Integrating Computational Thinking and AI-Driven Computational Methods into the Mathematics Curriculum

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.