The DOI matches its Crossref record; 115 of the 115 references it cites were found in a registry or an index; none of the 58 sources its claims cite is checked for support yet.
Generative AI Literacy: Twelve Defining Competencies
Open accessAbstract
This article 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 12 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 become familiar with generative AI and for researchers and policymakers to develop assessments, educational programs, guidelines, and regulations.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper proposes a competency-based model for generative AI literacy consisting of twelve competencies, from basic AI literacy to prompt engineering, programming, ethical, and legal knowledge. It argues these competencies can guide education, policy, and professional training for responsible generative AI use.
Key findings
- The model identifies twelve competencies: basic AI literacy; knowledge of generative AI models; knowledge of capacity and limitations; skill to use generative AI tools; ability to detect AI-generated content; ability to assess output; prompt engineering; ability to program and fine-tune; knowledge of contexts; knowledge of ethical implications; knowledge of legal aspects; and ability to continuously learn.
- A preliminary database search in December 2023 using “generative AI literacy” OR “generative artificial intelligence literacy” across five databases returned 6 records after duplicates, indicating no well-established concept; existing papers often conflate generative AI literacy with general AI literacy.
- The paper defines generative AI literacy as a long-term, continuous process of identifying, acquiring, practicing, and mastering core competencies to effectively and responsibly understand, assess, and work with generative AI technologies.
- It proposes the competencies as a foundation for assessments, curricula, professional development, public-sector guidelines, and further research, while noting that positive and negative implications of each competency are mostly speculative.
Methods
- Preliminary database search in ACM Digital Library, IEEExplore, Clarivate Web of Science, Scopus, and Dimensions with the query “generative AI literacy” OR “generative artificial intelligence literacy”; after removing duplicates, 6 records were included and summarized.
- Because of the topic’s novelty, the authors conducted a non-systematic, comprehensive literature review in English covering AI literacy, systematic reviews on generative AI applications, prompt engineering, and competency models.
- The authors identified twelve competencies from the literature, described each in detail with illustrative examples, and used a speculative approach for discussing potential implications due to scarce prior research.
Limitations
- The paper states that the novelty of the topic made a long-standing review approach infeasible, that the potential implications are mostly speculative and based on informed opinions and current trends, and that the competencies are designed based on the current state of the field and may need revision as technology advances.
- The text makes evident that the framework is conceptual and is not empirically validated or tested with a sample; no assessment instrument, proficiency metrics, or outcome data are reported.
- The preliminary database search was limited to one exact query and five databases, yielding only 6 records, which the authors use to justify the non-systematic review approach.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
0 of 58 cited sources checked · 0 supported · 0 flagged
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Given the low amount of results (6 records in total, as described in Section 3.1), all the articles are described in Table 1.”
- Also stated in: § 3.1 Preliminary Database Search · plain
“After removing duplicates, this search resulted in a total of 6 records, which have been included and summarized in Table 1.”
§ Generative AI Literacy: Twelve Defining Competencies > 2 METHODOLOGY
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None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Despite this, the concept of AI literacy has not evolved, and existing frameworks do not differentiate between generative and predictive models [61,62].”
- Also stated in: § 3.4 Need for a Generative AI Literacy competency model · plain
“the existing literacy frameworks do not differentiate between them.”
§ Generative AI Literacy: Twelve Defining Competencies > 3.2 AI literacy and generative AI
What the sources say
- Not matchedcites [61]
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None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“In the case of ChatGPT, for example, these include the temperature, which determines the “creativity” of the model, the maximum number of tokens to be used, and the top_k and top_p to limit the number of next word choices of the model and the probability of the next word choices, respectively [60,83].”
§ Generative AI Literacy: Twelve Defining Competencies > 5.7 Skill in prompting generative AI tools (Prompt Engineering)
What the sources say
- Not matchedcites [60]
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In the paper
“The output of ChatGPT and other LLMs can often contain a mixture of facts and completely false and fabricated statements, and awareness of this is fundamental [1,5].”
§ Generative AI Literacy: Twelve Defining Competencies > 5.3 Knowledge of the capacity and limitations of generative AI tools
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In the paper
“With the advance of generative AI, it has become increasingly difficult to discern which contents have been entirely human-generated, AI-generated, or AI-enhanced [29].”
§ Generative AI Literacy: Twelve Defining Competencies > 5.5 Ability to detect AI-generated content
What the sources say
- Not matchedcites [29]
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In the paper
“Most generative models that are deployed at the time, whether they are LLMs, text-to-speech, or text-to-image, rely on certain hyperparameters that can be adjusted by the users.”
§ Generative AI Literacy: Twelve Defining Competencies > 5.7 Skill in prompting generative AI tools (Prompt Engineering)
What the sources say
- Not matchedcites [60]
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- Not matchedcites [83]
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In the paper
“This includes a verification of the content of the output with the aid of trusted sources, so to prevent or minimize the effect of model hallucinations [1,3,5].”
§ Generative AI Literacy: Twelve Defining Competencies > 5.6 Ability to assess the output of generative AI tools
What the sources say
- Not matchedcites [1]
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None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Despite this, the concept of AI literacy has not evolved, and existing frameworks do not differentiate between generative and predictive models [61,62].”
§ Generative AI Literacy: Twelve Defining Competencies > 3.2 AI literacy and generative AI
What the sources say
- Not matchedcites [61]
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- Not matchedcites [62]
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In the paper
“It is important to note that prompt engineering is not limited to LLMs, but to all types of generative AI that use text as input, including text-to-image models, as evidenced by the existing literature and guidelines for text-to-image prompt engineering [57,108].”
§ Generative AI Literacy: Twelve Defining Competencies > 5.7 Skill in prompting generative AI tools (Prompt Engineering)
What the sources say
- Not matchedcites [57]
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- Probably not a substantial claim. 25
- It may state two separate claims as one. 14
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
- Also stated in: § 4 A COMPETENCY-BASED MODEL FOR GENERATIVE AI LITERACY · plain
“the ordering of these competencies is purposeful and follows a logical progression to represent a structured learning path reflecting a continuum of learning”
§ Generative AI Literacy: Twelve Defining Competencies > Abstract
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
- Rests on an assumption: The rapid rise of generative AI tools and their widespread adoption in various disciplines have created a unique set of skills specific to these novel tools, which go beyond the traditional competencies associated with data literacy, digital literacy, or AI literacy.
- Also stated in: § 4 A COMPETENCY-BASED MODEL FOR GENERATIVE AI LITERACY · plain
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Generative AI had a disruptive impact on different domains, and not all authorities and organizations have had the time to adapt to it by developing guidelines and rules, as in the case of the scientific and academic world [89,90].”
§ Generative AI Literacy: Twelve Defining Competencies > 5.9 Knowledge of the contexts where generative AI is used
What the sources say
- Not matchedcites [89]
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Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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In the paper
“some of these detectors tend to overestimate the probability that a text, image, or video is AI-generated, while others underestimate it, and there is often a great divergence in results across different AI detection tools [97].”
§ Generative AI Literacy: Twelve Defining Competencies > 5.5 Ability to detect AI-generated content
What the sources say
- Not matchedcites [97]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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In the paper
“In recent years, there has been a surge in software and AI models to detect AI-generated content, both text (such as ZeroGPTfoot_9 , GPTZerofoot_10 , PlagiarismCheckfoot_11 , GPT Radarfoot_12 , Writer.comfoot_13 , among others) [18,66,97], images (AI or Not 14 or Is it AI? 15 ) and videos [45,75].”
§ Generative AI Literacy: Twelve Defining Competencies > 5.5 Ability to detect AI-generated content
What the sources say
- Not matchedcites [18]
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Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“this has become a noteworthy skill to have in the labor market, as increasingly more companies require employees who can use and master generative AI [14,31].”
§ Generative AI Literacy: Twelve Defining Competencies > 5.4 Skill to use generative AI tools
What the sources say
- Not matchedcites [14]
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Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The twelve competencies identified in this paper can be intended as a complement to the existing framework on AI literacy, providing additional guidance on the specific toolbox that a user of generative AI tools needs.”
§ Generative AI Literacy: Twelve Defining Competencies > Abstract
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Rests on an assumption: The rapid rise of generative AI tools and their widespread adoption in various disciplines have created a unique set of skills specific to these novel tools, which go beyond the traditional competencies associated with data literacy, digital literacy, or AI literacy.
In the paper
“This concept of generative AI literacy is associated with that of prompt engineering, which is the ability to design effective prompts for generative AI models to produce desired outputs [79].”
§ Generative AI Literacy: Twelve Defining Competencies > Abstract
What the sources say
- Not matchedcites [79]
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In the paper
“However, human detection of AI-generated photos and texts becomes increasingly difficult [68], making AI detectors always more significant.”
§ Generative AI Literacy: Twelve Defining Competencies > 5.5 Ability to detect AI-generated content
What the sources say
- Not matchedcites [68]
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In the paper
“Multimodal prompting, including images and other formats as input, has also emerged and increased in popularity [76]”
§ Generative AI Literacy: Twelve Defining Competencies > 5.7 Skill in prompting generative AI tools (Prompt Engineering)
What the sources say
- Not matchedcites [76]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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In the paper
“Different sets of guidelines have been written on prompt engineering for ChatGPT and LLMs, highlighting its importance [2,34,87,100].”
§ Generative AI Literacy: Twelve Defining Competencies > 5.7 Skill in prompting generative AI tools (Prompt Engineering)
What the sources say
- Not matchedcites [2]
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As observed from Table 1, there is not a well-established concept of generative AI literacy in the current academic literature.”
- Also stated in: § 3.1 Preliminary Database Search · plain
“The few existing papers tend to conflate it with the more general and better-established concept of AI literacy.”
§ Generative AI Literacy: Twelve Defining Competencies > 3.1 Preliminary Database Search
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Also stated in: § 3.1 Preliminary Database Search · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This paper aims to fill a gap at the intersection of the topics of AI literacy and generative AI, as no set of competencies specific to the use of generative AI had been previously proposed.”
- Also stated in: § 2 METHODOLOGY · plain
“The scarcity of results highlighted the novelty of the concept and the current gap in academic literature.”
- Also stated in: § 3.4 Need for a Generative AI Literacy competency model · plain
“the database search showed the absence of literature defining the concept of generative AI literacy and also a comprehensive framework that systematically addresses competency acquisition in navigating and utilizing generative AI tools.”
§ Generative AI Literacy: Twelve Defining Competencies > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Also stated in: § 2 METHODOLOGY · plain
In the paper
“As noted, AI literacy does not equate to computing literacy, nor is computing literacy necessary for AI literacy, as individuals may recognize, learn about, and use AI without necessarily being able to program or having experience in computer science and engineering [61].”
§ Generative AI Literacy: Twelve Defining Competencies > 5.1 Basic AI Literacy
What the sources say
- Not matchedcites [61]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
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In the paper
“the existing frameworks on AI literacy tend to be quite generic, failing to address the specificities of generative AI tools.”
§ Generative AI Literacy: Twelve Defining Competencies > 3.4 Need for a Generative AI Literacy competency model
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“A number of techniques have emerged for this purpose, including automatically generated prompts [86], Chain of Thoughts prompting [50,98,107] and Tree of Thoughts prompting [62,104].”
§ Generative AI Literacy: Twelve Defining Competencies > 5.7 Skill in prompting generative AI tools (Prompt Engineering)
What the sources say
- Not matchedcites [86]
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In the paper
“Guidelines and techniques to help users identify which images, videos, and text have been created by AI [39] are already being discussed.”
§ Generative AI Literacy: Twelve Defining Competencies > 5.5 Ability to detect AI-generated content
What the sources say
- Not matchedcites [39]
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Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“while prompt engineering is one of the competencies and is crucial for generative AI literacy, the proposed model goes well beyond it and the more technical features of generative AI, and considers the contextual and situational aspects.”
§ Generative AI Literacy: Twelve Defining Competencies > Abstract
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Rests on an assumption: The rapid rise of generative AI tools and their widespread adoption in various disciplines have created a unique set of skills specific to these novel tools, which go beyond the traditional competencies associated with data literacy, digital literacy, or AI literacy.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Hence, fixing these parameters is an additional and more advanced skill to know when mastering prompt engineering.”
§ Generative AI Literacy: Twelve Defining Competencies > 5.7 Skill in prompting generative AI tools (Prompt Engineering)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Prompt engineering consists of a broad skill set, with different methods that allow to effectively interact with generative models [82].”
§ Generative AI Literacy: Twelve Defining Competencies > 5.7 Skill in prompting generative AI tools (Prompt Engineering)
What the sources say
- Not matchedcites [82]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Generative AI literacy goes beyond technical proficiency and requires combining it with other proficiencies such as adaptive usage, ethical considerations, and legal awareness.”
§ Generative AI Literacy: Twelve Defining Competencies > 7 CONCLUSION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Generative AI literacy is not a static set of skills, but an evolving one.”
§ Generative AI Literacy: Twelve Defining Competencies > 7 CONCLUSION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
- Rests on an assumption: Generative AI technology is evolving rapidly, with new models and techniques continually emerging, each with its own set of intricacies.
In the paper
“As part of organizations, individuals already use (or can use) generative AI applications for tasks such as note-taking, summarization, time management, and providing faster support to common queries [12].”
§ Generative AI Literacy: Twelve Defining Competencies > 6.2 Relevance for the Public Sector
What the sources say
- Not matchedcites [12]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“in 2023 President Joe Biden issued Executive Order 14110 on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence [40]”
§ Generative AI Literacy: Twelve Defining Competencies > 5.11 Knowledge of Legal Aspects
What the sources say
- Not matchedcites [40]
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In the paper
“In addition to its role in supporting policy development and regulation, generative AI has the potential to ease access to internal knowledge and databases through chat and also provide answers to frequently asked questions by citizens [28].”
§ Generative AI Literacy: Twelve Defining Competencies > 6.2 Relevance for the Public Sector
What the sources say
- Not matchedcites [28]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, the competencies outlined here are not confined to specialists; rather, they cater to a broader audience, reflecting the growing integration of AI into various aspects of daily life.”
§ Generative AI Literacy: Twelve Defining Competencies > 7 CONCLUSION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“An example of such a scenario occurred during the campaign for Switzerland's 2023 elections [99].”
§ Generative AI Literacy: Twelve Defining Competencies > 5.10 Knowledge of the Ethical Implications
What the sources say
- Not matchedcites [99]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“AI is already employed at various levels in the public sector, including service delivery, management, and monitoring [10,43,64,95].”
§ Generative AI Literacy: Twelve Defining Competencies > 6.2 Relevance for the Public Sector
What the sources say
- Not matchedcites [10]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [43]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [64]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [95]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“While at the time of writing this paper, only a few countries have begun a legislative process regulating AI and its applications, it is likely that several others will follow [37,65].”
§ Generative AI Literacy: Twelve Defining Competencies > 5.11 Knowledge of Legal Aspects
What the sources say
- Not matchedcites [37]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [65]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Indeed, in March 2024 the European Parliament approved the European AI Act [32], determining a set of norms to be followed with respect to several aspects of AI at large [41,42,63].”
§ Generative AI Literacy: Twelve Defining Competencies > 5.11 Knowledge of Legal Aspects
What the sources say
- Not matchedcites [32]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [41]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [42]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [63]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“Several universities have developed guidelines for the use of LLMs in assignments [26].This differs from campus to campus, with some of them prohibiting LLMs altogether, and others allowing them for certain types of coursework.”
§ Generative AI Literacy: Twelve Defining Competencies > 5.9 Knowledge of the contexts where generative AI is used
What the sources say
- Not matchedcites [26]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“several states from the United States have begun to adopt laws targeting specific aspects of AI [17]”
§ Generative AI Literacy: Twelve Defining Competencies > 5.11 Knowledge of Legal Aspects
What the sources say
- Not matchedcites [17]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper relies on this without support.
The rapid rise of generative AI tools and their widespread adoption in various disciplines have created a unique set of skills specific to these novel tools, which go beyond the traditional competencies associated with data literacy, digital literacy, or AI literacy.
“the rapid rise of generative AI tools and their widespread adoption in various disciplines have created a unique set of skills specific to these novel tools, which go beyond the traditional competencies associated with data literacy, digital literacy, or AI literacy.”
The paper relies on this without support.
Generative models have the potential to fabricate highly realistic content and give rise to ethical dilemmas about misinformation, authenticity, and societal impact.
“Generative models are ingrained with the potential to fabricate highly realistic content and give rise to ethical dilemmas about misinformation, authenticity, and societal impact.”
The paper relies on this without support.
The outputs of generative AI raise critical questions on authorship, ownership, and originality.
“In the third place, the outputs of generative AI raise critical questions on authorship, ownership, and originality.”
The paper relies on this without support.
Generative AI tools are increasingly becoming accessible and user-friendly, necessitating additional aspects of AI literacy for the general public, not just for people specialized in technology.
“Finally, generative AI tools are increasingly becoming accessible and user-friendly, necessitating additional aspects of AI literacy for the general public, not just for people specialized in technology.”
The paper relies on this without support.
Most prompt engineering techniques include an assessment of the output.
“most prompt engineering techniques include an assessment of the output, but this does not necessarily imply that the user is fully proficient in prompt engineering.”
The paper relies on this without support.
Most users will neither have nor need programming and fine-tuning ability for their specific goals, and can still be literate on generative AI without it.
“Most users will neither have nor need this ability for their specific goals, and still be literate on generative AI without it.”
The paper relies on this without support.
Generative AI technology is evolving rapidly, with new models and techniques continually emerging, each with its own set of intricacies.
“Generative AI technology is evolving rapidly, with new models and techniques continually emerging, each with its own set of intricacies.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The result is a paradox: tools designed to accelerate research can, when used uncritically, undermine the very skills graduate education is meant to cultivate [1].”
From Students' Practices and Skills in the LLM-Era: "You Can't Outsource the Struggle and Still Get the Skill" (2026) · cites this paper as [1]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The innovation dimension relates to developing and tailoring new AI tools and workflows to specific educational needs and engaging in human-AI co-creation (Annapureddy et al., 2025).”
From An integrated AI literacy framework for higher‑education teachers: a critical review and conceptual synthesis (2026) · cites this paper as Annapureddy et al., 2025
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“A few studies have proposed AI literacy frameworks for higher education, identifying core competencies such as prompt engineering, ethical awareness, and critical evaluation of AI outputs [1, 22].”
From Students' Practices and Skills in the LLM-Era: "You Can't Outsource the Struggle and Still Get the Skill" (2026) · cites this paper as [1]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The study identified significant gaps in ethical understanding, innovative use of AI and institutional support among faculty.”
From An integrated AI literacy framework for higher‑education teachers: a critical review and conceptual synthesis (2026) · cites this paper as Annapureddy et al., 2025
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Pentagon example changes from a video to a photo
MinorThe example describes a viral AI-generated video, whereas both cited references describe the incident as a fake picture or photo.
“A recent example from 2023 is the case of a viral AI-generated video of the Pentagon on fire, which created some panic [23,38].”
“38. Mohammed Haddad. 2023. Fake Pentagon explosion photo goes viral: How to spot an AI image.”
AI-literacy definition is cited to the wrong reference
MinorThe text attributes the definition to Long and Magerko as reference [61], but reference 61 is a paper by Leo S. Lo. Long and Magerko are listed as reference 62.
“An often-cited definition of AI literacy is given by Long and Magerko [61]”
“61. Leo S. Lo. 2023. The CLEAR path: A framework for enhancing information literacy through prompt engineering.”
NIH competency definition is cited to the wrong reference
MinorThe quoted competency definition is attributed to the National Institute of Health as reference [70], but reference 70 is by Noh and Han. The NIH entry is reference 71.
“this paper adopts the definition given by the National Institute of Health (NIH), according to which "Competencies are: Knowledge (Information developed or learned through experience, study, or investigation), Skills (The result of repeatedly applying knowledge or ability); Abilities (An innate potential to perform mental and physical actions or tasks); Behaviors (The observable reaction of an individual to a certain situation)" [70].”
“70. YangJin Noh and Songlee Han. 2023. Exploring perception and support strategies for Generative AI Literacy: Focusing on pre-service secondary teachers.”
Citations
Cites
Transformers: State-of-the-Art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.6 · Cited by 8,741
ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · doi:10.35542/osf.io/5er8f · Cited by 7,552
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326 · cited 1×
“Multiple systematic reviews have been published on the topic [78, 81] and discussing the impacts of ChatGPT and generative AI in general on different areas including education [6, 47, 49, 58, 72, 81, 88], healthcare [4, 52, 56, 92, 93], research [77, 80, 88], and society [7], among others.”
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584
Generative Adversarial Networks: An Overview
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2765202 · Cited by 4,540
Chain-Of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1800 · Cited by 4,505
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352
Large language models in medicine
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., & Ting, D. S. W. (2023) · Nature Medicine · doi:10.1038/s41591-023-02448-8 · Cited by 3,944
BAİDOO-ANU, D., & OWUSU ANSAH, L. (2023) · Journal of AI · doi:10.61969/jai.1337500 · Cited by 3,939
Learning to Prompt for Vision-Language Models
Zhou, K., Yang, J., Loy, C. C., & Liu, Z. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01653-1 · Cited by 3,304 · cited 1×
“It is important to note that prompt engineering is not limited to LLMs, but to all types of generative AI that use text as input, including text-to-image models, as evidenced by the existing literature and guidelines for text-to-image prompt engineering [57, 108].”
What is AI Literacy? Competencies and Design Considerations
Long, D., & Magerko, B. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376727 · Cited by 3,241 · cited 6×
“Concept introduced but not defined. communicate and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace” [61].”
InfluentialSallam, M. (2023) · Healthcare · doi:10.3390/healthcare11060887 · Cited by 3,126 · cited 3×
“Thus, in addition to the knowledge of the ethical issues from the technical side, one has to build an understanding of the origins and mechanisms of AI-generated content to mitigate potential harm on the issues inherent to the dissemination and consumption of generated content.”
Chatting and cheating: Ensuring academic integrity in the era of ChatGPT
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2023.2190148 · Cited by 3,087
Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT
Cotton, D., Cotton, P., & Shipway, J. R. (2023) · doi:10.35542/osf.io/mrz8h · Cited by 3,079
Baidoo-Anu, D., & Owusu Ansah, L. (2023) · doi:10.2139/ssrn.4337484 · Cited by 3,004
An Introduction to Variational Autoencoders
Kingma, D. P., & Welling, M. (2019) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000056 · Cited by 2,609
Students' voices on generative ai: perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023) · arXiv · doi:10.48550/arxiv.2305.00290 · Cited by 2,504
Ray, P. P. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.04.003 · Cited by 2,463
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023) · arXiv · doi:10.48550/arxiv.2303.10130 · Cited by 2,330
Students’ voices on generative AI: perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00411-8 · Cited by 2,301
Conceptualizing AI literacy: An exploratory review
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100041 · Cited by 2,205 · cited 1×
“Several studies in recent years have explored the concept of AI literacy [67, 105], and a number of systematic reviews have been published on the topic [15, 61, 67].”
Conditional Prompt Learning for Vision-Language Models
Zhou, K., Yang, J., Loy, C. C., & Liu, Z. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01631 · Cited by 2,069
What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature
Lo, C. K. (2023) · Education Sciences · doi:10.3390/educsci13040410 · Cited by 2,024
Large language models are zero-shot reasoners
Kojima, T., Gu, S. (., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1613 · Cited by 2,013
ChatGPT: five priorities for research
van Dis, E. A. M., Bollen, J., Zuidema, W., van Rooij, R., & Bockting, C. L. (2023) · Nature · doi:10.1038/d41586-023-00288-7 · Cited by 1,842
Deist, F. L., & Winterton, J. (2005) · Human Resource Development International · doi:10.1080/1367886042000338227 · Cited by 1,583
Artificial hallucinations in ChatGPT: implications in scientific writing
Alkaissi, H., & McFarlane, S. I. (2023) · Cureus · doi:10.7759/cureus.35179 · Cited by 1,540
A Brief Overview of ChatGPT: The History, Status Quo and Potential Future Development
Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q.-L., & Tang, Y. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123618 · Cited by 1,452
ChatGPT for Language Teaching and Learning
Kohnke, L., Moorhouse, B. L., & Zou, D. (2023) · RELC Journal · doi:10.1177/00336882231162868 · Cited by 1,371
Generative AI and ChatGPT: applications, challenges, and AI-human collaboration
Fui-Hoon Nah, F., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023) · Journal of Information Technology Case and Application Research · doi:10.1080/15228053.2023.2233814 · Cited by 1,363
Artificial Intelligence and the Public Sector—Applications and Challenges
Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019) · International Journal of Public Administration · doi:10.1080/01900692.2018.1498103 · Cited by 1,341 · cited 1×
“AIisalreadyemployed at various levels in the public sector, including service delivery, management, and monitoring [10, 43, 64, 95].”
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Shin, T., Razeghi, Y., Logan IV, R. L., Wallace, E., & Singh, S. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.346 · Cited by 1,279
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023) · arXiv · doi:10.48550/arxiv.2305.10601 · Cited by 1,158
Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2022) · doi:10.1101/2022.12.23.521610 · Cited by 1,099 · cited 1×
“At the same time, it is noteworthy that this task is becoming increasingly difficult, and individuals should ideally be aware of state-of-the-art tools to try to optimize their probability to correctly distinguish AI-generated from human content [8, 33, 35].”
Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00819-6 · Cited by 1,099
Unleashing the potential of prompt engineering for large language models
CHEN, B. ‐., Zhang, Z., Langrené, N., & Zhu, S. (2025) · Patterns · doi:10.1016/j.patter.2025.101260 · Cited by 1,097
The imperative for regulatory oversight of large language models (or generative AI) in healthcare
Meskó, B., & Topol, E. J. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00873-0 · Cited by 1,047
Multimodal Chain-of-Thought Reasoning in Language ModelsPreprint
Zhang, Z., Zhang, A., Li, M., Zhao, H., Karypis, G., & Smola, A. J. (2023) · Trans. Mach. Learn. Res. · doi:10.48550/arxiv.2302.00923 · Cited by 994
Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm
Reynolds, L., & McDonell, K. (2021) · Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411763.3451760 · Cited by 952 · cited 2×
“This concept of generative AI literacy is associated with that of prompt engineering , which is the ability to design effective prompts for generative AI models to produce desired outputs [79].”
Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., Boselie, P., Lee Cooke, F., Decker, S., DeNisi, A., et al. (2023) · Human Resource Management Journal · doi:10.1111/1748-8583.12524 · Cited by 908 · cited 1×
“Indeed, this has become a noteworthy skill to have in the labor market, as increasingly more companies require employees who can use and master generative AI [14, 31].”
ChatGPT listed as author on research papers: many scientists disapprove
Stokel-Walker, C. (2023) · Nature · doi:10.1038/d41586-023-00107-z · Cited by 868
A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT
White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., & Schmidt, D. C. (2023) · arXiv · doi:10.48550/arxiv.2302.11382 · Cited by 838
What ChatGPT and generative AI mean for science
Stokel-Walker, C., & Van Noorden, R. (2023) · Nature · doi:10.1038/d41586-023-00340-6 · Cited by 828
Prompt Engineering with ChatGPT: A Guide for Academic Writers
Giray, L. (2023) · Annals of Biomedical Engineering · doi:10.1007/s10439-023-03272-4 · Cited by 776
AI literacy in K-12: a systematic literature review
Casal-Otero, L., Catala, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023) · International Journal of STEM Education · doi:10.1186/s40594-023-00418-7 · Cited by 724
General competencies and accreditation in graduate medical education.
Batalden, P., Leach, D., Swing, S. R., Dreyfus, H., & Dreyfus, S. B. (2002) · Health Affairs · doi:10.1377/hlthaff.21.5.103 · Cited by 699
Design Guidelines for Prompt Engineering Text-to-Image Generative Models
Liu, V., & Chilton, L. B. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501825 · Cited by 639 · cited 1×
“It is important to note that prompt engineering is not limited to LLMs, but to all types of generative AI that use text as input, including text-to-image models, as evidenced by the existing literature and guidelines for text-to-image prompt engineering [57, 108].”
Design Guidelines for Prompt Engineering Text-to-Image Generative Models
Liu, V., & Chilton, L. B. (2021) · arXiv · doi:10.48550/arxiv.2109.06977 · Cited by 635
Generating scholarly content with ChatGPT: Ethical challenges for medical publishing
Liebrenz, M., Schleifer, R., Buadze, A., Bhugra, D., & Smith, A. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00019-5 · Cited by 594
Testing of detection tools for AI-generated text
Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., Šigut, P., & Waddington, L. (2023) · International Journal for Educational Integrity · doi:10.1007/s40979-023-00146-z · Cited by 582 · cited 2×
“Example : Several universities have developed guidelines for the use of LLMs in assignments [26].”
Preiksaitis, C., & Rose, C. (2023) · JMIR Medical Education · doi:10.2196/48785 · Cited by 552 · cited 1×
“Last, incorporating differentiation between generative and predictive models into professional training programs and educational initiatives is crucial to address the challenges of each model type and be better prepared for using generative AI in typical applications, or in specific careers in…”
The CLEAR path: A framework for enhancing information literacy through prompt engineering
Lo, L. S. (2023) · The Journal of Academic Librarianship · doi:10.1016/j.acalib.2023.102720 · Cited by 485
Hoffmann, T. M. (1999) · Journal of European Industrial Training · doi:10.1108/03090599910284650 · Cited by 481
ChatGPT for education and research: A review of benefits and risks
Sok, S., & Heng, K. (2023) · Cambodian Journal of Educational Research · doi:10.62037/cjer.2023.03.01.06 · Cited by 475
Regulating ChatGPT and other Large Generative AI Models
Hacker, P., Engel, A., & Mauer, M. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594067 · Cited by 466 · cited 1×
“…article, only a few countries have begun a legislative process regulating AI and its applications, it is likely that several others will follow [37, 65] Parliament approved the European AI Act [32], determining a set of norms to be followed with respect to several aspects of AI at large [41,…”
ChatGPT in healthcare: A taxonomy and systematic review
Li, J., Dada, A., Puladi, B., Kleesiek, J., & Egger, J. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108013 · Cited by 452
What Is Meant by a Competency?
Woodruffe, C. (1993) · Leadership & Organization Development Journal · doi:10.1108/eb053651 · Cited by 417
ChatGPT in Healthcare: A Taxonomy and Systematic ReviewPreprint
Li, J., Dada, A., Puladi, B., et al. (2023) · medRxiv · doi:10.1101/2023.03.30.23287899 · Cited by 411
Athaluri, S. A., Manthena, S. V., Kesapragada, V. S. R. K. M., Yarlagadda, V., Dave, T., & Duddumpudi, R. T. S. (2023) · Cureus · doi:10.7759/cureus.37432 · Cited by 403
AI-synthesized faces are indistinguishable from real faces and more trustworthy
Nightingale, S. J., & Farid, H. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2120481119 · Cited by 397
Using AI-generated suggestions from ChatGPT to optimize clinical decision support
Liu, S., Wright, A. P., Patterson, B. L., Wanderer, J. P., Turer, R. W., Nelson, S. D., McCoy, A. B., Sittig, D. F., & Wright, A. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad072 · Cited by 397
OpenAI ChatGPT Generated Literature Review: Digital Twin in Healthcare
Aydın, Ö., & Karaarslan, E. (2022) · doi:10.2139/ssrn.4308687 · Cited by 391
ChatGPT for Education and Research: A Review of Benefits and Risks
Sok, S., & Heng, K. (2023) · doi:10.2139/ssrn.4378735 · Cited by 386
van Noordt, C., & Misuraca, G. (2022) · Government Information Quarterly · doi:10.1016/j.giq.2022.101714 · Cited by 356
Large Language Model Guided Tree-of-ThoughtPreprint
Long, J.-Y. (2023) · arXiv.org · doi:10.48550/arxiv.2305.08291 · Cited by 353
Large language models and the perils of their hallucinations
Azamfirei, R., Kudchadkar, S. R., & Fackler, J. C. (2023) · Critical Care · doi:10.1186/s13054-023-04393-x · Cited by 352
Improving public services using artificial intelligence: possibilities, pitfalls, governance
Henman, P. (2020) · Asia Pacific Journal of Public Administration · doi:10.1080/23276665.2020.1816188 · Cited by 310
An integrated approach to BIM competency assessment, acquisition and application
Succar, B., Sher, W., & Williams, A. P. (2013) · Automation in Construction · doi:10.1016/j.autcon.2013.05.016 · Cited by 303
Understanding Competencies and Competency Modeling ― A Literature Survey
Chouhan, V. S., & Srivastava, S. K. (2014) · IOSR Journal of Business and Management · doi:10.9790/487x-16111422 · Cited by 295
ChatGPT: Is this version good for healthcare and research?
Vaishya, R., Misra, A., & Vaish, A. (2023) · Diabetes & Metabolic Syndrome: Clinical Research & Reviews · doi:10.1016/j.dsx.2023.102744 · Cited by 293
ChatGPT and Academic Research: A Review and Recommendations Based on Practical Examples
Rahman, M. M., Terano, H. J. R., Rahman, M. N., Salamzadeh, A., & Rahaman, M. S. (2023) · Journal of Education Management and Development Studies · doi:10.52631/jemds.v3i1.175 · Cited by 277
A Systematic Survey of Prompt Engineering on Vision-Language Foundation ModelsPreprint
Gu, J., Han, Z. Y., Chen, S., Beirami, A., He, B., Zhang, G., Liao, R., Qin, Y., Volker Tresp, C., & Torr, P. H. S. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2307.12980 · Cited by 266
Review of large vision models and visual prompt engineering
Wang, J., Liu, Z., Zhao, L., Wu, Z., Ma, C., Yu, S., Dai, H., Yang, Q., Liu, Y., Zhang, S., et al. (2023) · Meta-Radiology · doi:10.1016/j.metrad.2023.100047 · Cited by 257
Prompting GPT-3 To Be ReliablePreprint
Si, C., Gan, Z., Yang, Z., Wang, S., Wang, J., Boyd-Graber, J. L., & Wang, L. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2210.09150 · Cited by 251
DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms
Qi, H., Guo, Q., Juefei-Xu, F., Xie, X., Ma, L., Feng, W., Liu, Y., & Zhao, J. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413707 · Cited by 220
Ask Me Anything: A simple strategy for prompting language modelsPreprint
Arora, S., Narayan, A., Chen, M. F., Orr, L., Guha, N., Bhatia, K., Chami, I., Sala, F., & Ré, C. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2210.02441 · Cited by 218
ChatGPT in education: a discourse analysis of worries and concerns on social media
Li, L., Ma, Z., Fan, L., Lee, S., Yu, H., & Hemphill, L. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-12256-9 · Cited by 218
Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples
Hussain, S., Neekhara, P., Jere, M., Koushanfar, F., & McAuley, J. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00339 · Cited by 204
Data science, artificial intelligence and the third wave of digital era governance
Dunleavy, P., & Margetts, H. (2023) · doi:10.31219/osf.io/f3rza · Cited by 186
Data science, artificial intelligence and the third wave of digital era governance
Dunleavy, P., & Margetts, H. (2025) · Public Policy and Administration · doi:10.1177/09520767231198737 · Cited by 186
On the measurement of competency
Shavelson, R. J. (2010) · Empirical research in vocational education and training · doi:10.1007/bf03546488 · Cited by 185
Bozkurt, A. (2023) · Open Praxis · doi:10.55982/openpraxis.15.4.609 · Cited by 179
Meijer, A., Lorenz, L., & Wessels, M. (2021) · Public Administration Review · doi:10.1111/puar.13391 · Cited by 174
Using ChatGPT in Medical Research: Current Status and Future Directions
Ruksakulpiwat, S., Kumar, A., & Ajibade, A. (2023) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s413470 · Cited by 156
Chan, A. (2023) · AI and Ethics · doi:10.1007/s43681-022-00148-6 · Cited by 155
The Social Impact of Generative AI: An Analysis on ChatGPT
Baldassarre, M. T., Caivano, D., Nieto, B. F., Gigante, D., & Ragone, A. (2023) · International Conference on Information Technology for Social Good (GoodIT) · doi:10.1145/3582515.3609555 · Cited by 151
23 Artificial Intelligence Act
(2024) · Carl Hanser Verlag GmbH & Co. KG eBooks · doi:10.3139/9781569902356.023 · Cited by 140
Unleashing the potential of prompt engineering for large language modelsPreprint
CHEN, B. ‐., Zhang, Z., Langrené, N., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.14735 · Cited by 138
The Art and Science of Prompt Engineering: A New Literacy in the Information Age
Lo, L. S. (2023) · Internet Reference Services Quarterly · doi:10.1080/10875301.2023.2227621 · Cited by 136
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.2.12 · Cited by 132 · cited 1×
“In recent years, there has been a surge in software and AI models to detect AI-generated content, like text ( ZeroGPT , 9 GPTZero , 10 PlagiarismCheck , 11 GPT Radar , 12 and Writer.com , 13 etc.) [18, 66, 97], images ( AI or Not 14 or Is it AI?”
Helberger, N., & Diakopoulos, N. (2023) · Internet Policy Review · doi:10.14763/2023.1.1682 · Cited by 123
Establishing the concept of AI literacy: Focusing on competence and purpose
Yi, Y. (2021) · JAHR · doi:10.21860/j.12.2.8 · Cited by 113
Human detection of political speech deepfakes across transcripts, audio, and video
Groh, M., Sankaranarayanan, A., Singh, N., Kim, D. Y., Lippman, A., & Picard, R. (2024) · Nature Communications · doi:10.1038/s41467-024-51998-z · Cited by 89
Bright, J., Enock, F. E., Esnaashari, S., Francis, J., Hashem, Y., & Morgan, D. (2024) · Digital Government Research and Practice · doi:10.1145/3700140 · Cited by 83
Generative AI Professional Development Needs for Teacher Educators
Nyaaba, M., & Zhai, X.-M. (2024) · Journal of AI · doi:10.61969/jai.1385915 · Cited by 81
AI vs. AI: Can AI Detect AI-Generated Images?
Baraheem, S. S., & Nguyen, T. (2023) · Journal of Imaging · doi:10.3390/jimaging9100199 · Cited by 79
The European AI Act and How It Matters for Research into AI in Media and Journalism
Helberger, N., & Diakopoulos, N. (2022) · Digital Journalism · doi:10.1080/21670811.2022.2082505 · Cited by 73
Initial Images: Using Image Prompts to Improve Subject Representation in Multimodal AI Generated Art
Qiao, H., Liu, V., & Chilton, L. (2022) · Creativity and Cognition · doi:10.1145/3527927.3532792 · Cited by 46
Augmented Imagination: Exploring Generative AI from the Perspectives of Young Learners
Putjorn, T., & Putjorn, P. (2023) · 2023 15th International Conference on Information Technology and Electrical Engineering (ICITEE) · doi:10.1109/icitee59582.2023.10317680 · Cited by 37
Artificial Intelligence in Law
Nikolskaia, K. Y., & Naumov, V. B. (2020) · International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon) · doi:10.1109/fareastcon50210.2020.9271095 · Cited by 34
Testing of Detection Tools for AI-Generated Text
Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., Šigut, P., & Waddington, L. (2023) · arXiv · doi:10.48550/arxiv.2306.15666 · Cited by 32
A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT
(2024) · Proceedings of the 30th Conference on Pattern Languages of Programs · doi:10.64346/plop2023p05 · Cited by 32 · cited 1×
“…only a few countries have begun a legislative process regulating AI and its applications, it is likely that several others will follow [37, 65] Parliament approved the European AI Act [32], determining a set of norms to be followed with respect to several aspects of AI at large [41, 42, 63].”
Kreinsen, M., & Schulz, S. (2023) · doi:10.35542/osf.io/xguzk · Cited by 29
Hwang, Y., Lee, J. H., & Shin, D. (2023) · arXiv · doi:10.48550/arxiv.2311.05373 · Cited by 29
ChatGPT in education: A discourse analysis of worries and concerns on social mediaPreprint
Li, L., Ma, Z., Fan, L., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.02201 · Cited by 25
Competency and Human Resource Management
Cernușca, L., & Dima, C. (2008) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · Cited by 19
Sand-in-the-loop: Investigating embodied co-creation for shared understandings of generative AI
El-Zanfaly, D., Huang, Y., & Dong, Y. (2023) · Designing Interactive Systems Conference · doi:10.1145/3563703.3596652 · Cited by 19
Dadhich, M., & Bhaumik, A. (2023) · IEEE International Conference on ICT in Business Industry & Government (ICTBIG) · doi:10.1109/ictbig59752.2023.10456172 · Cited by 18
Human Detection of Political Speech Deepfakes across Transcripts, Audio, and VideoPreprint
Groh, M., Sankaranarayanan, A., Singh, N., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2202.12883 · Cited by 11
Noh, Y., & Han, S. (2023) · Korean Association For Learner-Centered Curriculum And Instruction · doi:10.22251/jlcci.2023.23.17.317 · Cited by 10
Generative AI Professional Development Needs for Teacher EducatorsPreprint
Nyaaba, M., & Zhaı, X. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4628126 · Cited by 6
Schüller, K., Rampelt, F., Koch, H., & Schleiß, J. (2023) · Gesellschaft für Informatik (GI) · doi:10.18420/inf2023_49 · Cited by 5
Sarrion, E. (2023) · Apress eBooks · doi:10.1007/978-1-4842-9804-6_6 · Cited by 1
Generative AI is already widespread in the public sector
Bright, J., Enock, F. E., Esnaashari, S., Francis, J., Hashem, Y., & Morgan, D. (2024) · arXiv · doi:10.48550/arxiv.2401.01291
Flagged first, then by citations
Research works printed but not linked: 2
- Yoon Chae. 2020. US AI regulation guide: Legislative overview and practical considerations. Journal of Robotics, Artificial Intelligence & Law 3 (2020), 17–40.
- Tambiama Madiega. 2021. Artificial Intelligence Act. European Parliamentary Research Service.https://policycommons.net/artifacts/1894362/artificial-intelligence-act/2644377/#::text=https%3A//policycommons.net/artifacts/1894362/artificial%2Dintelligence%2Dact/2644377/
Cited by
Sorted by citations
GLAT: The generative AI literacy assessment test
Jin, Y., Martínez‐Maldonado, R., Gašević, D., & Yan, L. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100436 · Cited by 119
Human detection of political speech deepfakes across transcripts, audio, and video
Groh, M., Sankaranarayanan, A., Singh, N., Kim, D. Y., Lippman, A., & Picard, R. (2024) · Nature Communications · doi:10.1038/s41467-024-51998-z · Cited by 89
Liu, X., & Zhong, B. (2025) · Educational Research Review · doi:10.1016/j.edurev.2025.100741 · Cited by 78
Algorithmic Literacy, AI Literacy and Responsible Generative AI Literacy
Cox, A. (2024) · Journal of Web Librarianship · doi:10.1080/19322909.2024.2395341 · Cited by 67
Exploring students’ experience of ChatGPT in STEM education
Valeri, F., Nilsson, P., & Cederqvist, A. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100360 · Cited by 73
Metadata
- Authors
- Annapureddy, R., Fornaroli, A., & Gatica-Perez, D.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Idiap Research Institute (CH)
- Idiap Research Institute, Martigny, Switzerland and EPFL, Lausanne, Switzerland
- Venue
- Digital Government: Research and Practice
- Published
- 2025-02-13
- Volume
- 6
- Issue
- 1
- Pages
- 1-21
- ISSN
- 2639-0175 (ISSN-L)
- 2691-199X
- OpenAlex
- W4401288535
- Cited by
- 333
- References
- 95
- Citations by year
- 2026: 245 · 2025: 81
- FWCI
- 166.06
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Digital Government Research and Practice (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- European Union’s Horizon 2020 IcARUS project (882749)
- Horizon Europe ELIAS project (101120237)
- Topics
- Social Sciences › Education › Digital literacy in education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Education › Artificial Intelligence in Education
- Keywords
- generative AI literacy, competency-based model, prompt engineering, AI ethics, responsible AI use, artificial intelligence in education, professional training, AI regulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-11-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-11-06 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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