Abstract:

The rapid development of generative artificial intelligence, especially the emergence of generative AI, together with the widespread use of Computer Algebra Systems (CAS) such as Wolfram Alpha and GeoGebra, has fundamentally transformed the landscape of mathematics education. In higher education — and particularly in the teaching of economic mathematics — these technologies raise essential pedagogical and methodological questions about the future of learning and teaching. While CAS tools can perform algebraic manipulations, differentiation, or function analysis within seconds, AI-based systems are now capable of providing natural-language explanations, generating examples, and offering adaptive feedback. As a result, the traditional challenge of manual computation has shifted toward a deeper issue: how students can interpret and apply machine-generated results within economic contexts.

In economic disciplines, mathematical concepts such as derivatives, optimization, and integrals serve as interpretive tools for decision-making, profit maximization, and cost analysis rather than as purely theoretical constructs. Consequently, the ability to reason about results — for example, understanding what an optimal point represents economically — becomes more important than the ability to perform manual symbolic operations.

The study, conducted within the framework of economic and business programs at the University of Debrecen, aims to explore how students begin to engage with emerging digital tools in the process of learning economic mathematics. Each week, students are required to complete short assignments in which they analyze a problem solved by ChatGPT and then correct or expand upon its solution. In addition, they solve a parallel task using a Computer Algebra System (CAS) such as GeoGebra or Wolfram Alpha. Through this dual approach, students are gradually introduced to both symbolic and generative computational environments.

Rather than presenting a fully established digital pedagogy, this phase of the research represents an exploratory stage — a practical attempt to understand how such technologies might be meaningfully integrated into teaching and learning. The aim is not only to observe students’ initial reactions and learning strategies, but also to identify the opportunities and limitations of using AI and CAS tools as complementary components in the rethinking of economic mathematics education.

Keywords:

Mathematics education, economics, CAS, AI.