Subproject description

Current machine learning approaches have the potential to objectively and reliably record human behavior on the basis of predefined criteria. Behavior of students (e.g., in the interaction with acting patients during a medical consultation or during an exercise lecture) can be recognized in this way and thus used to formulate feedback with regard to their competence development. Based on extensive preliminary work, 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 their behavior exhibited in digitally enriched face-to-face settings in different subjects (communicative behavior, e.g., in doctor-patient or parent counseling interviews). 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).