Grant approval of two RWTH applications on artificial intelligence in the field of higher education


 The RWTH University was successful with both an individual and a collaborative application in the federal-state initiative to promote artificial intelligence in higher education. The projects “bridgingAI” and “AIStudyBuddy”, which are coordinated by the RWTH Center for Artificial Intelligence (AI Center), have two main goals: building up interdisciplinary AI competences among as many students as possible and optimizing study planning using AI. To reach these goals, the initiatives will build on the existing AI expertise at RWTH.

  MOOCs bridgingAI Copyright: © Bastian Leibe Curriculum for the micro-bachelor "bridgingAI"


For the application “bridgingAI: Building interdisciplinary bridges to AI'', 1.9 million euros were requested for a project period of 4 years. Coordinated by the AI Center and under the leadership of Prof. Dr. Bastian Leibe, the principal investigators Dr. Holger Rauhut, Prof. Dr. Erhard Cramer, Prof. Dr. Markus Strohmaier, Prof. Dr. Saskia Nagel, Prof. Dr. Wil van der Aalst, Prof. Dr. Sebastian Trimpe, Prof. Dr. Ulrik Schroeder and PD Dr. Malte Persike are working on establishing interdisciplinary, future-oriented, and scientifically founded competences in the field of artificial intelligence for students.

Thereby, RWTH University is taking up an important challenge that many universities are currently facing. Educational offers related to AI are in high demand, while at the same time the numbers of students attending lectures are limited due to capacity problems. Hence, scalable solutions are required. This is where bridgingAI comes into place: Building on the existing AI expertise at RWTH University, a micro-bachelor will be developed that is conceived for students from various disciplines. This micro-bachelor aims to build a bridge curriculum at the transition from bachelor to master. In the first phase, it will consist of ten Massive Open Online Courses (MOOCs), which will account for different levels of background knowledge. All MOOCs will be published as Open Educational Resources, for example via the edX platform. The content is intended to create a common foundation of skills for students so that they can actively shape science, industry, and society in the AI era beyond their own professional perspective. The aim is for participants to be able to evaluate, use, and develop AI meaningfully and responsibly.

  Diagram AIStudyBuddy Copyright: © Wil van der Aalst Overview of the different components of the collaborative project "AIStudyBuddy"


For the collaborative project “AIStudyBuddy: AI-based support for study planning”, 3.9 million euros were requested for a funding period of 3.5 years. Within RWTH University, the AI Center takes on a leading role in planning the project. Besides RWTH, the Ruhr-Universität Bochum (RUB) and Bergische Universität Wuppertal (BUW) are working together toward using modern AI technologies to support the planning and reflection of individual courses of study.

The focus is on two target groups: For students, the "StudyBuddy” provides informed and evidence-based planning of their studies over several semesters into the future. StudyBuddy enables advanced visualizations of study progress and provides action-oriented feedback. AI technologies are, for example, used to determine courses of studies that have led to successful completion of a degree in the past.

The second target group is the group of curriculum designers. With "BuddyAnalytics”, they receive a tool that provides interactive visualizations and information for decision making. The goal is to help them improve competence-oriented curriculum development as well as advising students using data driven analytics. The project combines the AI paradigms of data-based (Process Mining) and rule-based AI (Answer Set Programming). Process mining discovers and analyzes actual study behavior using data from the Campus system, study management, and examination systems. It compares real courses of study with the intended ones. With the help of Answer Set Programming, detailed and transparent explanations for feedback are generated and presented in a way which makes them understandable for non-experts. All components are combined in an architecture that takes ethical aspects and data security into account.

The AIStudyBuddy is being developed and evaluated in the project network, coordinated by Prof. Dr. Ulrik Schroeder (RWTH), PD Dr. Malte Persike (RWTH), Prof. Dr. Gerhard Lakemeyer (RWTH), Prof. Dr. Wil van der Aalst (RWTH), Prof. Dr. Maren Scheffel (RUB), Prof. Dr. Sebastian Weydner-Volkmann (RUB), Dr. Peter Salden (RUB), Prof. Dr. Kerstin Schneider (BUW) and Dr. Simon Görtz (BUW). The aim is to use evidence-based study monitoring, interactive tools for study planning and data-based curriculum design to enable better individual courses of study and thereby more successful graduates.