MATE

Machine Learning for Adaptive Teaching (MATE)

01/05/2026 to 01/05/2030

Education is the foundation of our society. Yet recent studies revealed a troubling trend: Student achievement is declining, while social inequalities are considerably increasing. Schools face increasing pressure to provide personalized, adaptive support, ensuring equal opportunities for all students, regardless of their background. AI, especially large language models (LLMs), offers immense potential for adaptive learning, but its evidence-based integration into educational practice remains largely unexplored. Based on my expertise in adaptivity, with my Momentum proposal, I am transforming my professorship into a transdisciplinary hub for AI-driven educational research. I will integrate expertise in machine learning, data science, and AI engineering with educational research to design and rigorously investigate AI-driven adaptive learning systems. Using big data in real-world settings, I will provide causal and generalizable conclusions across domains. By collaborating closely with local and international partners, I create an intersection between educational research, subject-matter didactics, and AI. At the core of this proposal is a co-constructive process with teachers, students, and educational administrators, ensuring that adaptive learning systems are practical, effective, and scalable. This project strengthens my international visibility, expands research collaborations, and establishes a rigorous foundation for advancing AI-enhanced personalized education.