AI-POWERED ADAPTIVE LEARNING SYSTEMS FOR ENHANCING CYBERSECURITY EDUCATION IN UNDERGRADUATE INFORMATICS PROGRAMS

Abstract

Cybersecurity education is a critical component of undergraduate informatics programs, requiring innovative teaching approaches to address diverse student needs and evolving technological challenges. This study explores the application of AI-powered adaptive learning systems to enhance cybersecurity education by providing personalized learning experiences. The research leverages adaptive algorithms to tailor course content, assessments, and feedback based on individual student progress and learning styles. By integrating Artificial Intelligence, the proposed system dynamically adjusts to student competencies, offering real-time support and resources to bridge knowledge gaps. Initial findings demonstrate that adaptive learning systems significantly improve student engagement, comprehension, and performance in cybersecurity courses. Furthermore, the study highlights the potential of AI to optimize curriculum delivery and foster critical skills necessary for addressing modern cybersecurity threats. This research provides valuable insights for educators and institutions aiming to implement adaptive learning systems in informatics programs, setting a foundation for future developments in technology-enhanced education.