EAGER: CyberAI Innovation: Secure Autonomy in Adversarial Ecosystems: An Agentic AI-based Cyber Defense Education Framework

Foundation models and agentic AI systems are rapidly becoming part of the digital infrastructure that supports government operations, defense systems, critical services, and everyday technologies. These adaptations have created AI-native cyber threat environments that are not addressed within traditional cybersecurity. Traditional cybersecurity education still focuses heavily on networks, software, and systems, while emerging AI-enabled threats extend beyond classical network and software security models. This project addresses this gap by developing and evaluating modular CyberAI. The developed educational materials will help students understand, build, test, and secure AI-enabled systems. The broader goal is to prepare a national workforce with practical skills at the intersection of Cybersecurity and AI, while allowing institutions to adopt the materials within existing Computer Science, Cybersecurity, Data Science, or Engineering programs without requiring full curriculum redesign. This project will design, prototype, and evaluate an experimental modular curriculum framework for CyberAI education, with emphasis on investigating how agentic AI concepts can be integrated into cybersecurity instruction and whether such integration is pedagogically viable across multiple computing-related programs. Using design-based exercises, and assessment instruments that position students as both AI system builder and adversarial analysts. The technical scope includes AI-native attack surfaces, prompt and context manipulation, model and data leakage, agent workflow compromise, secure AI pipeline design and supply chain risk. Expected contributions include a reusable CyberAI curriculum architecture, evidence on student learning and skill development in AI-enabled cybersecurity, and a foundation for scalable educational models that align with emerging workforce needs in AI, cybersecurity, secure autonomy, and trustworthy AI systems. This project is supported by the CyberAICorps Scholarship for Service (CyberAI SFS) program which funds proposals to increase national capacity to educate and train professionals in Artificial Intelligence (AI) or Cybersecurity, and to support their placement and retention in the CyberAI mission of government organizations. The Scholarship Track funds academic institutions to award scholarships to students in exchange for their government service. The Innovation Track seeks transformative education proposals in the areas of AI, cybersecurity, or the integration of AI and cybersecurity. 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. NSF Award ID: 2621941 | Program: 04002627DB NSF STEM Education | Principal Investigator: Farah Kandah | Institution: Auburn University, AUBURN, AL | Award Amount: $299,978 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2621941 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2621941.html