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MAILS - Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies
Open accessAbstract
Valid measurement of AI literacy is important for the selection of personnel, identification of shortages in skill and knowledge, and evaluation of AI literacy interventions. A questionnaire is missing that is deeply grounded in the existing literature on AI literacy, is modularly applicable depending on the goals, and includes further psychological competencies in addition to the typical facets of AIL. This paper presents the development and validation of a questionnaire considering the desiderata described above. We derived items to represent different facets of AI literacy and psychological competencies, such as problem-solving, learning, and emotion regulation in regard to AI. We collected data from 300 German-speaking adults to confirm the factorial structure. The result is the Meta AI Literacy Scale (MAILS) for AI literacy with the facets Use & apply AI, Understand AI, Detect AI, and AI Ethics and the ability to Create AI as a separate construct, and AI Self-efficacy in learning and problem-solving and AI Self-management (i.e., AI persuasion literacy and emotion regulation). This study contributes to the research on AI literacy by providing a measurement instrument relying on profound competency models. Psychological competencies are included particularly important in the context of pervasive change through AI systems.
Verified 2026-11-05 · 1 reference not checked yet
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper develops and tests MAILS, a modular self-report questionnaire for AI literacy grounded in Ng et al.’s competency model and extended with psychological meta-competencies. In 300 German-speaking adults, confirmatory factor analyses supported AI Literacy facets (Use & Apply AI, Know & Understand AI, Detect AI, AI Ethics), a separate Create AI factor, and two psychological domains: AI Self-efficacy and AI Self-competency.
Key findings
- The final modified CFA model had good fit despite a significant chi-square: χ²(513) = 886.87, p < .001, CFI = .926, TLI = .920, RMSEA = .057, 95% CI [.051, .063], SRMR = .079.
- AI Literacy was supported as a higher-order factor with “Use & Apply AI,” “Know & Understand AI,” “Detect AI,” and “AI Ethics”; “Create AI” did not load on AI Literacy and correlated with it at r = .49.
- Instead of one AI Self-management factor, the study found two domains: “AI Self-efficacy” (AI Learning and AI Problem solving) and “AI Self-competency” (AI Persuasion literacy and AI Emotion regulation).
- In the extended model including attitudes and willingness to use technology, fit was acceptable: χ²(2035) = 3004.35, p < .001, CFI = .900, TLI = .894, RMSEA = .043, 95% CI [.039, .047], SRMR = .069; AI Literacy correlated with AI Self-competency at .86 and AI Self-efficacy at .93.
Methods
- Online survey of 300 German-speaking adults recruited via Prolific on 3–4 November 2022; mean age 32.13 years (SD = 11.66, range 18–72), 48.33% female, 50.67% male, 1.00% diverse.
- Items were generated from AI literacy literature and psychological competency concepts: 72 AI literacy/self-assessment items plus 12 AI self-management items, with 68 items retained for analysis after review.
- Confirmatory factor analysis was conducted in R with lavaan (version 0.6-12) using robust Satorra-Bentler estimation; the model was modified after poor initial fit by removing low-loading items, moving three Know & Understand AI items to Detect AI, separating Create AI, and splitting AI Self-management into AI Self-efficacy and AI Self-competency.
Limitations
- The paper states the sample was online and specific to German-speaking individuals living in Germany or Austria, and that no already validated AI literacy instrument was available to validate the questionnaire against.
- The authors state that because they made changes to the model after testing it, their approach should be considered exploratory, and future research should test the factorial structure in independent samples.
- The paper states the most important next step is validation with external criteria such as tests, tasks, expert evaluations, group differences, intervention change, or future validated questionnaires; the text also makes evident that the findings rely on self-report and cross-sectional correlational data rather than behavioral outcomes.
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Claims the paper makes
32 Own result · 22 Own interpretation · 8 Sources not found · 6 Own method · 4 No full text of the sources · 2 Partly supported · 1 Supported so far · 1 Mixed support · 1 Not supported · 1 Couldn’t confirm · 1 Not checked yet · 1 No basis given · accuracy not yet measured
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In the paper
“This is in line with most conceptualisations of AI literacy where the development of AI is not explicitly mentioned [13, 23, 29]”
What the sources say
- Supportedcites [13] Chai, 2020, An extended theory of planned behavior for the modelling of Chinese secondary school students' intention to learn artificial intelligence, Mathematics source
The Instruments section defines this paper’s AI-literacy measure as perceived knowledge and skills in using AI technology. Its six AI-literacy items (Table 1) concern understanding how AI works and is used; none explicitly mentions developing AI. The paper therefore supports the claim as an example of such a conceptualisation, though it does not itself establish what most conceptualisations include. (LLM, conf=0.86)
- Could not verifycites [23] Kim, 2021, Why and what to teach: Ai curriculum for elementary school sourceExperimental — accuracy not yet measured
The Existing Guidelines for K-12 AI Education section discusses AI-literacy competencies but does not assess whether most conceptualisations omit AI development. The AI Knowledge section includes programming simple AI applications in this paper’s own framework; that example does not establish what most conceptualisations say. No page numbers are available in our copy. (LLM, conf=0.72)
- Not checked — no full text of the sourcecites [29] Latikka, 2019, Self-efficacy and acceptance of robots, Computers in Human Behavior source
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In the paper
“Outside the theory of the planned behavior-framework, it was also shown that the subjective assessment of one's own competencies is central to the intention to use AI [24, 26].”
What the sources say
- Not checked yetcites [24] Kim, 2022, The artificial intelligence literacy scale for middle school students, Journal of the Korea Society of Computer and Information
- Partly supportedcites [26] Kong, 2021, Evaluation of an artificial intelligence literacy course for university students with diverse study backgrounds, Computers and Education: Artificial Intelligence sourceExperimental — accuracy not yet measured
Section 2.3, “Empowerment through AI” (page unavailable), defines AI self-efficacy as a person's assessment of their ability and says those with higher self-efficacy are more likely to begin or continue an AI-related task. This supports a connection between perceived competence and engagement, but the paper does not establish that perceived competence is central to an intention to use AI generally. (LLM, conf=0.82)
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The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“For their measurement instrument on AI literacy, Wang and colleagues define AI literacy to have the components awareness , usage , evaluation , and ethics but do not include the ability to develop AI applications as part of their conceptualisation [45].”
What the sources say
- Could not verifycites [45] Sniehotta, 2014, Time to retire the theory of planned behaviour, Health Psychology Review sourceExperimental — accuracy not yet measured
The paper’s Criticism and Concerns about validity sections discuss the theory of planned behaviour, not Wang and colleagues’ AI literacy scale. Searches for the scale’s proposed components and the ability to develop AI applications found no passage addressing either point. (LLM, conf=0.78)
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“we found the facets”
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“It did not correlate with AI Self-efficacy and only negatively with AI Literacy and AI Self-competency.”
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“There are high correlations between the constructs of our questionnaire (i.e.,”
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“A clear correlation emerged for”
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“All superordinate dimensions positively correlate with a positive attitude towards AI.”
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“All AI competencies correlated positively with the perceived control over new technology and acceptance of new technology except Create AI, which did not correlate with perceived control over new technology.”
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“It did not correlate with AI Self-efficacy and only negatively with AI Literacy and AI Self-competency.”
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“Self-assessed competence in the use of new technologies correlated positively only with Create AI.”
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“All AI competencies correlated positively with the perceived control over new technology and acceptance of new technology except Create AI, which did not correlate with perceived control over new technology.”
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“A negative attitude was negatively correlated to AI Literacy and AI Self-competency but was not correlated to Create AI and AI Self-efficacy.”
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“In a similar way, the three dimensions of the willingness to use technology are positively related to most of the dimensions of our questionnaire.”
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“Additionally, we found a common factor for the ability to detect AI similar to the dimensions”
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“Interestingly, the facet”
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“A negative attitude was negatively correlated to AI Literacy and AI Self-competency but was not correlated to Create AI and AI Self-efficacy.”
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“In place of one domain called”
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“A negative attitude was negatively correlated to AI Literacy and AI Self-competency but was not correlated to Create AI and AI Self-efficacy.”
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“However, as expected, we found the other factors with the expected items to load on the superordinate factor”
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In the paper
“A negative attitude was negatively correlated to AI Literacy and AI Self-competency but was not correlated to Create AI and AI Self-efficacy.”
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“A clear correlation emerged for”
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“A positive attitude towards AI was positively correlated with all AI competencies.”
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“Interestingly, the facet”
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“It did not correlate with AI Self-efficacy and only negatively with AI Literacy and AI Self-competency.”
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“All AI competencies correlated positively with the perceived control over new technology and acceptance of new technology except Create AI, which did not correlate with perceived control over new technology.”
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“Interestingly, items for the evaluation of AI did not load on one factor with items on”
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“We mainly found positive relations for our questionnaires' dimensions with attitudes towards AI and willingness to use technology.”
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“Instead of finding one superordinate factor for the psychological competencies related to AI, we found two (i.e.,”
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“While the items on the evaluation of AI loaded on”
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“However, enough uniqueness exists in both constructs.”
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In the paper
“This study contributes to the research on AI literacy by providing a measurement instrument relying on profound competency models.”
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“Rather, our research suggests that”
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In the paper
“According to the theory of planned behaviour [1] or similar theories (i.e., the social cognitive theory or extensions to the UTAUT2 [42] by different authors [2, 17, 25]), the perceived capabilities regarding a certain behavior (called perceived control or self-efficacy) are central to the intention of showing or changing a behavior.”
What the sources say
- Not checked — no full text of the sourcecites [1] Ajzen, 1985, From intentions to actions: A theory of planned behavior, Action control source
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In the paper
“In the further course of the use of AI, the control of action and emotion processes is necessary for the successful long-term and sustainable use of AI [18].”
What the sources say
- Not checked — no full text of the sourcecites [18] Gao, 2015, The adoption of smartphones among older adults in China source
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In the paper
“We suggest that other psychological competencies are central to mitigating negative effects on perceived behavioural control and thus the behavioral intention and actual use of AI.”
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- Rests on an assumption: New developments in AI might unsettle potential users, lead to failed behavior, and reduce perceived behavioral control.
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
“Therefore, we base our measurement instrument on the established competence taxonomies of [7] and [33].”
What the sources say
- Not matchedcites [7] van Beuningen, 2014
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [33] Maslej, 2023
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In the paper
“It seems that the ability to evaluate AI is more closely related to the general knowledge and understanding of AI than to the ability to develop AI.”
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In the paper
“Moreover, there is no measurement instrument that takes into account psychological meta-competencies [47].”
What the sources say
- Not checked — no full text of the sourcecites [47] Venkatesh, 2012, Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology, MIS Quarterly source
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“All three scales showed good internal consistencies (all 𝛼𝛼 > .81).”
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In the paper
“however, is in conflict with the conceptualisation by Ng and colleagues [33].”
What the sources say
- Not matchedcites [33] Maslej, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“Our findings partly contradict the conceptualisation by Ng and colleagues [33].”
What the sources say
- Not matchedcites [33] Maslej, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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“Presumably, according to the subjects' self-assessment, for the evaluation of AI systems, precise knowledge and understanding (i.e.,”
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“In our sample, both subscales showed high internal consistencies ( 𝛼𝛼 = .88 for positive and 𝛼𝛼 = .82 for negative attitude).”
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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
“This result from our measurement model can be seen as support for the conceptualisations which do not include the creation of AI as a part (or dimension) of AI literacy.”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“meets psychometric requirements”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“There are high correlations between the constructs of our questionnaire (i.e.,”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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 the items on the evaluation of AI loaded on”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Also, it was not possible to use an already existing and validated instrument on AI literacy to validate our questionnaire, as would be the gold standard for scale development.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 16
- Probably not a substantial claim. 17
- The quote only partly states this claim. 2
At least one cited source doesn’t support this claim or points to a different paper, and none confirmed it — see the verdicts below.
In the paper
“So far, there is some conceptual thinking to include such aspects [8-10, 13, 47], but no measurement tool yet.”
What the sources say
- Not matchedcites [8] Bloom, 1956
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites [9] Carolus, 2022, Digital interaction literacy model – conceptualizing competencies for literate interactions with voice-based ai systems, Computers and Education: Artificial Intelligence source
- Not matchedcites [10] Carolus, 2021, Towards a holistic approach and measurement of humans interacting with speech-based technology
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Disputedcites [13] Chai, 2020, An extended theory of planned behavior for the modelling of Chinese secondary school students' intention to learn artificial intelligence, Mathematics sourceExperimental — accuracy not yet measured
Section 5 (Discussion and Conclusions) says the paper provides a theory-driven, validated survey with psychometric measures for nine factors related to learning AI. Section 3.2 (Instruments) describes its 41-item survey. For the aspects this source measures, the statement that there is 'no measurement tool yet' is contrary to the paper. (LLM, conf=0.86)
Checked by a reasoning model — may be wrong.
- Not checked — no full text of the sourcecites [47] Venkatesh, 2012, Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology, MIS Quarterly source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
Some cited sources support this claim and some don’t — read each source’s verdict below.
In the paper
“This fits in with the general picture that Create AI is separate [13, 23, 29] and no part of AI Literacy.”
What the sources say
- Partly supportedcites [13] Chai, 2020, An extended theory of planned behavior for the modelling of Chinese secondary school students' intention to learn artificial intelligence, Mathematics sourceExperimental — accuracy not yet measured
The Instruments section (3.2; page unavailable) defines the study’s AI-literacy measure as perceived knowledge and skills in using AI technology, and the Table 1 items concern understanding AI processes rather than creating AI. This supports a distinction in this study’s measure, but the paper does not establish the broader claim that creating AI is categorically separate from, and never part of, AI literacy. (LLM, conf=0.78)
- Disputedcites [23] Kim, 2021, Why and what to teach: Ai curriculum for elementary school sourceExperimental — accuracy not yet measured
The AI Knowledge section places programming simple AI applications within the AI Skill competency. The AI Literacy and Competencies of the Curriculum section says AI Skill is one of three competencies needed to attain AI literacy. The paper therefore treats creating AI applications as part of AI literacy, not separate from it. (LLM, conf=0.89)
Checked by a reasoning model — may be wrong.
- Not checked — no full text of the sourcecites [29] Latikka, 2019, Self-efficacy and acceptance of robots, Computers in Human Behavior source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Learning has been considered as an important part of or addition for AI literacy before [8, 10, 13].”
What the sources say
- Not matchedcites [8] Bloom, 1956
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites [10] Carolus, 2021, Towards a holistic approach and measurement of humans interacting with speech-based technology source
- Partly supportedcites [13] Chai, 2020, An extended theory of planned behavior for the modelling of Chinese secondary school students' intention to learn artificial intelligence, Mathematics sourceExperimental — accuracy not yet measured
The Introduction calls learning AI an important topic, and §3.2 defines AI literacy in terms of students’ perceived knowledge and skills. But §2.1 treats AI literacy as a background factor for students’ intention to learn AI; it does not explicitly describe learning as a component of, or addition to, AI literacy. The indexed passages provide no page numbers. (LLM, conf=0.82)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
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
“Interestingly, AI Self-efficacy and AI Self-competency showed very high correlations with AI Literacy, whereas they were still highly correlated with each other, but to a lesser extent.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
“Interestingly, items for the evaluation of AI did not load on one factor with items on”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
In the paper
“Overall, we find the factors”
What the sources say
- Not matchedcites [33] Maslej, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
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
“The factor”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
In the paper
“Additionally, emotion regulation helps to reduce negative and increase positive emotional arousal [8] and, by that, also increases perceived behavioral control.”
What the sources say
- Not matchedcites [8] Bloom, 1956
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
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
“is embedded in the current literature on AI Literacy”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
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
“Presumably, individuals with a higher positive attitude also deal with the topic more often and are therefore more competent.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“This is in line with the conceptualisation by Long and Magerko [29], who included the ability to recognize AI in their framework.”
What the sources say
- Not checked — no full text of the sourcecites [29] Latikka, 2019, Self-efficacy and acceptance of robots, Computers in Human Behavior source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
“Possibly, recognizing AI does not seem to be necessarily tied to knowing and understanding AI, but also is an independent competence.”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“It appears that individuals with higher positive attitudes, lower negative attitudes, and more willingness to use technology are also more likely to consider themselves competent [1, 46, 48].”
What the sources say
- Not checked — no full text of the sourcecites [1] Ajzen, 1985, From intentions to actions: A theory of planned behavior, Action control source
- Not matchedcites [46] for Statistics, U. I. (n.d.).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites [48] Wang, 2022 source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Self-assessed AI competencies reflect actual AI competencies.
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
“These correlative findings suggest that there are relations between competencies related to AI and attitudes towards AI as well as the willingness to use new technology.”
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
“Possibly, problem solving and learning are competencies that are primarily aimed at managing information and information processing, while persuasion literacy and emotion regulation also focus on the management of information, albeit with greater personal value (own decisions and emotions).”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It 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
“As Ng and colleagues conclude [33], most conceptualisations of AI literacy parallel Bloom's taxonomy [7] regarding their general configuration of skills.”
What the sources say
- Not matchedcites [33] Maslej, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [7] van Beuningen, 2014
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“Ng and colleagues, in their review on AI literacy conceptualisations in education, postulate that these can be organized into four concepts: (1) know and understand AI, (2) use and apply AI, (3) evaluate and create AI, and (4) AI Ethics [33].”
What the sources say
- Not matchedcites [33] Maslej, 2023
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
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 result is a measurement instrument that measures AI literacy with the facets”
plainn = 300- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
“Compared to the previous literature, we have fully incorporated all aspects of the taxonomy, including the component creation/development of AI , which was not previously considered.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
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
“We argue that AI Self-management is important to ensure the prolonged and sustainable use of AI and, thus, consider it to be an important part of our measurement instrument.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: New developments in AI might unsettle potential users, lead to failed behavior, and reduce perceived behavioral control.
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
“So far, there are not many ways to measure AI Literacy, and many scales are bound to specific contexts (e.g., only usable in a medical setting) or are not validated [20, 46].”
What the sources say
- Not matchedcites [20] 2022
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [46] for Statistics, U. I. (n.d.).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
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
“considers psychological meta-competencies”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
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
“is cross-contextual applicable”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
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
“We argue that an instrument for measuring AI literacy with the aim of predicting and preparing the use of AI in a professional context should, from a psychological point of view, primarily focus on the subjective assessment of one's own competencies (i.e., behavioral control or self-efficacy).”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Prior to participation, the participants were informed about the general purpose of the study and gave their informed consent.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
“In addition, higher-order psychological competencies are included that are particularly important in the context of pervasive change through AI systems.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Rests on an assumption: The introduction of AI systems is often accompanied by general change processes that must be mastered constructively.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Even though we chose confirmatory factor analysis to test our models, we made changes to the model, thus, it is necessary to consider our approach exploratory.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although other instruments exist, non have been validated with external criteria so far.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Our sample was collected online and is specific to German-speaking individuals who live in Germany or Austria.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper relies on this without support.
The introduction of AI systems is often accompanied by general change processes that must be mastered constructively.
“since the introduction of AI systems is often accompanied by general change processes that must be mastered constructively.”
The paper relies on this without support.
Many prospective users of AI who are already working will not benefit from AI education in schools right now.
“However, many prospective users of AI who are already working will not benefit from AI education in schools right now.”
The paper relies on this without support.
New developments in AI might unsettle potential users, lead to failed behavior, and reduce perceived behavioral control.
“Innovations might unsettle potential users, lead to failed behavior, and thus reduce perceived behavioral control.”
The paper relies on this without support.
Self-assessed AI competencies reflect actual AI competencies.
“It appears that individuals with higher positive attitudes, lower negative attitudes, and more willingness to use technology are also more likely to consider themselves competent [1, 46, 48].”
The paper relies on this without support.
The factorial structure identified in this sample is valid in the population.
“Future research should aim to test the factorial structure we identified in independent samples.”
The paper relies on this without support.
The items based on the existing literature adequately cover AI literacy.
“We based our developed items on the existing literature on AI literacy and psychological competencies.”
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 second sample (N = 219) also confirmed the measurement model of the MAILS as it showed a good model fit (χ2(513) = 781.49, p < .001, CFI = .95, RMSEA = .05, 95 %-CI [.05, .06], SRMR = .06).”
From Meta AI literacy scale: Further validation and development of a short version (2024) · cites this paper as [10]
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
“In the first study by Carolus, Koch, and colleagues [10], a factorial structure for the MAILS was confirmed.”
From Meta AI literacy scale: Further validation and development of a short version (2024) · cites this paper as [10]
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 MAILS includes 34 items, 18 items focusing on AI literacy, four focusing on the ability to create AI, and 6 each focusing on AI self-efficacy and AI self-competency when dealing with AI.”
From Meta AI literacy scale: Further validation and development of a short version (2024) · cites this paper as [10]
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
“Moreover, positive correlations of the MAILS with related constructs as postulated in axiom 1 c in German- and English-speaking samples have been shown [10]:”
From Meta AI literacy scale: Further validation and development of a short version (2024) · cites this paper as [10]
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
“Interestingly, the ability to create AI was not correlated with control over new technology and negatively correlated with competence when dealing with new technologies.”
From Meta AI literacy scale: Further validation and development of a short version (2024) · cites this paper as [10]
Experimental — accuracy not yet measured
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
“Beginning with axiom 1a (factorial structure), confirmatory analyses of two independent samples had confirmed the factorial structure of the MAILS already ([10]; Koch et al., sub.).”
From Meta AI literacy scale: Further validation and development of a short version (2024) · cites this paper as [10]
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
“AI literacy, AI self-competency, and AI self-efficacy correlated significantly positive with most of the factors of openness to technology (except AI self-efficacy and competence when dealing with new technology) and positive attitude towards AI.”
From Meta AI literacy scale: Further validation and development of a short version (2024) · cites this paper as [10]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“For reasons of evaluation efficiency, it was not possible to examine how respondents would have responded to other adapted questionnaires (e.g., [23-25]).”
From Evaluating AI Courses: A Valid and Reliable Instrument for Assessing Artificial-Intelligence Learning through Comparative Self-Assessment (2023) · cites this paper as [24]
The cited source says what the claim attributes to it.
In the citing paper
“AI literacy encompasses not only technical knowledge but also the ability to critically evaluate the ethical implications and societal impacts of AI systems (Carolus et al. 2023;Laupichler et al. 2023;Long and Magerko 2020;Ng et al. 2024;Touretzky et al. 2019;Wang et al. 2023).”
From Fostering AI Literacy in Undergraduates: A ChatGPT Workshop Case Study (2025) · cites this paper as Carolus et al. 2023
The cited source says what the claim attributes to it.
In the citing paper
“However, empirical data in an initial factor-analytical review indicate a slightly different structure of the items as described in the following paragraph [10].”
From Meta AI literacy scale: Further validation and development of a short version (2024) · cites this paper as [10]
The cited source says what the claim attributes to it.
In the citing paper
“In contrast to the original concept of Ng et al. [3], structural equation modelling revealed that the ability to create AI was not a subdimension of AI literacy but only a correlate.”
From Meta AI literacy scale: Further validation and development of a short version (2024) · cites this paper as [10]
The cited source says what the claim attributes to it.
In the citing paper
“The Meta AI Literacy Scale (MAILS; [10]) is a scale for measuring AI literacy and other psychological competencies (problem-solving, learning, self-efficacy, persuasion literacy) that are assumed to support successful interaction with AI [[17], [18], [19]].”
From Meta AI literacy scale: Further validation and development of a short version (2024) · cites this paper as [10]
The cited source says what the claim attributes to it.
In the citing paper
“To summarize, unlike other available measures of AI literacy, the MAILS includes the ability to create AI as a related construct and also measures psychological competencies that are expected to be helpful or needed for the prolonged use of AI.”
From Meta AI literacy scale: Further validation and development of a short version (2024) · cites this paper as [10]
The cited source says what the claim attributes to it.
In the citing paper
“Further, the ability to create AI and AI self-efficacy were not correlated with negative attitudes toward AI.”
From Meta AI literacy scale: Further validation and development of a short version (2024) · cites this paper as [10]
The cited source says what the claim attributes to it.
In the citing paper
“Prior analyses supported axiom 1c as correlations of the MAILS with attitude towards AI and openness to technology were found [10].”
From Meta AI literacy scale: Further validation and development of a short version (2024) · cites this paper as [10]
The cited source says what the claim attributes to it.
In the citing paper
“The prior findings regarding negative attitudes toward AI were mixed [10].”
From Meta AI literacy scale: Further validation and development of a short version (2024) · cites this paper as [10]
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.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
“| | Competence | 4.83 | 0.82 | 4.29 | 1.00 | 5.00 |”
Reported 4.83 · recomputed 4.645
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ 4.1 Limitations and Future Work · p. 10
“Our sample was collected online and is specific to German-speaking individuals who live in Germany or Austria.”
§ 3.1 Sample · p. 6
“Most participants lived in Germany (77.00%) or Austria (7.00%).”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ MAILS - Meta AI Literacy Scale: Development and Testing of an AI Literacy Questionnaire Based on Well-Founded Competency Models and Psychological Change- and Meta-Competencies · p. 1
“We derived 60 items to represent different facets of AI Literacy according to Ng and colleagues' conceptualisation of AI literacy [33] and additional 12 items to represent psychological competencies such as problem solving, learning, and emotion regulation in regard to AI.”
§ 3.2 Measures · p. 6
“we generated 72 items for the self-assessments in different domains of AI literacy. We focused on the four superordinate domains described by Ng and colleagues [33], namely "Know & understand AI", "Use & apply AI", "Evaluate & create AI", and "AI ethics". In addition to the items aiming at the assessment of the domains of AI literacy, we generated 12 items”
Citations
Cites
Principles and Practice of Structural Equation Modeling
Tabri, N., & Elliott, C. M. (2012) · Canadian Graduate Journal of Sociology and Criminology · doi:10.15353/cgjsc.v1i1.3787 · Cited by 39,094
lavaan : An R Package for Structural Equation Modeling
Rosseel, Y. (2012) · Journal of Statistical Software · doi:10.18637/jss.v048.i02 · Cited by 27,955
Self-efficacy: the exercise of control
(1997) · Choice Reviews Online · doi:10.5860/choice.35-1826 · Cited by 24,451
From Intentions to Actions: A Theory of Planned Behavior
Ajzen, I. (1985) · Action Control · doi:10.1007/978-3-642-69746-3_2 · Cited by 19,057
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012) · MIS Quarterly · doi:10.2307/41410412 · Cited by 15,756
The future of employment: How susceptible are jobs to computerisation?
Frey, C. B., & Osborne, M. A. (2017) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2016.08.019 · Cited by 8,727
Toward an Emotion Nuanced Sensemaking Perspective of Organizational Consultation
Lundberg, C. C. (2000) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-60752-837-120251015 · Cited by 3,340
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
Dawes, J. G. (2008) · International Journal of Market Research · doi:10.1177/147078530805000106 · Cited by 2,361
Taxonomy of Educational Objectives, Handbook I: Cognitive Domain
Kridel, C. (2010) · Encyclopedia of Curriculum Studies · doi:10.4135/9781412958806.n446 · Cited by 2,317
Research works printed but not linked: 3
- Bandura, 2006, Guide for constructing self-efficacy scales, Self-efficacy beliefs of adolescents, 5, 307
- Kim, 2022, The artificial intelligence literacy scale for middle school students, Journal of the Korea Society of Computer and Information, 27, 225
- Polanco-Levicán, 2022, Digital interaction literacy model – conceptualizing competencies for literate interactions, International Journal of Environmental Research and Public Health, 19, 1
Cited by
Sorted by citations
Wang, C., Wang, H., Li, Y., Dai, J., Gu, X., & Yu, T. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2383033 · Cited by 468
Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu, S. K. W. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13411 · Cited by 390
Almatrafi, O., Johri, A., & Lee, H. (2024) · Computers and Education Open · doi:10.1016/j.caeo.2024.100173 · Cited by 341
A systematic review of AI literacy scales
Lintner, T. (2024) · npj Science of Learning · doi:10.1038/s41539-024-00264-4 · Cited by 292
Schiavo, G., Businaro, S., & Zancanaro, M. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102537 · Cited by 284
Metadata
- Authors
- Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C.
- Affiliations
- University of Würzburg (DE)
- Corresponding author
- Koch, Martin J.
- Venue
- Computers in Human Behavior: Artificial Humans
- Publisher
- Elsevier BV
- Published
- 2023-08
- Volume
- 1
- Issue
- 2
- Pages
- 100014
- ISSN
- 2949-8821 (ISSN-L)
- OpenAlex
- W4387368391
- Cited by
- 488
- References
- 47
- Citations by year
- 2026: 274 · 2025: 160 · 2024: 47 · 2023: 7
- FWCI
- 88.15
- Type
- journal article
- Language
- en
- Update notices
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers in Human Behavior Artificial Humans (publishedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Bundesministerium für Arbeit und Soziales (DKI.00.00030.21)
- 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
- AI literacy, questionnaire development, scale validation, psychological competence, AI self-efficacy, emotion regulation, AI ethics, problem solving, competency model
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 2026-11-05, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-11-05 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
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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