Pith. sign in

REVIEW 3 major objections 5 minor 109 references

Generative AI Literacy: Twelve Defining Competencies

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read This paper proposes that generative AI literacy consists of twelve defined competencies, from basic AI knowledge to prompt engineering and legal awareness, and argues this set fills a gap left by generic AI literacy frameworks.

desk verdict A transparent, useful competency framework that overstates its 'defining' claim but clearly deserves constructive peer review. read the letter →

arxiv 2412.12107 v1 pith:R2DJOXM4 submitted 2024-11-29 cs.HC cs.AI

Ravinithesh Annapureddy , Alessandro Fornaroli , Daniel Gatica-Perez This is my paper · ORCID
classification cs.HCcs.AI
keywords generativeAIliteracycompetencymodelpromptengineeringethicslegalaspectscontinuouslearningcompetencies
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper proposes a competency-based model of generative AI literacy built from twelve competencies, ranging from basic AI awareness through tool use, prompt engineering, and programming to contextual, ethical, legal, and continuous-learning skills. The authors argue that such a specific framework is needed because existing AI literacy frameworks are too generic and do not distinguish generative from predictive AI, even though generative tools raise distinct questions of authorship, hallucination, and authenticity. The model is put forward as a roadmap for individuals, educators, policymakers, and government bodies, and as a foundation for future assessments, curricula, and regulations. A sympathetic reader would take the paper's core contribution to be a shared vocabulary for naming and organizing what it means to be literate with generative AI.

What carries the argument

The central object is the twelve-item competency model itself, an ordered list of knowledge, skills, abilities, and behaviors arranged as a learning path. The ordering is purposeful: foundational awareness comes first, then tool use and output assessment, then prompting and programming, then context, ethics, law, and finally continuous learning, which cuts across all the others. The model works as a checklist and roadmap: it lets individuals, educators, and institutions locate themselves on the spectrum from consumer to developer.

What would settle it

A systematic search that locates a published generative AI literacy framework existing before or alongside this one, or an empirical study showing that a person can be fully competent with generative AI while lacking one of the twelve competencies, would overturn the claim that the set is defining.

Watch

Extended reading notes

Core claim

The paper's central claim is that generative AI literacy is a distinct concept from general AI literacy and is fully delineated by the twelve competencies listed here. Existing frameworks, it argues, treat literacy as generic and fail to separate generative from predictive AI, whereas these competencies cover what users need to understand, assess, interact with, and create generative AI. The list is presented as definitive: these twelve, in this order, are the defining competencies.

Load-bearing premise

The load-bearing premise is that a small database search and a non-systematic review of the literature are enough to prove that no prior generative AI literacy framework exists and that these twelve competencies are the complete, non-redundant set.

Editorial extensions

If this is right

  • Educators can use the twelve competencies as the backbone of curricula that move learners from passive consumers to interpreters and creators of generative AI.
  • Public institutions can benchmark employee skills against the model and use it to design training for generative AI adoption.
  • Assessment designers can derive tasks and metrics from the competencies, supporting certification, proficiency levels, and educational placement.
  • Policymakers can ground literacy guidelines and regulation in the competencies, including awareness of legal frameworks such as the AI Act.

Reading between the lines

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

  • The paper does not itself build the assessment instruments it calls for; a direct next step would be to turn each competency into testable tasks and rubrics.
  • The twelve-competency set could be treated as a layered extension of existing digital and data literacy frameworks, rather than a standalone construct, since many competencies (continuous learning, context, ethics) are not unique to generative AI.
  • The claimed logical progression is testable: one could compare learning outcomes for groups trained in the proposed order versus a different order to see whether the sequence matters.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. This paper proposes a competency-based model of generative AI literacy, identifying twelve competencies ranging from foundational AI literacy through prompt engineering and programming to ethical, legal, and continuous-learning abilities. The authors argue that these competencies delineate a general definition of generative AI literacy and can serve as a framework for assessment, curriculum development, and policy. The method combines a non-systematic literature review with a narrow database search (six records, Section 3.1), and the potential implications are explicitly labeled as speculative in Sections 2 and 4.

Significance. If the twelve competencies were validated as complete and non-redundant, the framework could provide a useful common vocabulary for educators, policymakers, and researchers, complementing existing AI literacy frameworks such as Long and Magerko (2020). The paper's strengths are its transparency about the non-systematic method and speculative implications, and its clear, exemplified descriptions of each competency in Section 5. However, the evidence presented does not support the 'defining' status of the set, so the contribution is best read as a proposal or starting point rather than an established definition.

major comments (3)
  1. [Sections 2, 3.1, Table 3] The paper's central claim, stated in the title and abstract, is that the twelve competencies are 'defining' for generative AI literacy. That claim requires both completeness and non-redundancy of the set. The supporting evidence is an explicitly non-systematic literature review (Section 2) and a database search restricted to two exact phrases across five databases, yielding six records (Section 3.1). No selection criteria, coding scheme, inter-rater agreement, or external validation is reported for the list in Table 3, and Section 6.1 concedes that the framework reflects the current state of the field and may require revision. As written, the evidence cannot distinguish the twelve competencies from the authors' judgment, so the 'defining' claim is not supported. I recommend either softening the claim to 'proposed' or 'initial' competencies, or substantially strengthening the method with a systematic review and a validation step.
  2. [Section 1, Table 1] The Introduction states that 'no set of competencies specific to the use of generative AI had been previously proposed.' This novelty claim is contradicted by the paper's own Table 1, which lists Dadhich and Bhaumik (2023) proposing a 'Comprehensive Model' of generative AI literacy and Noh and Han (2023) implementing a 'generative AI literacy education program.' The authors dismiss these works as conflating generative AI literacy with general AI literacy, but this dismissal is asserted rather than demonstrated. Because the novelty of the contribution rests on this gap, the authors should either provide a more systematic analysis of the six records, including any competency lists they contain, or qualify the gap claim.
  3. [Table 3, Sections 4 and 5.12] The twelve competencies are not shown to be non-redundant, which is required if they are 'defining.' Competency 12 ('Ability to continuously learn') is explicitly described as 'transversal to all the other capabilities' in Section 4 and as applying 'to each of the other ones' in Section 5.12. Similarly, Competency 1 ('Basic AI literacy') and Competency 2 ('Knowledge of generative AI models') overlap in that the latter presupposes the former. If the model is intended as a set of independent competencies, the authors should justify the boundaries; alternatively, the model should be presented as a structured learning progression with a transversal meta-competency, which would be a different and more defensible claim.
minor comments (5)
  1. [Section 2] The phrase 'a long-standing review approach' is unclear; presumably 'a systematic review approach' was intended, or the sentence should be reworded for clarity.
  2. [Section 4] The sentence beginning 'Alongside, this paper defines...' uses an awkward transition; consider 'In addition, this paper defines...' or 'The paper also defines...'.
  3. [Section 5.9] The phrase 'to which extent' should be 'to what extent.'
  4. [Section 3.1 and Table 1] The database search was conducted in December 2023, but the manuscript was received in January 2024 and contains a March 2024 regulatory reference (the European AI Act approval) elsewhere; the authors should acknowledge that the 'state of the art' on generative AI literacy may have advanced since the search date.
  5. [Table 3] The positive and negative implications in Table 3 are described as mostly speculative in Section 4, but the table itself does not display this caveat; consider adding a footnote to the table so the caveat is visible without reading the main text.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the twelve-competency model is a literature-based synthesis, not a derivation that reduces to its own inputs.

full rationale

This is a conceptual proposal paper, not a formal derivation, so the circularity patterns that apply to fitted models or imported uniqueness theorems do not arise. The paper's central chain is: a December 2023 database search for "generative AI literacy" and "generative artificial intelligence literacy" returns six records; a non-systematic review of AI literacy, prompt engineering, generative AI applications, and competency models is used to identify twelve competencies; those competencies are then proposed as a definitional model. Each step is transparently declared. The authors do not fit a parameter to a subset of data and then claim to predict a closely related quantity; there is no equation or statistical reduction. There are no self-citations at all, so no self-citation chain is load-bearing, and no uniqueness theorem from the authors' prior work is invoked. The strongest possible circularity concern would be the self-definitional flavor of defining generative AI literacy in terms of competencies and then presenting the competency list as the model, but that is inherent to any competency framework and the paper does not pretend to derive the list from the definition; it derives the list from external literature. The weaknesses identified by the skeptic are evidentiary: Section 2 explicitly describes the review as non-systematic, Table 1 reports only six records, Section 6.1 concedes that the framework reflects the current state of the field and that revisions may be necessary, and the implications are labeled speculative in Sections 2 and 4. Those are limitations on completeness and generalizability, not circular reductions. The paper also positions the twelve competencies as a complement to, not a replacement of, the existing AI literacy framework of Long and Magerko, which further shows the model is built on external benchmarks rather than on its own conclusion. Therefore, under the rule that an honest non-finding is expected unless the specific reduction can be exhibited, the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 1 invented entities

The framework relies on several domain assumptions about the distinctness and universality of generative AI literacy, and it introduces an unvalidated construct (the twelve-competency model). No quantitative free parameters are involved.

assumptions (4)
  • domain assumption Generative AI literacy is a distinct concept requiring its own competency set.
    Introduced in Section 1 and Section 3.4, the paper argues that generative AI's unique features (probabilistic synthesis, deepfakes, authorship questions) necessitate a new literacy framework beyond general AI literacy.
  • domain assumption The NIH definition of competency (knowledge, skills, abilities, behaviors) is adopted as the foundation.
    Section 3.3 explicitly adopts the NIH grouping, which shapes how each of the twelve competencies is categorized.
  • domain assumption A non-systematic literature review is sufficient to identify a comprehensive and non-redundant set of competencies.
    Section 2 states the review is non-systematic and speculative; the completeness of the twelve-competency list rests on this assumption.
  • domain assumption The database search with the specific query captures the current state of generative AI literacy research.
    Section 3.1 reports only six records, which underpins the claim that no prior framework exists. Different search terms could yield more prior work.
invented entities (1)
  • Twelve defining competencies for generative AI literacy
    purpose: To operationalize the concept of generative AI literacy as a universal competency model.
    The competencies are introduced and defined in Section 4 and Section 5 without empirical validation, inter-rater reliability, or external benchmarks. They are author-derived constructs.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Generative AI Literacy: Twelve Defining Competencies." pith.science (2026). https://pith.science/paper/R2DJOXM4

@misc{pith2026241212107,
  author       = {Pith},
  title        = {Pith review of: Generative AI Literacy: Twelve Defining Competencies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/R2DJOXM4}},
  note         = {Machine review of arXiv:2412.12107}
}
read the original abstract

This paper introduces a competency-based model for generative artificial intelligence (AI) literacy covering essential skills and knowledge areas necessary to interact with generative AI. The competencies range from foundational AI literacy to prompt engineering and programming skills, including ethical and legal considerations. These twelve competencies offer a framework for individuals, policymakers, government officials, and educators looking to navigate and take advantage of the potential of generative AI responsibly. Embedding these competencies into educational programs and professional training initiatives can equip individuals to become responsible and informed users and creators of generative AI. The competencies follow a logical progression and serve as a roadmap for individuals seeking to get familiar with generative AI and for researchers and policymakers to develop assessments, educational programs, guidelines, and regulations.

Figures

Figures reproduced from arXiv: 2412.12107 by the authors.

Figure 1
Figure 1. Diagram of the 12 generative-AI competencies [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

109 extracted references · 25 canonical work pages

  1. [1]

    Hussam Alkaissi and Samy I McFarlane. 2023. Artificial Hallucinations in ChatGPT: Implications in Scientific Writing. Cureus 15, 2 (2023), e35179. https://doi.org/10.7759/cureus.35179

    doi:10.7759/cureus.35179 2023

  2. [2]

    Chen, Laurel Orr, Neel Guha, Kush Bhatia, Ines Chami, Frederic Sala, and Christopher Ré

    Simran Arora, Avanika Narayan, Mayee F. Chen, Laurel Orr, Neel Guha, Kush Bhatia, Ines Chami, Frederic Sala, and Christopher Ré. 2022. Ask Me Anything: A simple strategy for prompting language models. arXiv:2210.02441 [cs.CL]

    arXiv 2022

  3. [3]

    Sai Anirudh Athaluri, Sandeep Varma Manthena, V S R Krishna Manoj Kesapragada, Vineel Yarlagadda, Tirth Dave, and Rama Tulasi Siri Duddumpudi. 2023. Exploring the Boundaries of Reality: Investigating the Phenomenon of Artificial Intelligence Hallucination in Scientific Writing Through ChatGPT References. Cureus 15, 4 (2023), e37432. https://doi.org/10.775...

    doi:10.7759/cureus.37432 2023

  4. [4]

    Ömer Aydın and Enis Karaarslan. 2022. OpenAI ChatGPT Generated Literature Review: Digital Twin in Healthcare. SSRN Electronic Journal (2022), 22–31. https://doi.org/10.2139/ssrn.4308687

    doi:10.2139/ssrn.4308687 2022

  5. [5]

    Razvan Azamfirei, Sapna R Kudchadkar, and James Fackler. 2023. Large language models and the perils of their hallucinations. Critical Care 27, 1 (March 2023), 120. https://doi.org/10.1186/s13054-023-04393-x Manuscript submitted to ACM Generative AI Literacy: Twelve Defining Competencies 19

    doi:10.1186/s13054-023-04393-x 2023

  6. [6]

    David Baidoo-Anu and Leticia Owusu Ansah. 2023. Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. Journal of AI 7, 1 (2023), 52–62. https://doi.org/10.61969/jai.1337500

    doi:10.61969/jai.1337500 2023

  7. [7]

    Maria Teresa Baldassarre, Danilo Caivano, Berenice Fernandez Nieto, Domenico Gigante, and Azzurra Ragone. 2023. The Social Impact of Generative AI: An Analysis on ChatGPT. In Proceedings of the 2023 ACM Conference on Information Technology for Social Good (Lisbon, Portugal) (GoodIT ’23). Association for Computing Machinery, New York, NY, USA, 363–373. htt...

    arXiv 2023

  8. [8]

    Baraheem and Tam V

    Samah S. Baraheem and Tam V. Nguyen. 2023. AI vs. AI: Can AI Detect AI-Generated Images? Journal of Imaging 9, 10 (2023). https: //doi.org/10.3390/jimaging9100199

    doi:10.3390/jimaging9100199 2023

Show all 109 references
  1. [9]

    Paul Batalden, David Leach, Susan Swing, Hubert Dreyfus, and Stuart Dreyfus. 2002. General Competencies And Accreditation In Graduate Medical Education. Health Affairs 21, 5 (2002), 103–111. https://doi.org/10.1377/hlthaff.21.5.103 arXiv:https://doi.org/10.1377/hlthaff.21.5.103

    2002 doi

  2. [10]

    Weyerer Bernd W

    Jan C. Weyerer Bernd W. Wirtz and Carolin Geyer. 2019. Artificial Intelligence and the Public Sector—Applications and Chal- lenges. International Journal of Public Administration 42, 7 (2019), 596–615. https://doi.org/10.1080/01900692.2018.1498103 arXiv:https://doi.org/10.1080...

    2019

  3. [11]

    Aras Bozkurt. 2023. Unleashing the potential of generative AI, conversational agents and chatbots in educational praxis: A systematic review and bibliometric analysis of GenAI in education. Open Praxis 15, 4 (Nov 2023). https://doi.org/10.55982/openpraxis.15.4.609

    2023 doi

  4. [12]

    Enock, Saba Esnaashari, John Francis, Youmna Hashem, and Deborah Morgan

    Jonathan Bright, Florence E. Enock, Saba Esnaashari, John Francis, Youmna Hashem, and Deborah Morgan. 2024. Generative AI is already widespread in the public sector. https://doi.org/10.48550/arXiv.2401.01291 arXiv:2401.01291 [cs.CY]

    2024 doi arXiv

  5. [13]

    Damien Bruce, Ankit Fadia, Tom Isherwood, Chiara Marcati, Aldous Mitchell, Björn Münstermann, Gayatri Shenai, Hrishika Vuppala, and Thomas Weber. 2023. Unlocking the potential of generative AI: Three key questions for government agencies. https://www.mckinsey.com/industries/pu...

    2023

  6. [14]

    Bamber, Jose R

    Pawan Budhwar, Soumyadeb Chowdhury, Geoffrey Wood, Herman Aguinis, Greg J. Bamber, Jose R. Beltran, Paul Boselie, Fang Lee Cooke, Stephanie Decker, Angelo DeNisi, Prasanta Kumar Dey, David Guest, Andrew J. Knoblich, Ashish Malik, Jaap Paauwe, Savvas Papagiannidis, Charmi Patel...

    2023

  7. [15]

    Lorena Casal-Otero, Alejandro Catala, Carmen Fernández-Morante, Maria Taboada, Beatriz Cebreiro, and Senén Barro. 2023. AI literacy in K-12: a systematic literature review. International Journal of STEM Education 10, 1 (19 Apr 2023), 29. https://doi.org/10.1186/s40594-023-00418-7

    2023 doi

  8. [16]

    Lucian Cernuşca and Cristina Dima. 2007. Competency and human resource management. Revista da Faculdade de Ciências Humanas e Sociais 4 (2007), 162–171. http://hdl.handle.net/10284/357

    2007

  9. [17]

    Yoon Chae. 2020. US AI regulation guide: legislative overview and practical considerations. The Journal of Robotics, Artificial Intelligence & Law 3 (2020)

    2020

  10. [18]

    Chaka Chaka. 2023. Detecting AI content in responses generated by ChatGPT, YouChat, and Chatsonic: The case of five AI content detection tools. Journal of Applied Learning and Teaching 6, 2 (2023). https://doi.org/10.37074/jalt.2023.6.2.12

    2023 doi

  11. [19]

    Contextual

    Anastasia Chan. 2023. GPT-3 and InstructGPT: technological dystopianism, utopianism, and “Contextual” perspectives in AI ethics and industry. AI and Ethics 3, 1 (Feb. 2023), 53–64

    2023

  12. [20]

    Cecilia Ka Yuk Chan and Wenjie Hu. 2023. Students’ voices on generative AI: perceptions, benefits, and challenges in higher education.International Journal of Educational Technology in Higher Education 20, 1 (July 2023), 43

    2023

  13. [21]

    Banghao Chen, Zhaofeng Zhang, Nicolas Langrené, and Shengxin Zhu. 2023. Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review. arXiv:2310.14735 [cs.CL]

    2023 arXiv

  14. [22]

    Vikram Singh Chouhan and Sandeep Srivastava. 2014. Understanding Competencies and Competency Modeling — A Literature Survey. IOSR Journal of Business and Management 16 (2014), 14–22. https://doi.org/10.9790/487X-16111422

    2014 doi

  15. [23]

    Ben Collins. 2023. Fake picture of explosion at Pentagon Spooks twitter. https://www.nbcnews.com/tech/misinformation/fake-picture-explosion- pentagon-spooks-twitter-rcna85659

    2023

  16. [24]

    Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, and Anil A. Bharath. 2018. Generative Adversarial Networks: An Overview. IEEE Signal Processing Magazine 35, 1 (2018), 53–65. https://doi.org/10.1109/MSP.2017.2765202

    2018

  17. [25]

    Manish Dadhich and Amiya Bhaumik. 2023. Demystification of Generative Artificial Intelligence (AI) Literacy, Algorithmic Thinking, Cognitive Divide, Pedagogical knowledge: A Comprehensive Model. In 2023 IEEE International Conference on ICT in Business Industry & Government (IC...

    2023

  18. [26]

    Cotton Debby R

    Peter A. Cotton Debby R. E. Cotton and J. Reuben Shipway. 2023. Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International 0, 0 (2023), 1–12. https://doi.org/10.1080/14703297.2023.2190148 arXiv:https://doi.org/...

    2023

  19. [27]

    Françoise Delamare Le Deist and Jonathan Winterton. 2005. What Is Competence? Human Resource Development International 8, 1 (2005), 27–46. https://doi.org/10.1080/1367886042000338227 arXiv:https://doi.org/10.1080/1367886042000338227

    2005 doi

  20. [28]

    Patrick Dunleavy and Helen Margetts. 2023. Data science, artificial intelligence and the third wave of digital era governance. Public Policy and Administration (2023). https://doi.org/10.1177/09520767231198737 arXiv:https://doi.org/10.1177/09520767231198737

    2023 doi

  21. [29]

    So what if ChatGPT wrote it?

    Yogesh K. Dwivedi, Nir Kshetri, Laurie Hughes, Emma Louise Slade, Anand Jeyaraj, Arpan Kumar Kar, Abdullah M. Baabdullah, Alex Koohang, Vishnupriya Raghavan, Manju Ahuja, Hanaa Albanna, Mousa Ahmad Albashrawi, Adil S. Al-Busaidi, Janarthanan Balakrishnan, Yves Barlette, Manusc...

    2023

  22. [30]

    Dina El-Zanfaly, Yiwei Huang, and Yanwen Dong. 2023. Sand-in-the-loop: Investigating embodied co-creation for shared understandings of generative AI. In Companion Publication of the 2023 ACM Designing Interactive Systems Conference (Pittsburgh, PA, USA) (DIS ’23 Companion). As...

    2023

  23. [31]

    Tyna Eloundou, Sam Manning, Pamela Mishkin, and Daniel Rock. 2023. GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. arXiv:2303.10130 [econ.GN]

    2023 arXiv

  24. [32]

    European Parliament and Council of the European Union. 2021. Regulation of the European Parliament and of the Council Laying down harmonised rules on Artificial Intelligence (Artificial Intelligence Act) and amending certain union Legislative acts. https://eur-lex.europa.eu/le...

    2021

  25. [33]

    Gao, Frederick M

    Catherine A. Gao, Frederick M. Howard, Nikolay S. Markov, Emma C. Dyer, Siddhi Ramesh, Yuan Luo, and Alexander T. Pear- son. 2022. Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence out- put detector, plagiarism detector,...

    2022 doi

  26. [34]

    Louie Giray. 2023. Prompt Engineering with ChatGPT: A Guide for Academic Writers. Annals of Biomedical Engineering 51, 12 (2023), 2629–2633. https://doi.org/10.1007/s10439-023-03272-4

    2023 doi

  27. [35]

    Matthew Groh, Aruna Sankaranarayanan, Nikhil Singh, Dong Young Kim, Andrew Lippman, and Rosalind Picard. 2024. Human Detection of Political Speech Deepfakes across Transcripts, Audio, and Video. arXiv:2202.12883 [cs.HC]

    2024 arXiv

  28. [36]

    Jindong Gu, Zhen Han, Shuo Chen, Ahmad Beirami, Bailan He, Gengyuan Zhang, Ruotong Liao, Yao Qin, Volker Tresp, and Philip Torr. 2023. A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models. arXiv:2307.12980 [cs.CV]

    2023 arXiv

  29. [37]

    Philipp Hacker, Andreas Engel, and Marco Mauer. 2023. Regulating ChatGPT and other Large Generative AI Models. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (Chicago, IL, USA) (FAccT ’23). Association for Computing Machinery, New York,...

    2023

  30. [38]

    Mohammed Haddad. 2023. Fake Pentagon explosion photo goes viral: How to spot an AI image. https://www.aljazeera.com/news/2023/5/23/fake- pentagon-explosion-photo-goes-viral-how-to-spot-an-ai-image

    2023

  31. [39]

    Xavier Harding. 2023. Did AI Generate This Photo? Here’s How To Tell. https://foundation.mozilla.org/en/blog/ai-photo-how-to-tell/

    2023

  32. [40]

    Harris and Chris Jaikaran

    Laurie A. Harris and Chris Jaikaran. 2023. Highlights of the 2023 Executive Order on Artificial Intelligence for Congress. Congressional Research Service R47843 (7 11 2023). https://crsreports.congress.gov/product/details?prodcode=R47843

    2023

  33. [41]

    Natali Helberger and Nicholas Diakopoulos. 2023. ChatGPT and the AI Act. Internet Policy Review 12, 1 (2023). https://doi.org/10.14763/2023.1.1682

    2023 doi

  34. [42]

    Natali Helberger and Nicholas Diakopoulos. 2023. The European AI Act and How It Matters for Research into AI in Media and Journalism. Digital Journalism 11, 9 (2023), 1751–1760. https://doi.org/10.1080/21670811.2022.2082505 arXiv:https://doi.org/10.1080/21670811.2022.2082505

    2023 arXiv

  35. [43]

    Paul Henman. 2020. Improving public services using artificial intelligence: possibilities, pitfalls, governance. Asia Pacific Journal of Public Administration 42, 4 (2020), 209–221. https://doi.org/10.1080/23276665.2020.1816188 arXiv:https://doi.org/10.1080/23276665.2020.1816188

    2020

  36. [44]

    Terrence Hoffmann. 1999. The meanings of competency. J. Eur. Ind. Train. 23, 6 (Aug. 1999), 275–286. https://doi.org/10.1108/03090599910284650

    1999 doi

  37. [45]

    Shehzeen Hussain, Paarth Neekhara, Malhar Jere, Farinaz Koushanfar, and Julian McAuley. 2021. Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples. In 2021 IEEE Winter Conference on Applications of Computer Vision (W ACV). 3347–3356. ht...

    2021

  38. [46]

    Yohan Hwang, Jang Ho Lee, and Dongkwang Shin. 2023. What is prompt literacy? An exploratory study of language learners’ development of new literacy skill using generative AI. arXiv:2311.05373 [cs.HC] https://arxiv.org/abs/2311.05373

    2023 arXiv

  39. [47]

    Enkelejda Kasneci, Kathrin Sessler, Stefan Küchemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan Günnemann, Eyke Hüllermeier, Stephan Krusche, Gitta Kutyniok, Tilman Michaeli, Claudia Nerdel, Jürgen Pfeffer, Oleksandra Poquet, Michael Sail...

    2023

  40. [48]

    Kingma and Max Welling

    Diederik P. Kingma and Max Welling. 2019. An Introduction to Variational Autoencoders. Foundations and Trends® in Machine Learning 12, 4 (2019), 307–392. https://doi.org/10.1561/2200000056

    2019 doi

  41. [49]

    Lucas Kohnke, Benjamin Luke Moorhouse, and Di Zou. 2023. ChatGPT for Language Teaching and Learning. RELC Journal 54, 2 (2023), 537–550. https://doi.org/10.1177/00336882231162868 arXiv:https://doi.org/10.1177/00336882231162868

    2023 doi

  42. [50]

    Takeshi Kojima, Shixiang (Shane) Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022. Large Language Models are Zero-Shot Reasoners. In Advances in Neural Information Processing Systems , S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. M...

    2022

  43. [51]

    Moritz Kreinsen and Sandra Schulz. 2023. Towards the Triad of Digital Literacy, Data Literacy and AI Literacy in Teacher Education – A Discussion in Light of the Accessibility of Novel Generative AI. https://doi.org/10.35542/osf.io/xguzk

    2023 doi

  44. [52]

    Jianning Li, Amin Dada, Jens Kleesiek, and Jan Egger. 2023. ChatGPT in Healthcare: A Taxonomy and Systematic Review. medRxiv (2023). https://doi.org/10.1101/2023.03.30.23287899 arXiv:https://www.medrxiv.org/content/early/2023/03/30/2023.03.30.23287899.full.pdf

    2023 doi

  45. [53]

    Lingyao Li, Zihui Ma, Lizhou Fan, Sanggyu Lee, Huizi Yu, and Libby Hemphill. 2023. ChatGPT in education: A discourse analysis of worries and concerns on social media. arXiv:2305.02201 [cs.CY]

    2023 arXiv

  46. [54]

    Michael Liebrenz, Roman Schleifer, Anna Buadze, Dinesh Bhugra, and Alexander Smith. 2023. Generating scholarly content with ChatGPT: ethical challenges for medical publishing. The Lancet Digital Health 5, 3 (2023), e105–e106. https://doi.org/10.1016/S2589-7500(23)00019-5

    2023 doi

  47. [55]

    Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023. Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing. ACM Comput. Surv. 55, 9, Article 195 (jan 2023), 35 pages. https://doi.org/10...

    2023 doi

  48. [56]

    Siru Liu, Aileen P Wright, Barron L Patterson, Jonathan P Wanderer, Robert W Turer, Scott D Nelson, Allison B McCoy, Dean F Sittig, and Adam Wright. 2023. Using AI-generated suggestions from ChatGPT to optimize clinical decision support. Journal of the American Medi- cal Infor...

    2023 doi

  49. [57]

    Vivian Liu and Lydia B Chilton. 2022. Design Guidelines for Prompt Engineering Text-to-Image Generative Models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA) (CHI ’22). Association for Computing Machinery, New York, NY, USA, ...

    2022

  50. [58]

    Chung Kwan Lo. 2023. What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature. Education Sciences 13, 4 (2023). https://doi.org/10.3390/educsci13040410

    2023 doi

  51. [59]

    Leo S. Lo. 2023. The Art and Science of Prompt Engineering: A New Literacy in the Information Age. Internet Reference Services Quarterly 27, 4 (2023), 203–210. https://doi.org/10.1080/10875301.2023.2227621 arXiv:https://doi.org/10.1080/10875301.2023.2227621

    2023

  52. [60]

    Leo S. Lo. 2023. The CLEAR path: A framework for enhancing information literacy through prompt engineering. The Journal of Academic Librarianship 49, 4 (2023), 102720. https://doi.org/10.1016/j.acalib.2023.102720

    2023

  53. [61]

    Duri Long and Brian Magerko. 2020. What is AI Literacy? Competencies and Design Considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20) . Association for Computing Machinery, New York, NY, USA, 1–16. https:/...

    2020

  54. [62]

    Jieyi Long. 2023. Large Language Model Guided Tree-of-Thought. arXiv:2305.08291 [cs.AI]

    2023 arXiv

  55. [63]

    Tambiama Madiega. 2021. Artificial intelligence act. European Parliament: European Parliamentary Research Service (2021). https://policycommons.net/artifacts/1894362/artificial-intelligence-act/2644377/#:~:text=https%3A//policycommons.net/artifacts/1894362/ artificial%2Dintell...

    2021

  56. [64]

    Albert Meijer, Lukas Lorenz, and Martijn Wessels. 2021. Algorithmization of Bureaucratic Organizations: Using a Practice Lens to Study How Context Shapes Predictive Policing Systems. Public Administration Review 81, 5 (2021), 837–846. https://doi.org/10.1111/puar.13391 arXiv:h...

    2021 doi

  57. [65]

    Bertalan Meskó and Eric J Topol. 2023. The imperative for regulatory oversight of large language models (or generative AI) in healthcare. npj Digital Medicine 6, 1 (July 2023), 120. https://doi.org/10.1038/s41746-023-00873-0

    2023 doi

  58. [66]

    Fiona Fui-Hoon Nah, Ruilin Zheng, Jingyuan Cai, Keng Siau, and Langtao Chen. 2023. Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of Information Technology Case and Application Research 25, 3 (2023), 277–304. https://doi.org/10.1080/15...

    2023

  59. [67]

    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 Intelligence 2 (2021), 100041. https://doi.org/10.1016/j.caeai.2021.100041

    2021

  60. [68]

    Nightingale and Hany Farid

    Sophie J. Nightingale and Hany Farid. 2022. AI-synthesized faces are indistinguishable from real faces and more trustwor- thy. Proceedings of the National Academy of Sciences 119, 8 (2022), e2120481119. https://doi.org/10.1073/pnas.2120481119 arXiv:https://www.pnas.org/doi/pdf...

    2022 doi

  61. [69]

    YangJin Noh and Songlee Han. 2023. Exploring perception and support strategies for Generative AI Literacy: Focusing on pre-service secondary teachers. Korean Association For Learner-Centered Curriculum And Instruction 23, 17 (2023), 317–333. https://doi.org/10.22251/jlcci.2023...

    2023 doi

  62. [70]

    National Institute of Health. 2020. Competencies at NIH. https://hr.nih.gov/working-nih/competencies

    2020

  63. [71]

    Eugenie Park and Risa GELLES-WATNICK. 2023. Most Americans haven’t used CHATGPT; few think it will have a major impact on their job. https: //www.pewresearch.org/short-reads/2023/08/28/most-americans-havent-used-chatgpt-few-think-it-will-have-a-major-impact-on-their-job/

    2023

  64. [72]

    Carl Preiksaitis and Christian Rose. 2023. Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review. JMIR Med Educ 9 (20 Oct 2023), e48785. https://doi.org/10.2196/48785

    2023 doi

  65. [73]

    Tat Putjorn and Pruet Putjorn. 2023. Augmented Imagination: Exploring Generative AI from the Perspectives of Young Learners. In 2023 15th International Conference on Information Technology and Electrical Engineering (ICITEE). 353–358. https://doi.org/10.1109/ICITEE59582.2023.10317680

    2023

  66. [74]

    Kurtis Pykes. 2023. How to use midjourney: A comprehensive guide to ai-generated artwork creation. https://www.datacamp.com/tutorial/how- to-use-midjourney-a-comprehensive-guide-to-ai-generated-artwork-creation Manuscript submitted to ACM 22 Ravinithesh Annapureddy, Alessandro...

    2023

  67. [75]

    Hua Qi, Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Wei Feng, Yang Liu, and Jianjun Zhao. 2020. DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms. In Proceedings of the 28th ACM International Conference on Multimedia (Seattle, WA, USA) (MM ’20). Asso...

    2020

  68. [76]

    Han Qiao, Vivian Liu, and Lydia Chilton. 2022. Initial Images: Using Image Prompts to Improve Subject Representation in Multimodal AI Generated Art. In Proceedings of the 14th Conference on Creativity and Cognition (Venice, Italy) (C&C ’22). Association for Computing Machinery...

    2022

  69. [77]

    Mizanur Rahman, Harold Jan R Terano, Nafizur Rahman, Aidin Salamzadeh, and Saidur Rahaman. 2023. ChatGPT and Academic Research: A Review and Recommendations Based on Practical Examples. Journal of Education, Management and Development Studies 3, 1 (Mar. 2023), 1–12. https://do...

    2023 doi

  70. [78]

    Partha Pratim Ray. 2023. ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems 3 (2023), 121–154. https://doi.org/10.1016/j.iotcps.2023.04.003

    2023 doi

  71. [79]

    Laria Reynolds and Kyle McDonell. 2021. Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI EA ’21). Association for Computing Machinery, New Y...

    2021

  72. [80]

    Suebsarn Ruksakulpiwat, Ayanesh Kumar, and Anuoluwapo Ajibade. 2023. Using ChatGPT in Medical Research: Current Status and Future Directions. Journal of Multidisciplinary Healthcare 16 (2023), 1513–1520. https://doi.org/10.2147/jmdh.s413470

    2023 doi

  73. [81]

    Malik Sallam. 2023. ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns. Healthcare 11, 6 (2023). https://doi.org/10.3390/healthcare11060887

    2023 doi

  74. [82]

    Elvis Saravia. 2022. Prompt Engineering Guide. https://github.com/dair-ai/Prompt-Engineering-Guide

    2022

  75. [83]

    Eric Sarrion. 2023. Parameters Used in ChatGPT . Apress, Berkeley, CA, 87–106. https://doi.org/10.1007/978-1-4842-9804-6_6

    2023 doi

  76. [84]

    Katharina Schüller, Florian Rampelt, Henning Koch, and Johannes Schleiss. 2023. Better ready than just aware: Data and AI Literacy as an enabler for informed decision making in the data age. (2023). https://doi.org/10.18420/inf2023_49

    2023 doi

  77. [85]

    Richard J Shavelson. 2010. On the measurement of competency. Empirical Research in Vocational Education and Training 2, 1 (July 2010), 41–63. https://doi.org/10.1007/BF03546488

    2010 doi

  78. [86]

    Logan IV, Eric Wallace, and Sameer Singh

    Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020. AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP),...

    2020 doi

  79. [87]

    Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan Boyd-Graber, and Lijuan Wang. 2023. Prompting GPT-3 To Be Reliable. arXiv:2210.09150 [cs.CL]

    2023 arXiv

  80. [88]

    Sarin Sok and Kimkong Heng. 2023. ChatGPT for Education and Research: A Review of Benefits and Risks. SSRN Electronic Journal (2023). https://doi.org/10.2139/ssrn.4378735

    2023 doi

  81. [89]

    Chris Stokel-Walker. 2023. ChatGPT listed as author on research papers: many scientists disapprove. Nature 613, 7945 (2023), 620–621. doi:https: //doi.org/10.1038/d41586-023-00107-z

    2023 doi

  82. [90]

    Chris Stokel-Walker and Richard Van Noorden. 2023. What ChatGPT and generative AI mean for science. Nature 614, 7947 (2023), 214–216

    2023

  83. [91]

    Bilal Succar, Willy Sher, and Anthony Williams. 2013. An integrated approach to BIM competency assessment, acquisition and application. Automation in Construction 35 (2013), 174–189. https://doi.org/10.1016/j.autcon.2013.05.016

    2013 doi

  84. [92]

    Arun James Thirunavukarasu, Darren Shu Jeng Ting, Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting. 2023. Large language models in medicine. Nature Medicine 29, 8 (Aug. 2023), 1930–1940

    2023

  85. [93]

    Raju Vaishya, Anoop Misra, and Abhishek Vaish. 2023. ChatGPT: Is this version good for healthcare and research? Diabetes & Metabolic Syndrome: Clinical Research & Reviews 17, 4 (2023), 102744. https://doi.org/10.1016/j.dsx.2023.102744

    2023

  86. [94]

    Eva A M van Dis, Johan Bollen, Willem Zuidema, Robert van Rooij, and Claudi L Bockting. 2023. ChatGPT: five priorities for research. Nature 614, 7947 (Feb. 2023), 224–226. https://doi.org/10.1038/d41586-023-00288-7

    2023 doi

  87. [95]

    Colin van Noordt and Gianluca Misuraca. 2022. Artificial intelligence for the public sector: results of landscaping the use of AI in government across the European Union. Government Information Quarterly 39, 3 (2022), 101714. https://doi.org/10.1016/j.giq.2022.101714

    2022

  88. [96]

    Jiaqi Wang, Zhengliang Liu, Lin Zhao, Zihao Wu, Chong Ma, Sigang Yu, Haixing Dai, Qiushi Yang, Yiheng Liu, Songyao Zhang, Enze Shi, Yi Pan, Tuo Zhang, Dajiang Zhu, Xiang Li, Xi Jiang, Bao Ge, Yixuan Yuan, Dinggang Shen, Tianming Liu, and Shu Zhang. 2023. Review of large vision...

    2023

  89. [97]

    Debora Weber-Wulff, Alla Anohina-Naumeca, Sonja Bjelobaba, Tomáš Foltýnek, Jean Guerrero-Dib, Olumide Popoola, Petr Šigut, and Lorna Waddington. 2023. Testing of detection tools for AI-generated text. International Journal for Educational Integrity 19, 1 (Dec. 2023), 26

    2023

  90. [98]

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou. 2022. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. In Advances in Neural Information Processing Systems , S. Koyejo, S. Mohamed, A. Agarw...

    2022

  91. [99]

    Myrtille Wendling. 2023. Élections Fédérales 2023 – une affiche du PLR, générée par une ia, crée La Polémique. https://www.24heures.ch/une- affiche-du-plr-generee-par-une-ia-cree-la-polemique-758569233102 Manuscript submitted to ACM Generative AI Literacy: Twelve Defining Comp...

    2023

  92. [100]

    Jules White, Quchen Fu, Sam Hays, Michael Sandborn, Carlos Olea, Henry Gilbert, Ashraf Elnashar, Jesse Spencer-Smith, and Douglas C. Schmidt

  93. [101]

    Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mari...

    2020

  94. [102]

    Charles Woodruffe. 1993. What is meant by a competency? Leadersh. Organ. Dev. J. 14, 1 (Jan. 1993), 29–36. https://doi.org/10.1108/eb053651

    1993 doi

  95. [103]

    Tianyu Wu, Shizhu He, Jingping Liu, Siqi Sun, Kang Liu, Qing-Long Han, and Yang Tang. 2023. A Brief Overview of ChatGPT: The History, Status Quo and Potential Future Development. IEEE/CAA Journal of Automatica Sinica 10, 5 (2023), 1122–1136. https://doi.org/10.1109/JAS.2023.123618

    2023

  96. [104]

    Griffiths, Yuan Cao, and Karthik Narasimhan

    Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. 2023. Tree of Thoughts: Deliberate Problem Solving with Large Language Models. arXiv:2305.10601 [cs.CL]

    2023 arXiv

  97. [105]

    Yumi Yi. 2021. Establishing the concept of AI literacy. Jahr–European Journal of Bioethics 12, 2 (2021), 353–368. https://doi.org/10.21860/j.12.2.8

    2021 doi

  98. [106]

    Xiaoming Zhai and Matthew Nyaaba. 2023. Professional Development Needs of Teacher Educators for Generative AI Literacy and Application in Ghana. Journal of AI, available at SSRN (2023). https://doi.org/10.2139/ssrn.4628126

    2023 doi

  99. [107]

    Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alex Smola. 2023. Multimodal Chain-of-Thought Reasoning in Language Models. arXiv:2302.00923 [cs.CL]

    2023 arXiv

  100. [108]

    Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. 2022. Learning to Prompt for Vision-Language Models. International Journal of Computer Vision 130, 9 (Sept. 2022), 2337–2348. Received 28 January 2024; revised 01 April 2024; accepted 18 May 2024 Manuscript submitted to ACM

    2022

  101. [2023]

    arXiv:2302.11382 [cs.SE]

    A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT. arXiv:2302.11382 [cs.SE]

    arXiv

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.