The DOI matches its Crossref record; 50 of the 51 references it cites were found in a registry or an index, and 1 are not checked yet; 8 of the 35 sources its claims cite were checked for support.
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
8 of 35 cited sources checked · 1 supported · 2 flagged · accuracy not yet measured
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
The cited sources support only part of this claim — read each source’s verdict below.
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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe 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)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Could not verify
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
“we found the facets”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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
“It did not correlate with AI Self-efficacy and only negatively with AI Literacy and AI Self-competency.”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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
“There are high correlations between the constructs of our questionnaire (i.e.,”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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
“A clear correlation emerged for”
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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
“All superordinate dimensions positively correlate with a positive attitude towards AI.”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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
“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.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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
“It did not correlate with AI Self-efficacy and only negatively with AI Literacy and AI Self-competency.”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
- 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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)
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
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.”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe 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.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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.”
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
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.
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.”
Load-bearingStrength:hedged· 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 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
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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).”
Strength:hedged· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably 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.
Strength:plain· not yet calibratedCausal:correlational· 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
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.
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
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”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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.”
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably 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.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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).”
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably 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.”
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.
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.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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.”
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.
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.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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.”
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.
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.
Technology-competence mean is incompatible with its median and maximum
MajorWith a median of 4.29 and a maximum of 5.00, the mean cannot exceed 4.645. The reported mean of 4.83 is therefore impossible for this sample.
Reported 4.83 · recomputed 4.645
“| | Competence | 4.83 | 0.82 | 4.29 | 1.00 | 5.00 |”
Geographic limitation excludes participants reported to live elsewhere
MinorThe limitations describe the sample as German-speaking individuals living in Germany or Austria, but the sample description places only 77% in Germany and 7% in Austria. The remaining 16% lived elsewhere.
“Our sample was collected online and is specific to German-speaking individuals who live in Germany or Austria.”
“Most participants lived in Germany (77.00%) or Austria (7.00%).”
Initial AI-literacy item count differs between abstract and methods
MinorThe abstract says 60 AI-literacy items were derived, plus 12 psychological-competency items. The methods instead say 72 AI-literacy items were generated, plus 12 additional items, for a total of 84.
“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.”
“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
Flagged first, then by citations
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
Items 101–200 of 490
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Nakano, H., Takezawa, J., Matulic, F., Yang, C.-L., & Yatani, K. (2026) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3742413.3789076 · Cited by 17
Han, S., & Zhang, Y. (2025) · Journal of Research on Technology in Education · doi:10.1080/15391523.2025.2456051 · Cited by 11
Developing and validating an AI literacy scale for university students in Vietnam
Do, H. V., Bui, T. T., Hoa Tran, D., Nguyen, H. T., & Dinh, L. D. (2026) · IFLA Journal · doi:10.1177/03400352251385058 · Cited by 11
Kox, E. S., & Beretta, B. (2024) · Frontiers in artificial intelligence and applications · doi:10.3233/faia240194 · Cited by 10
Yang, H., Zhang, D., Guo, W., & He, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3512499 · Cited by 10
Sun, X., Liu, Y., Bosch, J. A., & Li, Z. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711014 · Cited by 14
Yin, Y. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1618850 · Cited by 11
Wang, Y., & Guo, R. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104588 · Cited by 12
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Chrostowski, M., & Mąkosa, P. M. (2025) · Teaching Theology & Religion · doi:10.1111/teth.70020 · Cited by 10
Ruiz, C. C., & Yambao, F. M. (2026) · International Journal of Learning Teaching and Educational Research · doi:10.26803/ijlter.25.2.29 · Cited by 10
Che, C. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1755238 · Cited by 10
Asio, J. M. R., & Suero, A. N. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4973726 · Cited by 10
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Yang, Q., Lu, G., He, X., & Zhang, C. (2024) · International Symposium on Educational Technology · doi:10.1109/iset61814.2024.00029 · Cited by 12
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Ali, M., & Mughari, S. (2024) · Information Development · doi:10.1177/02666669241299765 · Cited by 8
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Zhang, Y., & Kang, B. (2026) · BMC Psychology · doi:10.1186/s40359-026-04047-x · Cited by 9
Zhijuan, L. A. I., Zhang, T., Huang, N., Li, Y., You, T., Zhao, J., & Zheng, J. (2026) · Nurse Education Today · doi:10.1016/j.nedt.2026.107019 · Cited by 8
Balatero, L. M., Jayno, J. R., & Miranda, K. M. M. (2024) · Journal of Tertiary Education and Learning · doi:10.54536/jtel.v2i3.3365 · Cited by 10
Ding, N., Chen, M., & Hu, L. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105709 · Cited by 7
Ko, E. G., Nanayakkara, S., & Huff, E. W. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3720032 · Cited by 7
Uluğ, E., Öner, K., ARSLANTAŞ, S., & Tatli, S. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13597-3 · Cited by 7
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Döring, N. M., & Mohseni, M. R. (2025) · Archives of Sexual Behavior · doi:10.1007/s10508-025-03318-9 · Cited by 7
Culture or Industry: Chinese and American Young Journalists’ Usage and Perception of AI and Gen-AI
Liu, Y. (2025) · Journalism Practice · doi:10.1080/17512786.2025.2572965 · Cited by 7
Neufeld, K., Ohly, S., Sedefoglu, D., Steinhardt, I., & Mauermeister, S. (2025) · Career Development International · doi:10.1108/cdi-10-2024-0442 · Cited by 7
Dang, T. H. A., & Nguyen, V. T. T. (2025) · Journal of Information Technology Education Research · doi:10.28945/5689 · Cited by 7
Tarhini, A., Al-Sharafi, M. A., & Lawati, H. A. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2628249 · Cited by 7
Bećirović, S., Augustín, M., Mattoš, B., & Dančo, J. (2026) · Discover Computing · doi:10.1007/s10791-026-10042-y · Cited by 7
Kang, K. Y., & Park, J.-H. (2026) · Journal of Librarianship and Information Science · doi:10.1177/09610006261425235 · Cited by 7
Altamimi, R., & Al-Hija’a, A. A. (2026) · Scientific Reports · doi:10.1038/s41598-026-47133-1 · Cited by 8
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Zhang, S. B., Xiao, R., Botelho, A. F., Liao, G., Chiu, T. K. F., Stamper, J. C., & Koedinger, K. R. (2026) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3785022.3785088 · Cited by 7
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Dzhanegizova, A., Nurseiit, А. М., & Vyborova, К. S. (2024) · Qainar Journal of Social Science · doi:10.58732/2958-7212-2023-4-34-49 · Cited by 7
Meta Artificial Intelligence Literacy of University Students: A Comparative Analysis
Generale, C., Jaylo, A. I., Paican, E., Liporada, M. H., Vitor, A. R., & Sumatra, K. P. (2024) · International Journal of Multidisciplinary Studies in Higher Education · doi:10.70847/588571 · Cited by 7
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Hasanah, F. F., & Degeng, P. D. D. (2025) · Englisia Journal of language education and humanities · doi:10.22373/ej.v12i2.29157 · Cited by 9
Silva, L. I. M., Moreno-Pimentel, A. G., Hernández-Martín, M. M., Moreno, G., Maté‐Muñoz, J. L., & Monroy, A. M. (2025) · Teaching and learning in nursing · doi:10.1016/j.teln.2025.08.018 · Cited by 6
Malacapay, M. C. (2025) · Edelweiss Applied Science and Technology · doi:10.55214/2576-8484.v9i9.10059 · Cited by 6
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Dong, L. (2026) · Feedback Research in Second Language · doi:10.32038/frsl.2026.04.04 · Cited by 6
Su, J., Wang, Z., Qiang, Z., & Wang, Z. (2026) · Journal of Intelligence · doi:10.3390/jintelligence14080182 · Cited by 9
Pinto, P. H. R., de Araujo, V. M. U., Ferreira, C. D. S., Goulart, L. L., Aguiar, G. S., Beltrão, J. V. C., de Lira, P. D., Mendes, S. J. F., Monteiro, F. D. L. V., & Avelino, E. L. (2026) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2026.6085 · Cited by 6
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Lambrecht, G.-M., Lintfert, B., Martschiske, R., & Wiehenbrauk, D. (2025) · Zeitschrift für Hochschulentwicklung · doi:10.21240/zfhe/sh-ki-2/02 · Cited by 5
Aleksić, V., Mandić, M., & Ivanović, M. D. (2025) · Education Sciences · doi:10.3390/educsci15121582 · Cited by 5
Asio, J. M. R. (2026) · European Journal of Health and Biology Education · doi:10.29333/ejhbe/17850 · Cited by 5
Philippakos, Z. A. T. (2026) · AI in Education · doi:10.3390/aieduc2010003 · Cited by 6
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Duong, C. D. (2026) · International Journal of Innovation Science · doi:10.1108/ijis-03-2025-0143 · Cited by 5
Kesuma, D. P., & Fransen, L. A. (2025) · Jurnal Sistem Informasi Manajemen dan Teknologi Informasi · doi:10.33020/jsimtek.v3i1.811 · Cited by 5
Yang, J. T., & Li, X. (2026) · BMC Psychology · doi:10.1186/s40359-025-03949-6 · Cited by 5
Medina-Gual, L., Medina-Velázquez, L., & Parejo, J.-L. (2025) · Australasian Journal of Educational Technology · doi:10.14742/ajet.10596 · Cited by 4
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Manto, A. M., Señedo, G. E., Jauculan, F. G. E., Giangan, M. S. Q., & Alcantara, G. A. (2025) · International Journal of Learning Teaching and Educational Research · doi:10.26803/ijlter.24.7.37 · Cited by 5
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Babiker, A., Almourad, M. B., Alshakhsi, S. A. A., Liebherr, M., & Ali, R. (2026) · AI & Society · doi:10.1007/s00146-026-02929-7 · Cited by 3
Hermita, M., Rohani, A., Syah, R. D., Suhatril, R. J., & Sartika, S. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2644679 · Cited by 3
Mara, M., Bauer, L., Tschopp, M. V., Grosswieser, H., & Kraus, J. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791005 · Cited by 4
Kusters, A., Prajod, P., Cesar, P. S., & Ali, A. E. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791288 · Cited by 7
Oh, H., Lee, E., Shin, S., Lee, K., Pyun, M., & Lim, H. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791492 · Cited by 4
Klein-Avraham, I., & Baram‐Tsabari, A. (2026) · Public Understanding of Science · doi:10.1177/09636625261442388 · Cited by 4
Yauri, H. Y. M., Luyo, S. L. L., Pérez, M. D. R., Arévalo-Aranda, C. P., Guerrero, L. A. O., Reyes, C. J. S., Vásquez, A. M. M., Ballesteros, M. A. A., & Alarcón, R. (2026) · European Journal of Investigation in Health Psychology and Education · doi:10.3390/ejihpe16070105 · Cited by 3
Masalimova, A. R., ZHELTUKHINA, M. R., Demir, S., Molodozhnikova, N. M., Zvereva, E., Berechikidze, I. A., & Lazareva, Y. B. (2026) · Frontiers in Communication · doi:10.3389/fcomm.2026.1888712 · Cited by 3
Expanding AI Awareness Through Everyday Interactions with AI: A Reflective Journal Study
Hingle, A., & Johri, A. (2024) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie61694.2024.10892993 · Cited by 3
Thinking Like a Scientist: Can Interactive Simulations Foster Critical AI Literacy?
Zhao, Y., Michal, A. L., Thain, N., & Subramonyam, H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98417-4_5 · Cited by 3
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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