Integrating Computational Thinking and AI-Driven Computational Methods into the Mathematics Curriculum
Type of Presentation
Panel
Location
D34011
Start Date
4-16-2025 10:00 AM
End Date
4-16-2025 10:45 AM
Description of Program
This panel, led by mathematics and computer science faculty, explores computational thinking (CT) in mathematics education, focusing on calculus, statistics, and courses for future teachers. It examines AI’s role in computational mathematics, showcasing problem-solving examples and addressing challenges in integrating CT and AI into the curriculum.
Abstract
Computational thinking (CT) is transforming mathematics education by equipping students with algorithmic problem-solving skills that enhance their understanding of core mathematical concepts. Computational Thinking (CT) is a problem-solving approach with key features such as decomposition, pattern recognition, abstraction, and algorithm design. Researchers in the field of problem-solving are increasingly recognizing CT as essential for developing students' problem-solving skills, enabling them to effectively navigate complex challenges. This presentation explores the integration of CT into calculus, statistics, and mathematics courses addressed to future teachers, illustrating the potential of the computational tools to improve student learning outcomes. Additionally, we will extend the discussion by examining the interaction between computational mathematics and artificial intelligence (AI). We will highlight key mathematical foundations of AI, including linear algebra, probability, optimization, and numerical methods. Through practical examples, we will display how AI techniques, such as machine learning and symbolic computation, are reshaping mathematical problem-solving and education. The presentation concludes with a discussion on the challenges and future directions of integrating CT and AI-driven computational methods into the mathematics curriculum.
Integrating Computational Thinking and AI-Driven Computational Methods into the Mathematics Curriculum
D34011
Computational thinking (CT) is transforming mathematics education by equipping students with algorithmic problem-solving skills that enhance their understanding of core mathematical concepts. Computational Thinking (CT) is a problem-solving approach with key features such as decomposition, pattern recognition, abstraction, and algorithm design. Researchers in the field of problem-solving are increasingly recognizing CT as essential for developing students' problem-solving skills, enabling them to effectively navigate complex challenges. This presentation explores the integration of CT into calculus, statistics, and mathematics courses addressed to future teachers, illustrating the potential of the computational tools to improve student learning outcomes. Additionally, we will extend the discussion by examining the interaction between computational mathematics and artificial intelligence (AI). We will highlight key mathematical foundations of AI, including linear algebra, probability, optimization, and numerical methods. Through practical examples, we will display how AI techniques, such as machine learning and symbolic computation, are reshaping mathematical problem-solving and education. The presentation concludes with a discussion on the challenges and future directions of integrating CT and AI-driven computational methods into the mathematics curriculum.