STEM Education in the Age of Generative AI: Case Studies in Teaching Mathematics and Computer Science to Generation Z and Alpha

This paper presents two pedagogical approaches for teaching in the era of generative artificial intelligence (GenAI), using case studies from mathematics and computer science. As Generation Z and Alpha have grown up immersed in technology, their learning styles differ significantly from previous generations, creating challenges for traditional instructional methods. While digital platforms provide flexibility, accessibility, and interactivity, the rapid advancement of generative AI adds new layers of complexity. These AI tools enhance self-learning and problem-solving but also raise concerns about ethical usage and conceptual depth. Building on existing research, we introduce two methods: one based on a “learning how to learn” approach in mathematics, which emphasizes mathematical reasoning and problem interpretation over procedural mastery, and another for integrating AI into computer science to support problem-solving, software development, and data-driven decision-making. Preliminary results suggest that students using these approaches demonstrate a stronger ability to leverage AI responsibly, critically assess its outputs, and apply it across disciplines. These findings highlight the need for curriculum adaptations that foster critical thinking, ethical awareness, and adaptability in the AI-driven future.