Artificial Intelligence (AI): Publisher Policies and Requirements. https://libguides.ucmerced.edu/artificial-intelligence/publisher- policies. Accessed: YYYY-MM-DD

    work page 2025

  57. [57]

    Karthikeya Puttur Venkatraj, Sophie Morosoli, Hannes Cools, Laurens Naudts, Claes de Vreese, Natali Helberger, Pablo Cesar, and Abdallah El Ali. 2025. Under- standing AI Disclosure Needs for News Production and Journalism. InProceedings of the 24th International Conference on Mobile and Ubiquitous Multimedia. 202– 208

    work page 2025

  58. [58]

    Kari D Weaver. 2024. The artificial intelligence disclosure (AID) framework: an introduction.arXiv preprint arXiv:2408.01904(2024)

    work page arXiv 2024

  59. [59]

    Tongshuang Wu, Ellen Jiang, Aaron Donsbach, Jeff Gray, Alejandra Molina, Michael Terry, and Carrie J Cai. 2022. PromptChainer: Chaining Large Language Model Prompts through Visual Programming. InExtended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems(New Orleans, LA, USA) (CHI EA ’22). Association for Computing Machinery, New Y...

    work page doi:10.1145/3491101.3519729 2022

  60. [60]

    2011.A Guide to Form Elements

    Yell Business. 2011.A Guide to Form Elements. https://business.yell.com/insights/ website-design/a-guide-to-form-elements/ Accessed: 26 January 2026

    work page 2011

  61. [61]

    John Zimmerman, Jodi Forlizzi, and Shelley Evenson. 2007. Research through design as a method for interaction design research in HCI. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems(San Jose, California, USA)(CHI ’07). Association for Computing Machinery, New York, NY, USA, 493–502. doi:10.1145/1240624.1240704

    work page doi:10.1145/1240624.1240704 2007

  62. [62]

    Tim Zindulka, Sven Goller, Daniela Fernandes, Robin Welsch, and Daniel Buschek

    work page

  63. [63]

    It’s impressive, but in practice

    The AI Memory Gap: Users Misremember What They Created With AI or Without.arXiv preprint arXiv:2509.11851(2025). CHIWORK ’26, June 22–25, 2026, Linz, Austria Ahmetoglu et al. Appendix A Corpus of Analyzed Publications Table 4: Corpus of analyzed publications included in this study. ID Venue / Publisher Year Title C1 ACM CHI 2025 “It’s impressive, but in p...

    work page arXiv 2025