fobizz Impuls
Math Behind the Machine: Finding Real World Math in AI Literacy
Medienbildung Künstliche Intelligenz Lehrkräfte Schulleitungen Lehrkräfte in leitender Position Lehramtsstudent*innen Referendar*innen Grundschule Weiterführende Schule Mathematik Einsteiger
fobizz Impuls
Math Behind the Machine: Finding Real World Math in AI Literacy
Medienbildung Künstliche Intelligenz Lehrkräfte Schulleitungen Lehrkräfte in leitender Position Lehramtsstudent*innen Referendar*innen Grundschule Weiterführende Schule Mathematik Einsteiger
Math Behind the Machine: Finding Real-World Math in AI Literacy is a practical, classroom-ready introduction to how artificial intelligence is built on math concepts students already learn. Educators will explore how AI models recognize patterns using Quick, Draw!, then build a simple AI classification model using Teachable Machine. Participants will see how core math ideas—like pattern recognition, fractions, proportional relationships, accuracy, probability, and data analysis—naturally connect to AI literacy without adding “one more thing” to teach. You’ll walk away with ready-to-use classroom activity ideas for grades K–12 and a hands-on understanding of how to embed AI literacy into everyday math instruction.
Lernziele
- You will learn to recognize how artificial intelligence operates as a prediction machine that depends on training data and pattern recognition to make decisions.
- You will learn to examine training data quality and assess how dataset characteristics, including size and accuracy, influence AI performance and discover potential sources of bias.
- You will learn to connect mathematical pattern recognition skills with AI literacy concepts, bridging these ideas across elementary through high school grade levels.
- You will learn to develop custom AI models by constructing and evaluating your own machine learning projects using Teachable Machine for mathematics education applications.
- You will learn to foster critical AI evaluation skills, guiding students to question AI outputs, understand technological limitations, and develop fact-checking habits when working with AI tools.