In recent years, the handling, processing, and analysis of data, e.g. in the context of Big Data and machine learning, has developed enormously - due to the transformation processes of digitalization. The increasing collection, storage, and automated processing and evaluation of data in all areas of human life also have an enormous impact on everyday life, society, and politics. Today, everyone needs the skills to store, use and analyze data competently, but also to assess the consequences and effects on our society. Teaching the corresponding competencies - so-called "data literacy" - is a major challenge for schools, universities, and education in general. Artificial intelligence, the most emergent topic of recent years, is of particular importance in this context. It is essential to prepare learners for a life in the digital world. Therefore, it is crucial to impart competencies that - independent of current technologies and "hypes" - enable students to understand and shape the so-called digital world in the long term. In this research area, underlying ideas and principles will be identified that are independent of concrete technologies or applications and thus contribute to sustainable teaching. These ideas and principles are prepared and evaluated in the form of teaching-learning concepts for learners, and their transfer into teaching practice is investigated.
Morales-Navarro, L., Kafai, Y. Kahn, K., Romeike, R., Michaeli, T., DiPaola, D., Ali, S., Williams, R., Breazeal, C., Castro, F., Desportes, K., Stager, G., Kumar, V., Bodon, H., Worsley, M., Lee, V., Sarin, P., Xie, B., Wolf, J., Sieh, I., Dennison, D., Garcia, R. & Solomon, C. (2023). Constructionist Approaches to Learning Artificial Intelligence/Machine Learning: Past, Present, and Future. Proceedings of Constructionism 2023.
Michaeli, T., Romeike, R. & Seegerer, S. (2023): What students can learn about artificial intelligence - recommendations for K12 computing education. In Proceedings of World Conference on Computers in Education, WCCE 2022. Hiroshima, Japan.
Jaschke, S., Klusch, M., Krupka, D., Losch, D., Michaeli, T., Opel, S., Schmid, U., Schwarz, R., Seegerer, S., & Stechert, P. (2023). Positionspapier der Gesellschaft für Informatik e.V. (GI): Künstliche Intelligenz in der Bildung.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences 103, 2023
Michaeli, T., Seegerer, S., Kerber, L. & Romeike, R. (2023). Data, Trees, and Forests – Decision Tree Learning in K-12 Education. Proceedings of the 3rd Teaching Machine Learning and Artificial Intelligence Workshop, PMLR.
Michaeli, T., Seegerer, S., Jatzlau, S., & Romeike, R. (2020). Looking Beyond Supervised Classification and Image Recognition – Unsupervised Learning with Snap! In Carina Girvan, Jake Rowan Byrne (Eds.), Constructionism 2020: Exploring, Testing and Extending our Understanding of Constructionism conference proceedings. Dublin, IE.
Seegerer, S., Michaeli, T., & Romeike, R. (2020). So lernen Maschinen! LOG IN - Informatische Bildung und Computer in der Schule, 40 (193/194).
Michaeli, T., Seegerer, S. & Jatzlau, S., (2020). Kein Hexenwerk: Ideen des maschinellen Lernens in Snap! LOG IN - Informatische Bildung und Computer in der Schule, 40 (193/194).
Jatzlau, S., Michaeli, T., Seegerer, S., & Romeike, R. (2019). It’s not Magic After All - Machine Learning in Snap! using Reinforcement Learning. In 2019 IEEE Blocks and Beyond Workshop (Blocks and Beyond). Memphis, TN, USA: IEEE.
Teaching material
- MOOC "Exploring the World of AI" (d/en)
- AI teaching modules (IT2School; de)
- How machines learn (de)
- Further materials (de)