AI-Powered Education: Rethinking the Way Programming is Taught Using AI Tools and Reversed Bloom's Taxonomy

Abstract

Artificial intelligence (AI) is revolutionizing the way programming is taught and learnt and is redefining t he way programming skills are assessed in the continuously changing field of computer science education. This paper introduces a novel approach for teaching and evaluating programming knowledge by reversing Bloom's taxonomy in order to accommodate AI-powered learning. Traditionally, Bloom's taxonomy progresses from basic cognitive skills like remembering and understanding to advanced skills like creating. With AI tools such as WebSim.ai and Anthropic Artifacts now capable of generating sophisticated outputs, the focus shifts away from students' ability to create, as these tools handle that task effectively. Instead, this paper proposes placing a higher emphasis on students' ability to understand and critically analyze AI -generated solutions, assessing their comprehension and ability to reverse-engineer existing work. We call this the Reverse Bloom Taxonomy, where students begin with creation and then move toward deeper understanding of the subject matter. This paper outlines the methodology for applying this reversed framework in programming education and presents a promising concept for improving student learning outcomes. The discussion addresses challenges in implementation and emphasizes the strategic integration of AI -driven learning to prepare students for a technology-driven workforce.