Mission

About Artificial Intelligence (AI)

An AI system is a machine-based system designed to operate at varying levels of autonomy and capable of generating results such as predictions, recommendations or decisions that influence physical or virtual environments (AI Act). The spectrum of AI topics covers several sub-areas, such as: problem solving and search methods, knowledge representation and reasoning, planning, uncertainty and probabilistic models, machine learning, neural networks and deep learning, natural language processing, robotics and perception, multi-agent systems and reinforcement learning (Russell & Norvig, 2021).

Sources:

European Union (2024). Regulation laying down harmonised rules on Artificial Intelligence (AI Act).

Russell, S. J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

About Digital Well-Being

The concept of digital well-being refers to the wellbeing of people in an increasingly digitalised world and aims to promote a healthy and balanced use of digital technologies (Baumann, 2024).

Digital well-being is treated as a separate strategic area of action in education policy at international and European level. In May 2025, WHO Europe published a policy brief on digital determinants of mental health in young people to support countries in formulating evidence-based policy responses to the increasing influence of digital technologies on well-being (WHO Regional Office for Europe, 2025). At European level, the EU Council adopted its own conclusions on supporting well-being in digital education in 2022 and calls on Member States to systematically integrate this aspect into their education strategies (Council of the European Union, 2022).

Sources:

Baumann, A. (2024). Digitales Wohlbefinden. Bayerisches Forschungsinstitut für Digitale Transformation (bidt). https://www.bidt.digital/glossar/digitales-wohlbefinden/

Council of the European Union (2022). EU highlights the importance of well-being in digital education. https://www.consilium.europa.eu/en/press/press-releases/2022/11/28/eu-highlights-the-importance-of-well-being-in-digital-education/

World Health Organization Regional Office for Europe (2025). Addressing the digital determinants of youth mental health and well-being: policy brief. https://www.who.int/europe/publications/i/item/WHO-EURO-2025-12187-51959-79685

About Digital Tools

Digital tools are – in the context of STEM teaching – specific digital applications and technical devices whose interactive functionality is used in a targeted manner to promote the acquisition of skills by learners and to support processes of knowledge acquisition (Roth et al, 2023).

Digital tools can help learners process information and construct knowledge from it. They enable individualised learning paths and provide immediate feedback, which makes it easier to take different learning requirements into account. Digital tools and assistive technologies demonstrate their potential particularly in the context of inclusion: they offer pupils with different abilities equal access to learning by personalising teaching methods, breaking down barriers, and promoting an accessible and equitable learning environment in which all learners can participate (Navas-Bonilla et al., 2025). At the same time, they promote collaborative working methods by facilitating communication and joint problem solving. In addition, digital tools contribute significantly to the development of key digital skills that are indispensable in a modern, technologised society and expand the didactic repertoire of teachers with new forms of teaching and learning.

Sources:

Navas-Bonilla, C. del R., Guerra-Arango, J. A., Oviedo-Guado, D. A., & Murillo-Noriega, D. E. (2025). Inclusive education through technology: A systematic review of types, tools and characteristics. Frontiers in Education, 10, 1527851. https://doi.org/10.3389/feduc.2025.1527851

Roth, J., Eilerts, K., Baum, M., Hornung, G. & Trefzger, T. (2023). Die Zukunft des MINT-Lernens – Herausforderungen und Lösungsansätze. In J. Roth, M. Baum, K. Eilerts, G. Hornung & T. Trefzger (Hrsg.), Die Zukunft des MINT-Lernens – Band 1. Springer Spektrum. https://doi.org/10.1007/978-3-662-66131-4_1

About Digital Skills & Literacy

While the topic of digital skills addresses the “what” and “how” of technology use – which tools are used and how they are operated – digital literacy broadens the focus to include the “why, when, who and for whom” (Bali, 2016). Digital literacy refers to the ability to use digital technologies safely and appropriately for information, communication, problem solving and the creation of digital content (Law et al., 2018) and includes critical judgement and ethical reflection.

BeLEARN treats digital skills and digital literacy as a single topic area, as technical skills without critical judgement are just as inadequate as reflective attitudes without practical application skills. Digital literacy is recognised at international, European, and national level as a strategically central competence for the 21st century and is anchored in education policy strategies and objectives:

• UNESCO: Inclusion of indicator 4.4.2 for SDG 4.4, which measures the percentage of youth and adults who have achieved at least a minimum level of digital literacy (UNESCO Institute for Statistics, n.d.).
• EU: The European Commission has developed guidelines for teachers to promote digital literacy and combat disinformation through education and training, emphasising that digital literacy has never been more important than in today’s increasingly digitalised world (European Commission, n.d.).

Sources:

Bali, M. (2016, 3. Februar). Knowing the difference between digital skills and digital literacies, and teaching both. Literacy Worldwide. https://www.literacyworldwide.org/blog/literacy-now/2016/02/03/knowing-the-difference-between-digital-skills-and-digital-literacies-and-teaching-both

European Commission (o.J.). Guidelines for teachers to foster digital literacy and tackle disinformation. https://education.ec.europa.eu/focus-topics/digital-education/action-plan/guidelines-for-teachers-to-foster-digital-literacy-and-tackle-disinformation

Law, N., Woo, D., de la Torre, J., & Wong, G. (2018). A global framework of reference on digital literacy skills for indicator 4.4.2 (UIS Information Paper No. 51). UNESCO Institute for Statistics. https://unesdoc.unesco.org/ark:/48223/pf0000265403

UNESCO Institute for Statistics. (o. J.). Monitoring framework. Abgerufen am 5. November 2025 von https://www.uis.unesco.org/en/gaml/monitoring-framework

About Digital Ethics

Digital ethics refers to a subfield of applied ethics that deals with the moral and normative issues of digital transformation. Building on information and media ethics, it examines which values and principles (e.g. autonomy, justice, privacy, transparency, responsibility) should guide the development, design and use of digital technologies – in particular data processing, algorithmic systems, and artificial intelligence – in individual, organisational, and social contexts, and how these can be implemented in practice.

Sources:

Fenner, Dagmar (2025): Exkurs: Digitale Ethik. Bundeszentrale für politische Bildung. Online. bpb.de

Filk, C. (2025). Strengthening Digital Self-Determination: Integrating Media Ethics and Artificial Intelligence into Teacher Training for Everyday School Life. In: medienimpulse, Bd. 63, Nr. 1. https://doi.org/10.21243/mi-01-25-29

About Data Science for Education

Data science for education refers to an interdisciplinary field of research and application that applies data science methods – including statistical analysis, machine learning, data mining, and data visualisation – to educational data in order to examine, predict, and improve teaching and learning processes, educational decisions, and education systems in an evidence-based manner. It combines data analysis methods with educational theory, taking into account the pedagogical, organisational and ethical context of data use in education.

Data science in education offers significant potential for the further development of research, teaching, and education systems. By integrating data science methods with pedagogical expertise, educational institutions can make data-informed decisions, design personalised learning environments, and better understand complex learning processes. At the same time, this field requires a high degree of sensitivity to ethical challenges, particularly with regard to data protection, fairness, and transparency. The research papers listed below show that data science in education can only be effective in the long term if technological possibilities and educational responsibility are considered together.

Sources:

Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s Not Forget: Learning Analytics Are About Learning. TechTrends, 59, 64–71. https://doi.org/10.1007/s11528-014-0822-x

Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann. https://doi.org/10.1016/C2009-0-61819-5

Ifenthaler, D., & Yau, J. Y.-K. (2020). Utilising Learning Analytics for Study Success: Reflections on Ethical and Data-Protection Considerations. Educational Technology Research and Development, 68, 421–426. https://doi.org/10.1007/s11423-020-09788-z

Martínez-Maldonado, R. (2019). Designing Learning Analytics for Educational Data Science. British Journal of Educational Technology, 50(6), 2763–2777. https://doi.org/10.1111/bjet.12871

Prinsloo, P., & Slade, S. (2017). An Elephant in the Learning Analytics Room: The Ethical Challenges of Learning Analytics. Journal of Learning Analytics, 4(1), 3–19. https://doi.org/10.18608/jla.2017.41.2

Suthers, D., & Verbert, K. (2013). Learning Analytics. American Behavioral Scientist, 57(10), 1410–1425. https://doi.org/10.1177/0002764213498851