As math teachers, we know the best modeling problems come from real-world, messy data. But finding those scenarios and tailoring them to our students' interests takes time.
What if you had an assistant who could instantly brainstorm 10 different modeling problems, generate a sample data set, and even help you differentiate the task for your students?
That assistant is Artificial Intelligence.
Welcome! In this 30-minute module, we'll explore how to use AI tools not as a scary black box, but as a practical, time-saving creative assistant to make your mathematical modeling lessons more relevant and engaging.
By the end of this module, participants will be able to:
Differentiate between Predictive AI and Generative AI and explain the evolving role of CAS in the context of modeling.
Analyze the mathematical modeling cycle to pinpoint the specific phases where Generative AI provides the greatest time-saving assistance (e.g., problem formulation, data generation).
Construct effective GenAI prompts to instantly generate contextually relevant, open-ended modeling problem ideas based on specific student interests or curriculum units.
Design and format synthetic datasets using Generative AI by specifying required variables, correlation strength, and output format (e.g., table, CSV, Python list) for immediate classroom use.
Identify and apply AI-powered techniques (Image AI, Code Interpreters) to create engaging visual aids for problem context and data visualization.