Current machine learning approaches have the potential to objectively and reliably capture human behavior based on predetermined criteria. Student behavior (for example, in interaction with simulated patients during medical interviews or during a practice lecture) can be recognized in this way and thus used to formulate feedback regarding their competence development. Building on extensive preliminary work of the experts involved in the project, a machine learning algorithm will be further developed that analyzes human behavior in terms of its convergence with desired behavioral criteria. The results thus obtained will be used to present students with automated, AI-based feedback on the behavior they exhibit in digitally-enriched face-to-face settings in different subjects (communicative behavior, e.g., in doctor-patient or parent-advice meetings). The added value compared to human feedback lies, among other things, in the greater objectivity and subsequent scalability of the approach. The Faculty of Applied Computer Science and the Faculty of Medicine are primarily responsible for this project. In the third year of the project, the developments will be transferred to teacher training (Faculty of Humanities and Social Sciences).

• ​​​​​​ Chair for Human-Centered Artificial Intelligence
• Chair of Medical Psychology and Sociology
• Chair of Medical Didactics and Education Research
• DigiLLab - Center for Digital Teaching and Learning