Innovating Cybersecurity Education Through AI-augmented Teaching

Abstract

In traditional teaching frameworks, instructors face significant obstacles in offering current and synchronized learning materials and examples, especially when the course is taught by multiple instructors. This situation can affect the quality of the course's learning outcomes. These challenges become more pronounced in today’s higher education, because of the heightened complexity arising from the need to cover a range of course materials, diverse student backgrounds, varying skill levels, and different student expectations—all within the constraints of a fixed teaching and learning schedule. Furthermore, due to resource constraints, not every instructor has the availability of a teaching assistant (TA). Especially, while the demand for cybersecurity continues to rise, the dynamic nature of the cybersecurity field leads to the frequent emergence of new issues and incidents. To address these challenges, we examine the capabilities of generative AI to innovate teaching techniques and methods for cybersecurity curricula. We further explore the novel challenges introduced by generative AI, including issues related to privacy, data ownership, transparency, and other associated concerns, underscoring the need for comprehensive solutions. Our work further examines the teaching and learning capabilities of dynamically generated, up-to-date class materials in a personalized study environment augmented by AI. The adaptability of AI-augmented teaching across various disciplines will bring innovation to higher