Neuroscience meets educational technology: we develop learning systems that make the learning process far more efficient. Our algorithms are specialized in capturing the individual level of knowledge and use advanced language learning models to make tasks more dynamic and interactive. That way knowledge will not only be imparted, but also promoted to in-depth understanding.Knowledge is always relational, not linear as in the textbook. Graph databases map these knowledge structures, which are then called up by an LLM. Our methodology is based on relational knowledge transfer and inquiry-based learning - an approach that arouses curiosity and demonstrates the significance of what has been learned.

Designing and implementing solar solutions for a cutting-edge racing car, powered by the sun.