Data and AI literacy for everyone

Abstract

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.

Unraveling generative AI in BBC News: application, impact, literacy and governance

“…A qualitative content analysis (Schreier, 2012) was conducted based on a sample of news articles. The texts of the news articles were segmented into every sentence as a unit for analysis.…”

The Scale of Artificial Intelligence Literacy for all (SAIL4ALL): A Tool for Assessing Knowledge on Artificial Intelligence in All Adult Populations and Settings

“…In this paper we will focus on AI literacy, for which a universally accepted definition has yet to be developed (Laupichler et al, 2023a;Schüller, 2022). Existing definitions vary, among others, in terms of the precise quantity and arrangement of competencies (Carolus et al, 2023).…”

Empowering Diverse Learners through Design-Based Data Literacy Education: the Data Design Cycle Framework

“…Drawing from the literature, it is clear that the practical skills required for data literacy are just as important as knowledge and education culture. Examples of successful practical teaching methods are discussed in the literature (Schüller, 2022;Gläser & Spree, 2022;Werning, 2020;Gehrke et al, 2021;Hitchcock et al, 2021;Cheng et al, 2019), and can be virtual or analog in nature. Building practical competence not only empowers students but also encourages their involvement in the public sphere.…”