Mathematical foundations of AI in schools – opportunities for enhancing mathematics education

Abstract

AMethods from the field of machine learning (ML) underlie numerous applications in our everyday lives. The enormous relevance of such applications gives rise to the demand for a stronger integration of ML related topics into the high-school curriculum. We argue that this can and should not only take place in computer science, but also in mathematics education. Ultimately, methods from data science and ML are based on mathematical modeling with a special focus on handling (numerous) data. The mathematical methods used are often elementary and accessible with high-school knowledge, e.g., with analytical geometry. In this talk, we discuss to what extent methods from the field of ML are suitable for addressing several goals at the same time: allowing for authentic modeling activities, promoting data literacy, and giving students an insight into the mathematical background as well as the risks and opportunities of AI systems.

Sarah Schönbrodt

Assistant professor @ University of Salzburg, Austria

Research in Mathematics Education and AI Education