Data and AI literacy for everyone
Open accessAbstract
The framework for data and AI literacy proposed in this contribution aims to create a common understanding of data and AI literacy so that it can be systematically included into curricula and educational standards of schools, teacher training, higher education, and further education. The purpose of the framework is to facilitate the respective set of skills and competencies to be taught as a transdisciplinary competence across all subjects from three perspectives: the application-oriented, the technical-methodological, and the socio-cultural perspective. The framework provides a pathway to establishing basic data literacy for everyone, to enable 21st Century citizens to deal with data and AI in a conscious and ethically sound manner. The framework can also serve as a reference for data and AI literacy programs for extracurricular and vocational training, to enable lifelong learning of data and AI literacy. Therefore, it can be a useful template for public statistical institutions planning to offer data literacy trainings for the public, responding to the challenges resulting from the changing roles and expectations of actors in a data-driven society. A detailed example for such an application, the app “Stadt | Land | Datenfluss”, demonstrates the benefit of the framework for the purpose of adult education.
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Data Literacy forms a foundation on which AI literacy can develop.
“As Schüller (2022) argues in their conceptualization of Data Literacy, the ability to understand, interpret, and work with data forms a foundation on which AIL can develop.”
From The AI literacy heptagon: A structured approach to AI literacy in higher education (2026) · cites this paper as Schüller, 2022
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In search of artificial intelligence (AI) literacy in teacher education: A scoping review
Sperling, K., Stenberg, C.-J., McGrath, C., Åkerfeldt, A., Heintz, F., & Stenliden, L. (2024) · Computers and Education Open · doi:10.1016/j.caeo.2024.100169 · Cited by 498 · see citing works
Liu, M., Zhang, L. J., & Zhang, D. (2025) · Computers in Human Behavior · doi:10.1016/j.chb.2025.108569 · Cited by 43 · see citing works
The AI literacy heptagon: A structured approach to AI literacy in higher education
Hackl, V., Müller, A. E., & Sailer, M. (2026) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2026.100540 · Cited by 41 · see citing works
“As Schüller (2022) argues in their conceptualization of Data Literacy, the ability to understand, interpret, and work with data forms a foundation on which AIL can develop.”
Soto-Sanfiel, M. T., Angulo-Brunet, A., & Lutz, C. (2024) · doi:10.31235/osf.io/bvyku · Cited by 20 · see citing works
Reframing AI Literacy in Higher Education: Designing Curricula for Ethically Competent Managers
Confetto, M. G., Covucci, C., Manta, O., & Califri, C. (2026) · Journal of Management Education · doi:10.1177/10525629261468213
- Authors
- Schüller, K.
- Affiliations
- STAT-UP Statistical Consulting & Data Science GmbH, Augustenstr. 5, 80333 Munich, Germany
- Corresponding author
- Schüller, Katharina
- ORCID
- Schüller, Katharina
- Venue
- Statistical Journal of the IAOS
- Publisher
- SAGE Publications
- Published
- 2022-06-07
- Volume
- 38
- Issue
- 2
- Pages
- 477-490
- ISSN
- 1874-7655 (ISSN-L)
- 1875-9254
- OpenAlex
- W4224569182
- Cited by
- 65
- References
- 0
- Citations by year
- 2026: 26 · 2025: 27 · 2024: 10 · 2023: 1 · 2022: 1
- FWCI
- 8.32
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Statistical Journal of the IAOS (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Digital literacy in education
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Social Sciences › Arts and Humanities › Literature and Literary Theory › Literacy, Media, and Education
- Keywords
- data literacy, AI literacy, literacy framework, transdisciplinary competence, curriculum development, teacher professional development, lifelong learning, adult education, data ethics, data-driven society, extracurricular training, vocational education, 21st century skills
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