Education DCL: EAGER: Generative AI-based Personalized Cybersecurity Tutor for Fourth Industrial Revolution

The increasing complexity of Fourth Industrial Revolution (4IR) systems makes them highly vulnerable to cyberattacks, justifying cybersecurity training for the 4IR workforce. Although online programs are highly scalable and ideal for reskilling and upskilling an existing workforce, currently, they are unsuitable for 4IR cybersecurity training which requires access to specialized hardware. In addition, students from different backgrounds have different learning styles depending on their upbringing, educational backgrounds, and motivations. Successful program completion is especially challenging for students from marginalized, underrepresented communities, as a historical lack of access to high-quality education during their formative years (pre/middle/high schools) can impede their success. To address these challenges, this project leverages a Generative AI based Personalized Cybersecurity Tutor (gAI-PCT), a scalable online learning framework that trains a 4IR security workforce through video lectures and virtual reality (VR) based cybersecurity labs, personalized to the students learning needs through generative AI, using sentiment analysis to evaluate student interest and knowledge comprehension. This research is a collaboration between cybersecurity and education researchers at the University of Arizona and Sandia National Labs. The research team will explore: 1) a Generative AI-based interactive instructor to assist and guide students through their online coursework; 2) Virtual Reality based cybersecurity training labs that allow gamified 4IR cybersecurity training, including training on extreme scenarios without risking expensive equipment or human life; 3) Transformer-based machine learning approach to gauge student sentiments and knowledge comprehension during learning activities; 4) Generative AI-based approach to use student sentiment, and comprehension, in combination with Bloom's Taxonomy, to personalize coursework according to student's learning needs; 5) An approach to gauge non-normative outcomes measuring the effectiveness of students learning interventions while serving marginalized students; 6) An approach to increasing student's sense of belonging, academic attainment, and engineering identity that will result in increased academic persistence through the degrees. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.