Leibniz AI Academy - Cross-disciplinary, hybrid micro-degrees for study & further education
Led by: | Verbundprojektleitung: Dr. Ralph Ewerth, Forschungszentrum L3S, Teilprojektleitung IfBE: Prof. Dr. Steffi Robak |
E-Mail: | steffi.robak@ifbe.uni-hannover.de |
Team: | Ariane Kramer |
Year: | 2021 |
Funding: | BMBF |
Duration: | 12/21 – 11/25 |
Further information | https://www.uni-hannover.de/de/universitaet/aktuelles/online-aktuell/details/news/grosszuegige-foerderung-fuer-faecheruebergreifendes-programm-leibniz-ai-academy/ |
The aim of the project is to develop and establish a transcurricular, interdisciplinary micro-degree programme "Leibniz AI Academy" at Leibniz Universität Hannover (LUH), in which students from different degree programmes acquire skills in the field of artificial intelligence (AI). The courses are additionally offered as a continuing education programme for interested parties from industry and other organisations. The courses are based on a flexible didactic hybrid concept and enable both flipped classroom with face-to-face events and online participation.
Through a cross-disciplinary concept, AI competences are not only anchored in computer science, but in the breadth of the LUH's teaching offer (e.g. mechanical engineering, geosciences, personalised medicine, mathematics, teaching STEM subjects). The modularised and differentiated course programme enables learners to recognise possible applications in their subject disciplines or companies, to develop predictions and prediction models, and to critically reflect on their opportunities and limitations. To this end, the interdisciplinary consortium incorporates the specific requirements of the subjects into the design of subject-specific micro-degree curricula.
This is made possible by domain-specific courses and a high degree of modularisation of the course units. The design of the learning units is based on current findings in instructional design and follows constructivist principles. The courses consist of short videos, quizzes, interactive exercises, programming tasks and projects, among other things. Problem-based learning is also used so that learners can develop practical solutions (also cooperatively) in realistic scenarios. The offer is accompanied, evaluated and continuously developed by sub-projects from computer science and further education didactics.