KI-AUX
Teaching and Learning for the Workplace of the Future
How do we learn and teach for a world of work that AI is already transforming?
With KI-AUX, the Center for Didactics and Media at the Technical University of Applied Sciences Augsburg is developing an integrated ecosystem for teaching and learning: AI Mentor and AI Tutor as personal learning guides, an active learning initiative for instructors, and a research division that empirically tracks this transformation.
The DMZ was awarded funding for this project under the “Teaching Architecture” funding line of the Foundation for Innovation in Higher Education—a funding line aimed at universities and universities of applied sciences throughout Germany.
Facts and Figures
approx. 5.5 million euros
Grant Amount from the Foundation for Innovation in Higher Education Teaching
2025–2031
Project duration, with an option to extend through 2033
3 Work Areas
Campus App and AI, Active Learning, Media Production
2 AI Learning Guides
AI Mentor for Faculty, AI Tutor for Students
An ecosystem instead of individual tools
KI-AUX brings together elements that were previously separate: two AI learning guides, a professional development program for faculty, new learning spaces, and a research division that empirically supports this transformation. It’s all held together by the Technical University of Applied Sciences Augsburg Campus App—the operating system for innovative teaching.
Faculty and students do not operate separately within this ecosystem but rather within the same synchronous, hybrid, and asynchronous formats. Doctoral candidates in the research division support teaching while also serving as role models for the next generation.
AI Mentor for Educators
The AI Mentor supports instructors in further developing their teaching. It suggests engaging teaching methods, provides feedback on how interactive a course actually is, and helps in restructuring individual teaching units.
- Supportive—suggestions for methods and materials
- Reflective – Feedback on one’s own teaching practice
- Diagnostic – Identifies where students are struggling
AI Tutor for Students
The AI tutor serves as a personal learning guide for students. It answers questions related to the specific course, identifies common misconceptions, and provides targeted feedback rather than simply providing ready-made solutions.
- Supportive—help exactly where it’s needed
- Reflective – encourages students to think for themselves rather than telling them the answers
- Diagnostic—identifies misconceptions early on
The Project in Detail
The goal of the project is to further develop teaching and learning, with a particular focus on engaging teaching formats. These include concepts such as the inverted classroom, just-in-time teaching, problem-based learning, and HyFlex—formats that more actively involve students in the learning process and promote independent learning.
These pedagogical approaches are specifically supported through new teaching and learning spaces as well as digital and AI-powered tools. Technically, the corresponding features are integrated into the Campus App—for example, for the automated generation of quiz questions—always under the academic supervision of the instructors.
Faculty and students interact in synchronous, hybrid, and asynchronous formats. What begins in person can be explored in greater depth at a later time; what was prepared digitally is applied in the lecture hall.
Publications Related to the Project
- List, C., Müller, M., Kipp, M. (2025): Yet Another Collection of Programming Misconceptions – To Help Educators Find What Matters. In: Proceedings of Koli Calling – 25th International Conference on Computing Education Research.
- List, C., Wagner, L., Prochaska, A., Müller, M., Kipp, M. (2025): Digital Dynamics in the Lecture Hall: How the Augsburg University of Applied Sciences Campus App Supports Synchronous Teaching. In: Proceedings of the 6th MINT Symposium – The Future of MINT Education, Nuremberg, pp. 315–322.
- Müller, M., List, C., Kipp, M. (2025): The Power of Context: An LLM-Based Programming Tutor with Focused and Proactive Feedback. In: Proceedings of the 6th European Conference on Software Engineering Education (ECSEE 2025).
- Jell, L., List, C., Kipp, M. (2023): Toward Automated Interactive Tutoring—Focusing on Misconceptions and Adaptive Level-Specific Feedback. In: Proceedings of the 5th European Conference on Software Engineering Education (ECSEE 2023), pp. 226–235. Best Paper Award.
- Romero, N., Geppert, A., Kipp, M. (2023): From Standard to Excellence – Concepts for Improving Moodle Courses at Universities. In: 5th Symposium on University Teaching in STEM Subjects, Nuremberg.
- List, C., Wagner, L., Fasel, B., Neubert, D., Prochaska, A., Kipp, M. (2023): Next-Level Learning and Teaching: The Augsburg University of Applied Sciences Campus App. In: 5th Symposium on Higher Education Teaching in STEM Subjects, Nuremberg.
