# 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

arXiv: https://arxiv.org/abs/2302.09319 · HTML: https://mlsanity.com/paper/2302.09319/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 58 references that are in the map.
- First posted on arXiv 18 Feb 2023.

## Builds on (most cited first, 2 of 2)

- [The AI Index 2022 Annual Report](https://mlsanity.com/paper/2205.03468/) (2022)
- [eXtended Artificial Intelligence: New Prospects of Human-AI Interaction Research](https://mlsanity.com/paper/2103.15004/) (2021)

## Built on by (most cited first, 17 of 17)

- ["AI enhances our performance, I have no doubt this one will do the same": The Placebo effect is robust to negative descriptions of AI](https://mlsanity.com/paper/2309.16606/) (2023)
- [The AI Literacy Heptagon: A Structured Approach to AI Literacy in Higher Education](https://mlsanity.com/paper/2509.18900/) (2025)
- [More Human or More AI? Visualizing Human-AI Collaboration Disclosures in Journalistic News Production](https://mlsanity.com/paper/2601.11072/) (2026)
- [Trust and Reliance on AI in Education: AI Literacy and Need for Cognition as Moderators](https://mlsanity.com/paper/2604.01114/) (2026)
- ["We need to avail ourselves of GenAI to enhance knowledge distribution": Empowering Older Adults through GenAI Literacy](https://mlsanity.com/paper/2506.06225/) (2025)
- [How to Assess AI Literacy: Misalignment Between Self-Reported and Objective-Based Measures](https://mlsanity.com/paper/2601.06101/) (2026)
- ["If You're Very Clever, No One Knows You've Used It": The Social Dynamics of Developing Generative AI Literacy in the Workplace](https://mlsanity.com/paper/2602.01386/) (2026)
- [AI Literacy as a Key Driver of User Experience in AI-Powered Assessment: Insights from Socratic Mind](https://mlsanity.com/paper/2507.21654/) (2025)
- [Explained, yet misunderstood: How AI Literacy shapes HR Managers' interpretation of User Interfaces in Recruiting Recommender Systems](https://mlsanity.com/paper/2509.06475/) (2025)
- [Expanding AI Awareness Through Everyday Interactions with AI: A Reflective Journal Study](https://mlsanity.com/paper/2410.18845/) (2024)
- [The World of AI: A Novel Approach to AI Literacy for First-year Engineering Students](https://mlsanity.com/paper/2506.08041/) (2025)
- [Thinking Like a Scientist: Can Interactive Simulations Foster Critical AI Literacy?](https://mlsanity.com/paper/2507.21090/) (2025)
- [From SERPs to Sound: How Search Engine Result Pages and AI-generated Podcasts Interact to Influence User Attitudes on Controversial Topics](https://mlsanity.com/paper/2601.11282/) (2026)
- [Train Yourself as an LLM: Exploring Effects of AI Literacy on Persuasion via Role-playing LLM Training](https://mlsanity.com/paper/2604.02637/) (2026)
- [The Pedagogy of AI Mistakes: Fostering Higher-Order Thinking](https://mlsanity.com/paper/2605.05472/) (2026)
- [A Survey Instrument to Assess Students' AI and Generative AI Knowledge](https://mlsanity.com/paper/2608.21391/) (2026)
- [Beyond AI Literacy: A Structured Review and Exploratory Meta-Analysis of Measures for Competent Generative-AI Use](https://mlsanity.com/paper/2609.15624/) (2026)

## Abstract (arXiv)

The goal of the present paper is to develop and validate a questionnaire to assess AI literacy. In particular, the questionnaire should be deeply grounded in the existing literature on AI literacy, should be modular (i.e., including different facets that can be used independently of each other) to be flexibly applicable in professional life depending on the goals and use cases, and should meet psychological requirements and thus includes further psychological competencies in addition to the typical facets of AIL. We derived 60 items to represent different facets of AI Literacy according to Ng and colleagues conceptualisation of AI literacy and additional 12 items to represent psychological competencies such as problem solving, learning, and emotion regulation in regard to AI. For this purpose, data were collected online from 300 German-speaking adults. The items were tested for factorial structure in confirmatory factor analyses. The result is a measurement instrument that measures 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. This study contributes to the research on AI literacy by providing a measurement instrument relying on profound competency models. In addition, higher-order psychological competencies are included that are particularly important in the context of pervasive change through AI systems.