pith. machine review for the scientific record. sign in

arxiv: 2602.01386 · v2 · submitted 2026-02-01 · 💻 cs.HC · cs.AI

"If You're Very Clever, No One Knows You've Used It": The Social Dynamics of Developing Generative AI Literacy in the Workplace

Qing Nancy Xia , Marios Constantinides , Advait Sarkar , Duncan Brumby , Anna Cox This is my paper

Pith reviewed 2026-05-16 08:23 UTC · model grok-4.3

classification 💻 cs.HC cs.AI
keywords generative AIAI literacyworkplace learningknowledge sharingtransparencysocial dynamicsknowledge workersprofessional development
0
0 comments X

The pith

Workers learn generative AI from colleagues but hide their usage to prove personal expertise, reducing team learning and transparency.

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

The paper investigates how knowledge workers build generative AI skills through real workplace interactions rather than formal training. Interviews show that sharing tips with colleagues helps people get started with the tools, yet many deliberately erase signs of AI assistance from their outputs. This hiding is interpreted as evidence of strong individual domain knowledge. The resulting secrecy cuts off further sharing and makes it harder for teams to see how the technology is actually being used. The authors conclude that workplaces need deliberate steps toward openness to keep up with fast-changing tools.

Core claim

In interviews with 19 knowledge workers, colleague knowledge sharing was found to support initial GenAI learning, yet the ability to remove cues of AI involvement was perceived as validation of domain expertise. These concealment behaviors reduced opportunities for ongoing knowledge sharing and undermined transparency about technology use in professional work.

What carries the argument

The perception that erasing indicators of generative AI use validates personal expertise, which in turn discourages open sharing of learning experiences.

Load-bearing premise

Self-reported perceptions from 19 interviews accurately capture actual workplace behaviors and generalize beyond the sampled knowledge workers without major social desirability or recall bias.

What would settle it

Direct observation of GenAI output in shared documents combined with measures of actual knowledge-sharing frequency would show whether hiding use correlates with reduced learning opportunities.

Figures

Figures reproduced from arXiv: 2602.01386 by Advait Sarkar, Anna Cox, Duncan Brumby, Marios Constantinides, Qing Nancy Xia.

Figure 1
Figure 1. Figure 1: Relationships between competencies and learning strategies identified in the study. Knowledge workers valued [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
read the original abstract

Generative AI (GenAI) tools are rapidly transforming knowledge work, making AI literacy a critical priority for organizations. However, research on AI literacy lacks empirical insight into how knowledge workers' beliefs around GenAI literacy are shaped by the social dynamics of the workplace, and how workers learn to apply GenAI tools in these environments. To address this gap, we conducted in-depth interviews with 19 knowledge workers across multiple sectors to examine how they develop GenAI competencies in real-world professional contexts. We found that, while knowledge sharing from colleagues supported learning, the ability to remove cues indicating GenAI use was perceived as validation of domain expertise. These behaviours ultimately reduced opportunities for learning via knowledge sharing and undermined transparency. To advance workplace AI literacy, we argue for fostering open dialogue, increasing visibility of user-generated knowledge, and greater emphasis on the benefits of collaborative learning for navigating rapid technological developments.

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

1 major / 2 minor

Summary. The manuscript reports findings from in-depth interviews with 19 knowledge workers across sectors on how social dynamics shape the development of generative AI (GenAI) literacy in professional settings. It finds that colleague knowledge sharing supports learning, yet the perceived ability to remove cues of GenAI use validates domain expertise; these behaviors are argued to ultimately reduce opportunities for learning via knowledge sharing and undermine transparency. The authors conclude by advocating open dialogue, greater visibility of user-generated knowledge, and emphasis on collaborative learning to advance workplace AI literacy.

Significance. If the described dynamics hold, the work offers timely empirical insight into a potential barrier to collective AI literacy: individual concealment strategies that may suppress knowledge sharing. This qualitative evidence from real workplace contexts can inform organizational policies on AI training and culture, complementing more quantitative adoption studies. The focus on social validation and transparency adds a novel angle to HCI research on GenAI use.

major comments (1)
  1. [Abstract and Discussion] Abstract and Discussion: The central claim that concealing GenAI cues 'ultimately reduced opportunities for learning via knowledge sharing' rests entirely on retrospective self-reports from 19 interviews. Without behavioral observations, usage logs, or longitudinal tracking, this inference from perception to enacted reduction in sharing is interpretive and load-bearing for the transparency-undermining argument; the manuscript should explicitly qualify the evidential limits and discuss potential recall or desirability biases.
minor comments (2)
  1. [Methods] Methods: Provide additional detail on how the semi-structured interview guide was piloted and how probes were used to elicit concrete examples rather than general perceptions.
  2. [Findings] Findings: Some participant quotes could be shortened or contextualized with role/sector to improve readability without losing richness.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on the evidential basis of our claims. We address the major comment below and will revise the manuscript to strengthen its transparency.

read point-by-point responses

  1. Referee: [Abstract and Discussion] Abstract and Discussion: The central claim that concealing GenAI cues 'ultimately reduced opportunities for learning via knowledge sharing' rests entirely on retrospective self-reports from 19 interviews. Without behavioral observations, usage logs, or longitudinal tracking, this inference from perception to enacted reduction in sharing is interpretive and load-bearing for the transparency-undermining argument; the manuscript should explicitly qualify the evidential limits and discuss potential recall or desirability biases.

    Authors: We agree that the central claim relies on retrospective self-reports from the 19 interviews and that the inference to reduced knowledge sharing opportunities is interpretive. In-depth qualitative interviews are a standard and appropriate method in HCI research for surfacing participants' perceptions of social dynamics and their believed effects on behavior. However, we acknowledge that this does not provide direct evidence of enacted reductions in sharing and that recall or social desirability biases could influence reports. In the revised manuscript we will explicitly qualify these limits in both the Abstract and Discussion: we will add text noting the reliance on self-reported perceptions, the interpretive nature of the link to reduced learning opportunities, and the absence of behavioral or longitudinal data. We will also note that future work could usefully combine interviews with observational or log-based methods to test these dynamics more directly. revision: yes

Circularity Check

0 steps flagged

No significant circularity; qualitative claims grounded in interview data

full rationale

The paper is a qualitative interview study with 19 knowledge workers. All claims (e.g., knowledge sharing supporting learning, removal of AI cues perceived as expertise validation, and resulting reduction in learning opportunities) are presented as direct interpretations of participant responses. No equations, parameter fitting, self-definitional loops, fitted inputs renamed as predictions, or load-bearing self-citation chains exist. The analysis is self-contained against the collected data without reducing to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the assumption that interview data from a limited sample captures genuine social dynamics without major distortion from self-presentation biases.

axioms (1)
  • domain assumption Self-reported interview responses accurately reflect participants' actual workplace behaviors and perceptions
    Standard assumption in qualitative HCI research but vulnerable to social desirability bias in discussions of expertise and tool use.

pith-pipeline@v0.9.0 · 5469 in / 1065 out tokens · 43861 ms · 2026-05-16T08:23:47.998543+00:00 · methodology

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

144 extracted references · 144 canonical work pages · 4 internal anchors

  1. [1]

    Ackerman

    Mark S. Ackerman. 2000. The Intellectual Challenge of CSCW: The Gap Between Social Requirements and Technical Feasibility.Human–Computer Interaction 15, 2-3 (Sept. 2000), 179–203. doi:10.1207/S15327051HCI1523_5

    work page doi:10.1207/s15327051hci1523_5 2000

  2. [2]

    Yoana Ahmetoglu, Marios Constantinides, and Anna Cox. 2026. AI Disclosure with DAISY. doi:10.48550/arXiv.2604.02760 arXiv:2604.02760 [cs]

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2604.02760 2026

  3. [3]

    Sumeyya Akdilek, Ibrahim Akdilek, and Narissra Maria Punyanunt-Carter. 2024. The Influence of Generative AI on Interpersonal Communication Dynamics:. In Advances in Educational Technologies and Instructional Design, Sanae Elmoudden and Jason S. Wrench (Eds.). IGI Global, 167–190. doi:10.4018/979-8-3693-0831- 8.ch009

    work page doi:10.4018/979-8-3693-0831- 2024

  4. [4]

    Albert and Daniel Smilek

    Derek A. Albert and Daniel Smilek. 2023. Comparing Attentional Disengage- ment between Prolific and MTurk Samples.Scientific Reports13, 1 (Nov. 2023), 20574. doi:10.1038/s41598-023-46048-5

    work page doi:10.1038/s41598-023-46048-5 2023

  5. [5]

    Bernstein, and Margaret Levi

    Ali Alkhatib, Michael S. Bernstein, and Margaret Levi. 2017. Examining Crowd Work and Gig Work Through The Historical Lens of Piecework. InProceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, Denver Colorado USA, 4599–4616. doi:10.1145/3025453.3025974

    work page doi:10.1145/3025453.3025974 2017

  6. [6]

    Omaima Almatrafi, Aditya Johri, and Hyuna Lee. 2024. A Systematic Review of AI Literacy Conceptualization, Constructs, and Implementation and Assessment Efforts (2019–2023).Computers and Education Open6 (June 2024), 100173. doi:10.1016/j.caeo.2024.100173

    work page doi:10.1016/j.caeo.2024.100173 2024

  7. [7]

    Tatiana Andreeva and Paola Zappa. 2023. Whose Lips Are Sealed? Gender Differ- ences in Knowledge Hiding at Work.Journal of Occupational and Organizational Psychology96, 4 (2023), 828–855. doi:10.1111/joop.12444

    work page doi:10.1111/joop.12444 2023

  8. [8]

    Ravinithesh Annapureddy, Alessandro Fornaroli, and Daniel Gatica-Perez. 2025. Generative AI Literacy: Twelve Defining Competencies.Digit. Gov.: Res. Pract. 6, 1 (Feb. 2025), 13:1–13:21. doi:10.1145/3685680

    work page doi:10.1145/3685680 2025

  9. [9]

    Weld, Walter S

    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. doi:10.1609/aaai.v33i01. 33012429

    work page doi:10.1609/aaai.v33i01 2019

  10. [10]

    Edyta Paulina Bogucka, Marios Constantinides, Sanja Šćepanović, and Daniele Quercia. 2025. Impact Assessment Card: Communicating Risks and Benefits of AI Uses.Proc. ACM Hum.-Comput. Interact.9, 7 (Oct. 2025), CSCW301:1– CSCW301:42. doi:10.1145/3757482

    work page doi:10.1145/3757482 2025

  11. [11]

    On the Opportunities and Risks of Foundation Models

    Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, and et al. 2022. On the Opportunities and Risks of Foundation Models. doi:10.48550/arXiv.2108. 07258 arXiv:2108.07258 [cs]

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2108 2022

  12. [12]

    Lane, Miaomiao Zhang, Vladimir Jacimovic, and Karim R

    Léonard Boussioux, Jacqueline N. Lane, Miaomiao Zhang, Vladimir Jacimovic, and Karim R. Lakhani. 2024. The Crowdless Future? Generative AI and Creative Problem-Solving.Organization Science35, 5 (Sept. 2024), 1589–1607. doi:10. 1287/orsc.2023.18430

    work page arXiv 2024

  13. [13]

    Michelle Brachman, Amina El-Ashry, Casey Dugan, and Werner Geyer. 2024. How Knowledge Workers Use and Want to Use LLMs in an Enterprise Context. InExtended Abstracts of the CHI Conference on Human Factors in Computing Systems. ACM, Honolulu HI USA, 1–8. doi:10.1145/3613905.3650841

    work page doi:10.1145/3613905.3650841 2024

  14. [14]

    Virginia Braun and Victoria Clarke. 2019. Reflecting on Reflexive Thematic Analysis.Qualitative Research in Sport, Exercise and Health11, 4 (Aug. 2019), 589–597. doi:10.1080/2159676X.2019.1628806

    work page doi:10.1080/2159676x.2019.1628806 2019

  15. [15]

    Virginia Braun and Victoria Clarke. 2021. To Saturate or Not to Saturate? Questioning Data Saturation as a Useful Concept for Thematic Analysis and Sample-Size Rationales.Qualitative Research in Sport, Exercise and Health13, 2 (March 2021), 201–216. doi:10.1080/2159676X.2019.1704846

    work page doi:10.1080/2159676x.2019.1704846 2021

  16. [16]

    Kelly Caine. 2016. Local Standards for Sample Size at CHI. InProceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, San Jose California USA, 981–992. doi:10.1145/2858036.2858498

    work page doi:10.1145/2858036.2858498 2016

  17. [17]

    Huajie Jay Cao, Hee Rin Lee, and Wei Peng. 2025. Empowering Adults with AI Literacy: Using Short Videos to Transform Understanding and Harness Fear for Critical Thinking. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems. ACM, Yokohama Japan, 1–8. doi:10.1145/3706598.3713254

    work page doi:10.1145/3706598.3713254 2025

  18. [18]

    Koch, Samantha Straka, Marc Erich Latoschik, and Carolin Wienrich

    Astrid Carolus, Martin J. Koch, Samantha Straka, Marc Erich Latoschik, and Carolin Wienrich. 2023. MAILS - Meta AI Literacy Scale: Development and Testing of an AI Literacy Questionnaire Based on Well-Founded Competency Models and Psychological Change- and Meta-Competencies.Computers in Human Behavior: Artificial Humans1, 2 (Aug. 2023), 100014. doi:10.101...

    work page doi:10.1016/j.chbah 2023

  19. [19]

    Dilek Cetindamar, Kirsty Kitto, Mengjia Wu, Yi Zhang, Babak Abedin, and Simon Knight. 2024. Explicating AI Literacy of Employees at Digital Workplaces.IEEE Transactions on Engineering Management71 (2024), 810–823. doi:10.1109/TEM. 2021.3138503

    work page doi:10.1109/tem 2024

  20. [20]

    Inha Cha and Richmond Y. Wong. 2025. Understanding Socio-technical Factors Configuring AI Non-Use in UX Work Practices. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing Machinery, New York, NY, USA, 1–17. doi:10.1145/3706598.3713140

    work page doi:10.1145/3706598.3713140 2025

  21. [21]

    It’s Freedom to Put Things Where My Mind Wants

    George Chalhoub and Advait Sarkar. 2022. “It’s Freedom to Put Things Where My Mind Wants”: Understanding and Improving the User Experience of Struc- turing Data in Spreadsheets. InCHI Conference on Human Factors in Computing Systems. ACM, New Orleans LA USA, 1–24. doi:10.1145/3491102.3501833

    work page doi:10.1145/3491102.3501833 2022

  22. [22]

    Chiu, Zubair Ahmad, Murod Ismailov, and Ismaila Temitayo Sanusi

    Thomas K.F. Chiu, Zubair Ahmad, Murod Ismailov, and Ismaila Temitayo Sanusi

    work page

  23. [23]

    doi:10.1016/j.caeo.2024.100171

    What Are Artificial Intelligence Literacy and Competency? A Compre- hensive Framework to Support Them.Computers and Education Open6 (June 2024), 100171. doi:10.1016/j.caeo.2024.100171

    work page doi:10.1016/j.caeo.2024.100171 2024

  24. [24]

    2023.The Economic Potential of Generative AI: The next Productivity Frontier

    Michael Chui, Eric Hazan, Roger Roberts, Alex Singla, Alexander Sukharevsky, Lareina Yee, and Rodney Zemmel. 2023.The Economic Potential of Generative AI: The next Productivity Frontier. Technical Report. McKinsey & Company

    work page 2023

  25. [25]

    Andy Cockburn, Carl Gutwin, Joey Scarr, and Sylvain Malacria. 2014. Supporting Novice to Expert Transitions in User Interfaces.Comput. Surveys47, 2 (Nov. 2014), 31:1–31:36. doi:10.1145/2659796

    work page doi:10.1145/2659796 2014

  26. [26]

    Kaija Marjukka Collin, Sara Keronen, Soila Lemmetty, Tommi Auvinen, and Elina Riivari. 2021. Self-Organised Structures in the Field of ICT: Challenges for Employees’ Workplace Learning.Journal of Workplace Learning33, 2 (March 2021), 95–108. doi:10.1108/JWL-10-2019-0124

    work page doi:10.1108/jwl-10-2019-0124 2021

  27. [27]

    Marios Constantinides and Daniele Quercia. 2025. AI, Jobs, and the Automation Trap: Where Is HCI?. InProceedings of the 4th Annual Symposium on Human- Computer Interaction for Work (CHIWORK ’25). Association for Computing Machinery, New York, NY, USA, 1–8. doi:10.1145/3729176.3729191 CHIWORK ’26, June 22–25, 2026, Linz, Austria Xia et al

    work page doi:10.1145/3729176.3729191 2025

  28. [28]

    Marios Constantinides, Himanshu Verma, Shadan Sadeghian, and Abdallah El Ali. 2025. The Future of Work Is Blended, Not Hybrid. InProceedings of the 4th Annual Symposium on Human-Computer Interaction for Work. ACM, Amsterdam Netherlands, 1–13. doi:10.1145/3729176.3729202

    work page doi:10.1145/3729176.3729202 2025

  29. [29]

    Andrew Cox. 2024. Algorithmic Literacy, AI Literacy and Responsible Gen- erative AI Literacy.Journal of Web Librarianship18, 3 (July 2024), 93–110. doi:10.1080/19322909.2024.2395341

    work page doi:10.1080/19322909.2024.2395341 2024

  30. [30]

    Peter Dalsgaard. 2025. Thinking through Prompting: Cognitive Mediation in Human–AI Interaction. InProceedings of the 36th Annual Conference of the Euro- pean Association of Cognitive Ergonomics (ECCE ’25). Association for Computing Machinery, New York, NY, USA, 1–6. doi:10.1145/3746175.3747192

    work page doi:10.1145/3746175.3747192 2025

  31. [31]

    Hai Dang, Lukas Mecke, Florian Lehmann, Sven Goller, and Daniel Buschek

    work page

  32. [32]

    arXiv:2209.01390

    How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models. arXiv:2209.01390

    work page arXiv

  33. [33]

    Davenport, Sirkka L

    Thomas H. Davenport, Sirkka L. Jarvenpaa, and Michael C. Beers. 1996. Im- proving Knowledge Work Processes.Sloane Management Review37, 4 (1996), 53–65

    work page 1996

  34. [34]

    José Osvaldo De Sordi, Marcia Carvalho De Azevedo, Eliane Maria Pires Giavina Bianchi, and Thiago Carandina. 2021. Defining the Term Knowledge Worker: Toward Improved Ontology and Operationalization.Knowledge and Process Management28, 1 (Jan. 2021), 56–70. doi:10.1002/kpm.1647

    work page doi:10.1002/kpm.1647 2021

  35. [35]

    Data quality in online human-subjects research: Comparisons between MTurk, prolific, CloudResearch, qualtrics, and SONA,

    Benjamin D. Douglas, Patrick J. Ewell, and Markus Brauer. 2023. Data Quality in Online Human-Subjects Research: Comparisons between MTurk, Prolific, CloudResearch, Qualtrics, and SONA.PLOS ONE18, 3 (March 2023), e0279720. doi:10.1371/journal.pone.0279720

    work page doi:10.1371/journal.pone.0279720 2023

  36. [36]

    Peter F. Drucker. 1993. The Rise of the Knowledge Society.The Wilson Quarterly 17, 2 (March 1993), 52–72

    work page 1993

  37. [37]

    Amy Edmondson. 1999. Psychological Safety and Learning Behavior in Work Teams.Administrative Science Quarterly44, 2 (June 1999), 350–383. doi:10.2307/ 2666999

    work page 1999

  38. [38]

    Tyna Eloundou, Sam Manning, Pamela Mishkin, and Daniel Rock. 2023. GPTs Are GPTs: An Early Look at the Labor Market Impact Potential of Large Lan- guage Models. doi:10.48550/arXiv.2303.10130 arXiv:2303.10130 [econ]

    work page doi:10.48550/arxiv.2303.10130 2023

  39. [39]

    Computational dynamic monism: Process metaphysics for the state space theory of consciousness.SSRN Electronic Journal (PREPRINT), dec 2025

    Edward W. Felten, Manav Raj, and Robert Seamans. 2023. How Will Language Modelers like ChatGPT Affect Occupations and Industries? doi:10.2139/ssrn. 4375268

    work page doi:10.2139/ssrn 2023

  40. [40]

    Dana Feng, Bhada Yun, and April Yi Wang. 2026. From Junior to Senior: Al- locating Agency and Navigating Professional Growth in Agentic AI-Mediated Software Engineering. InProceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI ’26). Association for Computing Machinery, New York, NY, USA, 1–24. doi:10.1145/3772318.3791642

    work page doi:10.1145/3772318.3791642 2026

  41. [41]

    Li Feng, Ryan Yen, Yuzhe You, Mingming Fan, Jian Zhao, and Zhicong Lu. 2024. CoPrompt: Supporting Prompt Sharing and Referring in Collaborative Natural Language Programming. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI ’24). Association for Computing Machinery, New York, NY, USA, 1–21. doi:10.1145/3613904.3642212

    work page doi:10.1145/3613904.3642212 2024

  42. [42]

    Paulo Freire and Donaldo P. Macedo. 2014.Pedagogy of the Oppressed: 30th Anniversary Edition(30th anniversary edition ed.). Bloomsbury Publishing, New York

    work page 2014

  43. [43]

    Mingmeng Geng and Roberto Trotta. 2025. Human-LLM Coevolution: Evidence from Academic Writing. doi:10.48550/arXiv.2502.09606 arXiv:2502.09606 [cs]

    work page doi:10.48550/arxiv.2502.09606 2025

  44. [44]

    Emmanouil Giannisakis, Jessalyn Alvina, Andrea Bunt, Parmit Chilana, and Joanna McGrenere. 2022. Promoting Feature Awareness by Leveraging Collabo- rators’ Usage Habits in Collaborative Editors. InGraphics Interface

    work page 2022

  45. [45]

    Bengt Goransson, Mats Lind, Else Pettersson, Bengt Sandblad, and Patrik Schwalbe. 1986. The Interface Is Often Not the Problem. InProceedings of the SIGCHI/GI Conference on Human Factors in Computing Systems and Graphics Interface. ACM, Toronto Ontario Canada, 133–136. doi:10.1145/29933.30872

    work page doi:10.1145/29933.30872 1986

  46. [46]

    Stuart Hall. 1997. Representation: Cultural Representations and Signifying Practices. InThe Work of Representation, Stuart Hall (Ed.). Sage, London, 13–74

    work page 1997

  47. [47]

    Jan Hatzius, Joseph Briggs, Davesh Kodnani, and Giovanni Pierdomenico. 2023. The Potentially Large Effects of Artificial Intelligence on Economic Growth. Tech- nical Report. Goldman Sachs

    work page 2023

  48. [48]

    Teresa Heyder and Oliver Posegga. 2021. Extending the Foundations of AI Literacy.ICIS 2021 Proceedings(Dec. 2021)

    work page 2021

  49. [49]

    Amir Jahanlou, Jo Vermeulen, Tovi Grossman, Parmit K Chilana, George Fitz- maurice, and Justin Matejka. 2023. Task-Centric Application Switching: How and Why Knowledge Workers Switch Software Applications for a Single Task. Graphic Interface 2023(2023)

    work page 2023

  50. [50]

    Huiying Jin and Masato Uchida. 2024. A Three-Year Analysis of Human Prefer- ences in Delegating Tasks to AI. InArtificial Intelligence in HCI, Helmut Degen and Stavroula Ntoa (Eds.). Vol. 14734. Springer Nature Switzerland, Cham, 48–66. doi:10.1007/978-3-031-60606-9_4

    work page doi:10.1007/978-3-031-60606-9_4 2024

  51. [51]

    Magnus Høholt Kaspersen, Line Have Musaeus, Karl-Emil Kjær Bilstrup, Mar- ianne Graves Petersen, Ole Sejer Iversen, Christian Dindler, and Peter Dals- gaard. 2024. From Primary Education to Premium Workforce: Drawing on K-12 Approaches for Developing AI Literacy. InProceedings of the CHI Confer- ence on Human Factors in Computing Systems. ACM, Honolulu HI...

    work page doi:10.1145/3613904.3642607 2024

  52. [52]

    Pranav Khadpe, Olivia Xu, Geoff Kaufman, and Chinmay Kulkarni. 2025. Hug Reports: Supporting Expression of Appreciation between Users and Contributors of Open Source Software Packages.Proc. ACM Hum.-Comput. Interact.9, 2 (May 2025), CSCW099:1–CSCW099:32. doi:10.1145/3710997

    work page doi:10.1145/3710997 2025

  53. [53]

    I Would Just Ask Someone

    Kimia Kiani, Parmit K. Chilana, Andrea Bunt, Tovi Grossman, and George Fitzmaurice. 2020. “I Would Just Ask Someone”: Learning Feature-Rich Design Software in the Modern Workplace. In2020 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). IEEE, Dunedin, New Zealand, 1–10. doi:10.1109/VL/HCC50065.2020.9127288

    work page doi:10.1109/vl/hcc50065.2020.9127288 2020

  54. [54]

    Nigel King. 1998. Template Analysis. InQualitative Methods and Analysis in Organizational Research: A Practical Guide. Sage Publications Ltd, Thousand Oaks, CA, 118–134

    work page 1998

  55. [55]

    We Need to Avail Ourselves of [GenAI] to Enhance Knowledge Distribution

    Eunhye Grace Ko, Shaini Nanayakkara, and Earl W Huff. 2025. "We Need to Avail Ourselves of [GenAI] to Enhance Knowledge Distribution": Empowering Older Adults through GenAI Literacy. InProceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. ACM, Yokohama Japan, 1–7. doi:10.1145/3706599.3720032

    work page doi:10.1145/3706599.3720032 2025

  56. [56]

    If the Machine Is As Good As Me, Then What Use Am I?

    Charlotte Kobiella, Yarhy Said Flores López, Franz Waltenberger, Fiona Draxler, and Albrecht Schmidt. 2024. "If the Machine Is As Good As Me, Then What Use Am I?" – How the Use of ChatGPT Changes Young Professionals’ Perception of Productivity and Accomplishment. InProceedings of the CHI Conference on Human Factors in Computing Systems. ACM, Honolulu HI U...

    work page arXiv 2024

  57. [57]

    Pawel Korzynski, Grzegorz Mazurek, Pamela Krzypkowska, and Artur Kurasin- ski. 2023. Artificial Intelligence Prompt Engineering as a New Digital Compe- tence: Analysis of Generative AI Technologies Such as ChatGPT.Entrepreneurial Business and Economics Review11, 3 (2023), 25–37. doi:10.15678/EBER.2023. 110302

    work page doi:10.15678/eber.2023 2023

  58. [59]

    Hao-Ping (Hank) Lee, Advait Sarkar, Lev Tankelevitch, Ian Drosos, Sean Rintel, Richard Banks, and Nicholas Wilson. 2025. The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems. AC...

    work page doi:10.1145/3706598.3713778 2025

  59. [60]

    Seyoung Lee and Gain Park. 2024. Development and Validation of ChatGPT Literacy Scale.Current Psychology43, 21 (June 2024), 18992–19004. doi:10.1007/ s12144-024-05723-0

    work page 2024

  60. [61]

    Soila Lemmetty and Kaija Collin. 2020. Self-Directed Learning as a Practice of Workplace Learning: Interpretative Repertoires of Self-Directed Learning in ICT Work.Vocations and Learning13, 1 (April 2020), 47–70. doi:10.1007/s12186- 019-09228-x

    work page doi:10.1007/s12186- 2020

  61. [62]

    Soila Lemmetty and Kaija Collin. 2020. Throwaway Knowledge, Useful Skills or a Source for Wellbeing? Outlining Sustainability of Workplace Learning Situations.International Journal of Lifelong Education39, 5-6 (Nov. 2020), 478–

    work page 2020

  62. [63]

    doi:10.1080/02601370.2020.1804004

    work page doi:10.1080/02601370.2020.1804004 2020

  63. [65]

    Yier Ling, Alex Kale, and Alex Imas. 2026. Underreporting of AI Use: The Role of Social Desirability Bias. InProceedings of the 2026 CHI Conference on Human Factors in Computing Systems. ACM, Barcelona Spain, 1–22. doi:10.1145/3772318. 3791073

    work page doi:10.1145/3772318 2026

  64. [66]

    Haidan Liu, Poorvi Bhatia, Nicholas Vincent, and Parmit Chilana. 2026. Tracing Everyday AI Literacy Discussions at Scale: How Online Creative Communities Make Sense of Generative AI. doi:10.48550/ARXIV.2603.09055

    work page doi:10.48550/arxiv.2603.09055 2026

  65. [67]

    Jiqun Liu, Jamshed Karimnazarov, and Ryen W. White. 2025. Trapped by Expec- tations: Functional Fixedness in LLM-Enabled Chat Search. doi:10.48550/arXiv. 2504.02074 arXiv:2504.02074 [cs]

    work page internal anchor Pith review doi:10.48550/arxiv 2025

  66. [68]

    Xin Liu, Longxin Zhang, and Xiaochong Wei. 2025. Generative Artificial Intelli- gence Literacy: Scale Development and Its Effect on Job Performance.Behavioral Sciences15, 6 (June 2025), 811. doi:10.3390/bs15060811

    work page doi:10.3390/bs15060811 2025

  67. [69]

    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. doi:10.1145/ 3313831.3376727

    work page arXiv 2020

  68. [70]

    Eva Mackamul, Géry Casiez, and Sylvain Malacria. 2023. Clarifying and Differ- entiating Discoverability.Human–Computer Interaction0, 0 (Oct. 2023), 1–26. doi:10.1080/07370024.2024.2364606 The Social Dynamics of Developing Generative AI Literacy in the Workplace CHIWORK ’26, June 22–25, 2026, Linz, Austria

    work page doi:10.1080/07370024.2024.2364606 2023

  69. [71]

    Lina Markauskaite, Rebecca Marrone, Oleksandra Poquet, Simon Knight, Roberto Martinez-Maldonado, Sarah Howard, Jo Tondeur, Maarten De Laat, Si- mon Buckingham Shum, Dragan Gašević, and George Siemens. 2022. Rethinking the Entwinement between Artificial Intelligence and Human Learning: What Capabilities Do Learners Need for a World with AI?Computers and Ed...

    work page doi:10.1016/j.caeai.2022.100056 2022

  70. [72]

    2025.Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential

    Hannah Mayer, Lareina Yee, Michael Chui, and Roger Roberts. 2025.Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential. Technical Report. McKinsey & Company. 1–47 pages

    work page 2025

  71. [73]

    Joanna Mcgrenere and Gale Moore. 2000. Are We All In the Same "Bloat"?. In Proceedings of the Graphics Interface 2000 Conference. 187–196

    work page 2000

  72. [74]

    van Stralen, and Robert West

    Susan Michie, Maartje M. van Stralen, and Robert West. 2011. The Behaviour Change Wheel: A New Method for Characterising and Designing Behaviour Change Interventions.Implementation Science6, 1 (April 2011), 42. doi:10.1186/ 1748-5908-6-42

    work page 2011

  73. [75]

    2024.2024 Work Trend Index Annual Report

    Microsoft. 2024.2024 Work Trend Index Annual Report. Technical Report. Microsoft. 1–38 pages

    work page arXiv 2024

  74. [76]

    Elizabeth Wolfe Morrison and Frances J Milliken. 2000. Organizational Silence: A Barrier to Change and Development in a Pluralistic World.The Academy of Management Review25, 4 (Oct. 2000)

    work page 2000

  75. [77]

    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 (Jan. 2021), 100041. doi:10.1016/j.caeai. 2021.100041

    work page doi:10.1016/j.caeai 2021

  76. [78]

    Trinh Nguyen and Amany Elbanna. 2025. Understanding Human-AI Augmen- tation in the Workplace: A Review and a Future Research Agenda.Information Systems Frontiers(March 2025). doi:10.1007/s10796-025-10591-5

    work page doi:10.1007/s10796-025-10591-5 2025

  77. [79]

    Workslop

    Kate Niederhoffer, Gabriella Rosen Kellerman, Angela Lee, Alex Liebscher, Kristina Rapuano, and Jeffrey T. Hancock. 2025. AI-Generated “Workslop” Is Destroying Productivity.Harvard Business Review(Sept. 2025)

    work page 2025

  78. [80]

    Midas Nouwens and Clemens Nylandsted Klokmose. 2018. The Application and Its Consequences for Non-Standard Knowledge Work. InProceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, Montreal QC Canada, 1–12. doi:10.1145/3173574.3173973

    work page doi:10.1145/3173574.3173973 2018

  79. [81]

    Orlikowski

    Wanda J. Orlikowski. 1992. The Duality of Technology: Rethinking the Concept of Technology in Organizations.Organization Science3, 3 (1992), 398–427. jstor:2635280

    work page 1992

  80. [82]

    Orlikowski

    Wanda J. Orlikowski. 1993. Learning from Notes: Organizational Issues in Groupware Implementation.The Information Society9, 3 (July 1993), 237–250. doi:10.1080/01972243.1993.9960143

    work page doi:10.1080/01972243.1993.9960143 1993

Showing first 80 references.