Integrating Artificial Intelligence in Cybersecurity Education: A Pedagogical Framework and Case Studies

The increasing adoption of Artificial Intelligence (AI) in cybersecurity demands a new approach to education that combines technical skills with critical thinking and problem-solving. This paper presents a pedagogical framework focused on constructivist learning, scaffolding, and experiential learning to integrate AI concepts into cybersecurity education effectively. The framework enables students to actively engage in solving real-world cybersecurity problems using AI, while gradually building their knowledge and confidence through structured support and hands-on experiences. The framework is demonstrated through three practical case studies: phishing detection using supervised learning, malware classification with decision trees, and anomaly detection in network traffic through clustering algorithms. These case studies illustrate how students progress from foundational AI concepts to advanced applications in cybersecurity, fostering a deeper understanding of both fields. This paper highlights the anticipated outcomes of this approach, including increased student engagement, enhanced technical performance, and better preparation for AI-driven cybersecurity roles. By emphasizing active learning, guided progression, and real-world application, this work offers a scalable and impactful method for transforming cybersecurity education.