Research Areas

AI has the potential to fundamentally transform the future of medicine and healthcare by enabling earlier and more accurate diagnosis and better treatment, leading to improved outcomes for patients and increased efficiency in healthcare. The emergence of AI for medicine and healthcare also offers a number of transformative opportunities for economic growth. Examples cover prevention and early detection, e.g. AI for wearable devices as well as AI for screening (e.g. mammography). In relAI, we combine the expertise in AI healthcare and medicine to successfully tackle these challenges.

Engineers and computer scientists are currently developing autonomous systems with AI techniques as a core component. This provides endless possibilities but also comes with enormous challenges regarding safety, security, and privacy. For example, how to guarantee safety of an autonomous agent (e.g., a robot in a human environment) under all circumstances, given that a designer cannot foresee all situations the agent will face in the future? How to balance the advantages of AI cloud computing with the increased risk of security violations? How to leverage data to adapt to the needs of a human user while bearing privacy concerns in mind? To answer such questions, relAI will focus on safe, secure, and privacy-preserving AI in the context of autonomous agents and interacting systems.

Ever more applications in AI consider prescriptive modeling in the sense of learning a model that stipulates appropriate decisions or actions to be taken in real-world scenarios: Which medical therapy should be applied? Should this person be hired for the job? Decisions of that kind are increasingly automated and made by algorithms instead of humans, often relying on AI methods. Our ambition is to develop AI-based methodologies for reliable algorithmic decision-making (ADM).

This research area explores how reliable AI can transform learning and teaching through intelligent tutoring systems, digital learning assistants, and adaptive feedback mechanisms. It addresses the robustness and pedagogical effectiveness of such systems, particularly in the face of incomplete or biased data. Central challenges include ensuring privacy, fairness, and responsibility in data-intensive educational contexts, as well as understanding the broader effects of AI on learners and teachers. A special focus lies on explainability and trust, investigating how transparent and pedagogically meaningful communication of AI-driven actions can foster agency and metacognitive skills. By combining expertise from AI methodology, learning sciences, and education research, this area creates strong synergies with relAI’s other domains, ranging from fair assessment and robust learner modeling to socially interactive learning environments and simulation-based training in medicine.