MAILS - Meta AI Literacy Scale
Original publication
Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). 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. Computers in Human Behavior: Artificial Humans, 1(2), 100014. https://doi.org/10.1016/j.chbah.2023.100014
The instrument at a glance
The MAILS is a scientifically developed and psychometrically evaluated self-report questionnaire for assessing AI literacy and additional psychological competencies involved in dealing with artificial intelligence.
The scale can be used in full or in a modular form. It measures subjectively perceived abilities and is not an objective knowledge test.
34 items in nine facets
Response format: 0 to 10
Languages: German and English
Use: full or modular
Full item list
Es folgen die Original-Items. Diese direkt für wissenschaftliche Untersuchungen übernommen werden.
Instruction: In the following, you will read descriptions of different abilities that one can have when dealing with artificial intelligence. These abilities can be more or less pronounced. Please rate yourself: How pronounced are your abilities?
Answering Scale: A value of 0 means that an ability is not at all or hardly pronounced.
A value of 10 means that an ability is very well or (almost) perfectly pronounced.
Auswertung: Please circle the value that best describes your ability.
AI LITERACY
Use & Apply AI
1. I can operate AI applications.
2. I can use AI applications to make my life easier.
3. I can use artificial intelligence meaningfully to achieve my goals.
4. I can interact with AI in a way that makes my tasks easier.
5. I can work together gainfully with an artificial intelligence.
6. I can communicate gainfully with artificial intelligence.
Know & Understand AI
7. I know the most important concepts of the topic “artificial intelligence”.
8. I know definitions of artificial intelligence.
9. I can assess what the limitations and opportunities of using an AI are.
10. I can assess what advantages and disadvantages the use of an artificial intelligence entails.
11. I can think of new uses for AI.
12. I can imagine possible future uses of AI.
Detect AI
13. I can tell if I am dealing with an application based on artificial intelligence.
14. I can distinguish devices that use AI from devices that do not.
15. I can distinguish if I interact with an AI or a “real human”.
AI Ethics
16. I can weigh the consequences of using AI for society.
17. I can incorporate ethical considerations when deciding whether to use data provided by an AI.
18. I can analyze AI-based applications for their ethical implications.
Create AI
19. I can design new AI applications.
20. I can program new applications in the field of “artificial intelligence”.
21. I can develop new AI applications.
22. I can select useful tools (e.g., frameworks, programming languages) to program an AI.
AI SELF-EFFICACY
AI Problem-Solving
23. I can rely on my skills in difficult situations when using AI.
24. I can handle most problems in dealing with artificial intelligence well on my own.
25. I can also usually solve strenuous and complicated tasks when working with artificial intelligence well.
Learning
26. Despite the rapid changes in the field of artificial intelligence, I can always keep up to date.
27. I can keep up with the latest innovations in AI applications.
28. Although there are often new AI applications, I manage to always be “up to date”.
AI SELF-COMPETENCY
Persuasion Literacy
29. I don’t let AI influence me in my decisions.
30. I can prevent an AI from influencing me in my decisions.
31. I realise if artificial intelligence is influencing me in my decisions.
Emotion Regulation
32. I keep control over feelings like frustration and anxiety while doing things with AI.
33. I can handle it when interactions with AI frustrate or frighten me.
34. I can control my euphoria that arises when I use artificial intelligence for different purposes.
Further publications
Koch, M. J., Carolus, A., Wienrich, C., & Latoschik, M. E. (2024). Meta AI Literacy Scale: Further validation and development of a short version. Heliyon, 10(21), e39686. https://doi.org/10.1016/j.heliyon.2024.e39686
This study provides additional validity evidence and develops an economical ten-item short version.
Koch, M. J., Wienrich, C., Straka, S., Latoschik, M. E., & Carolus, A. (2024). Overview and confirmatory and exploratory factor analysis of AI literacy scale. Computers and Education: Artificial Intelligence, 7, 100310.
The publication examines the factorial structure of the MAILS in an English-speaking sample and situates the instrument within existing research on the measurement of AI literacy.
MAILS short version
The short version consists of ten items and represents all nine facets of the MAILS. It is particularly suitable for studies in which AI literacy is not the central variable or in which assessment time is limited. Where feasible, the authors continue to recommend the full MAILS because it permits more detailed analyses of the individual facets.
Current evidence: The short version showed good model fit in the development study. Its validity should be examined in further independent studies.
FULL ITEM LIST
I can tell if I am dealing with an application based on artificial intelligence.
I can program new applications in the field of “artificial intelligence”.
Although there are often new AI applications, I manage to always be “up to date”.
I can handle it when interactions with AI frustrate or frighten me.
I can weigh the consequences of using AI for society.
I can design new AI applications.
I can use artificial intelligence meaningfully to achieve my goals.
I can also usually solve strenuous and complicated tasks when working with artificial intelligence well.
I can prevent an AI from influencing me in my decisions.
I can assess what advantages and disadvantages the use of an artificial intelligence entails.
Other language versions
Researchers around the world are working on translations and adaptations of the MAILS. Validated language versions and further information will be added here in the future.
Terms of use and citation
The MAILS may be used free of charge for scientific research.
Individual permission is not required, provided that the relevant original publication is cited correctly and in full.
Please observe the following when using the scale:
- State whether you used the full scale, the short version, or individual facets.
- Retain the original item wording and response scale when using the original version.
- Clearly identify and document any translations, shortening, or substantive adaptations.
- Cite Carolus et al. (2023) for the full scale.
- When using the ten-item short version, also cite Koch et al. (2024).
Citation for the full scale
Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). 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. Computers in Human Behavior: Artificial Humans, 1(2), 100014. https://doi.org/10.1016/j.chbah.2023.100014
Additional citation for the short version
Koch, M. J., Carolus, A., Wienrich, C., & Latoschik, M. E. (2024). Meta AI literacy scale: Further validation and development of a short version. Heliyon, 10(21), e39686. https://doi.org/10.1016/j.heliyon.2024.e39686
