Neben Fachkompetenzen sind in der Industrie Vorgehensweisen gefragt, komplexe Problemstellungen schnell und innovativ zu lösen. Das Lösungsdesign als wesentliche Problemlösungskompetenz in komplexen Systemen und Vorstufe des Entrepreneurship ist eine Schlüsselkompetenz des 21. Jahrhunderts. Studierende technologieorientierter Fachrichtungen erfahren eine exzellente Fachausbildung, haben aber häufig ein Defizit in dieser wesentlichen, praxisrelevanten Metakompetenz. Das Projekt zielt darauf ab, diese Lücke zu schließen, indem ein Lehrmodul „Lösungsdesign“ entwickelt und beispielhaft umgesetzt wird, welches flexibel in alle Studiengänge integriert werden kann. Das Ergebnis sind Learning-Nuggets sowie ein Train-the-Trainer Kurskonzept. Die Übertragbarkeit in beispielsweise berufsbildende oder schulische Ausbildungskontexte soll gewährleistet werden.

In addition to specialist skills, industry requires approaches that enable complex problems to be solved quickly and innovatively. Solution design as an essential problem-solving competence in complex systems and a precursor to entrepreneurship is a key competence of the 21st century. Students of technology-oriented disciplines experience excellent technical education, but often have a deficit in this essential, practice-relevant metacompetency. The project aims to close this gap by developing and exemplarily implementing a "solution design" teaching module that can be flexibly integrated into all degree programs. The results are learning nuggets and a train-the-trainer course concept. The transferability into e.g. vocational or school education contexts shall be guaranteed.

This proposal aims to integrate cutting-edge machine learning (ML) techniques into the economics, computer science, and mathematics curriculum. The planned teaching initiative covers the fun-damentals of ML, including supervised and unsupervised learning techniques like decision trees or clustering, as well as the latest models in deep learning, such as encoder/decoder architectures or attention-based transformer models. Students will work in mul-tidisciplinary teams, using these techniques to nalyse real-world economic datasets and address original research questions. Students will also learn how to incorporate AI-based conversational agents like ChatGPT as tools into their workflow. They will learn about these tools’ ethical and legal aspects and discuss pitfalls and challenges. A unique aspect of this course is integrating a peer review system to improve students’ projects.

Prof. Dr. Rebekka Hufendiek, Dr. Katja Springer & Roman Yaremko, PhD
(Humboldt-Centre, School of Humanities)

The Computer Science and Society module is an interdisciplinary and innovative course format in which a) basic argumentation skills, b) sensitivity to socially relevant topics in computer science, and c) ethical concepts and skills relevant to this area (autonomy, security, responsibility, etc., weighing up values, justifying ethical assumptions, etc.) are learnt and actively practised. To this end, the course is divided into lectures and written examinations as well as panel discussions and reflection reports. In the panel discussions, students are asked to take on distributed roles in discussions with experts from the field (AI and automated decision-making, data protection, sustainability, democracy and social media, digital participation, military technology, etc.) and then summarise their arguments in a reflection report.