Research
Intelligent Tutoring Systems
Our research focuses on the development and evaluation of intelligent tutoring systems (ITS) that leverage artificial intelligence to provide personalized learning experiences.
Research Focus
Intelligent tutoring systems represent a convergence of cognitive science, educational psychology, and artificial intelligence. Our work explores how these systems can:
- Adapt to individual learners by dynamically adjusting content difficulty, pacing, and instructional strategies based on learner performance and behavior
- Provide meaningful feedback by delivering timely, specific, and actionable feedback that supports learning rather than simply indicating correctness
- Model domain knowledge by representing expert knowledge in ways that support effective instruction and diagnosis of learner misconceptions
- Support cognitive processes by designing interactions grounded in theories of learning such as Cognitive Load Theory and the worked example effect
Current Interests
- Generative AI for personalized worked examples
- Adaptive scaffolding in problem-solving contexts
- AI-assisted formative assessment
- Human-AI collaboration in educational settings
BeLEARN Network
Our research contributes to BeLEARN, the Swiss research network dedicated to advancing the science of learning through interdisciplinary collaboration.