Demystifying AI in the Math Classroom – Exploring Machine Learning Concepts with High-School Students

Zusammenfassung

AI and machine learning (ML) rely fundamentally on mathematical modeling. Interestingly, many of the core mathematical techniques underpinning ML are quite elementary – often accessible with high-school level mathematics. This observation suggests that AI education should not be limited to computer science courses, but should also be meaningfully integrated into mathematics curricula. In our talk, we will explore various strategies for simplifying and teaching key mathematical ideas behind Support Vector Machines and Artificial Neural Networks. These methods are deeply rooted in mathematical concepts from linear algebra and calculus – mathematical areas that students often find abstract or unengaging By addressing data-driven problems from AI within the context of mathematics education, we have a unique opportunity to make mathematical concepts more relevant and exciting for students, while also fostering a deeper understanding of AI, including its risks and potential. We will outline intended learning pathways and digital learning material for high-school students and will critically examine which underlying mathematical concepts can be explored in the classroom (‘white-box’ approaches), and which may need to be treated as ‘black-box’ due to their inherent complexity.

Sarah Schönbrodt

Assistenzprofessorin @ Universität Salzburg

Forschung im Bereich Mathematikdidaktik und KI-Bildung