[person id=”3559″ format=”page” show=”raum”]
Lectures
No matching entries found.Publications
2025
- Grelka, F., Kruse-Kurbach, T., & Berges, M. (2025). A Framework for Evaluating AI Powered Learning Platforms in K-12 and University CS Education. In 2025 IEEE Global Engineering Education Conference (EDUCON) (pp. 1-5). London, GB: New York City: IEEE.
DOI: 10.1109/EDUCON62633.2025.11016415
BibTeX: Download - Grelka, F., Lohr, D., & Berges, M. (2025, June). ALeA : Advancing Personalized Learning with Adaptive Assistance and Semantic Annotation. Paper presentation at Workshop KI und Bildung 2024, Bamberg, DE.
DOI: 10.20378/irb-108296
BibTeX: Download
Projects
Funding source: Stiftung Innovation in der Hochschullehre
Project leader:
The FAUstairs project aims to improve academic success. The new teaching architecture developed and implemented as part of the project focuses on promoting learning and subject-specific skills, which are central components of FAU’s internal definition of academic success. Innovative and AI-supported formative and summative assessments are designed to support students in developing these skills. In addition, data-based modelling of FAU degree programmes is being used to develop a privacy-compliant m…
Funding source: BMBF / Verbundprojekt
Project leader: , ,
Prof. Dr. Marc-Pascal Berges
Professors
Contact
The joint project VoLL-KI further develops higher education on three levels: study programs, individual study planning and learning progress within a course. AI-based systems provide support at all three levels. For this purpose, data from the Computer-based Decision Support System for Higher Education in Bavaria (CEUS) will also be used. At the same time, CEUS can be extended with data from the VoLL-KI project. The analysis combines individual and group-specific data. This should help to make university teaching non-discriminatory in the long term. Based on the data, individual recommendations for the course of study can be created. Students can then view reasons for the recommendations, look at alternatives, and also provide feedback on the suggestions. In a pseudonomous form, those responsible for the study program can also access this data. The project is evaluated by means of surveys and data analysis. Scientists from the fields of AI, computer science, computer science didactics and educational research from three neighboring universities are involved in the project. The initial focus is on study programs in computer science that differ at the three locations: a large, engineering-oriented computer science, a medium-sized, interdisciplinary-oriented computer science, and a small, application-oriented computer science. The results will then be transferred to other study programs at the participating universities.