A Novel AI-Human Teaming Approach to Trust and Cooperation in AI-Cybersecurity Education

点击翻译按钮获取中文摘要

This project aims to serve the national interest by improving the state-of-the-art in cybersecurity education. The national cyberinfrastructure is vulnerable to a wide range of cyberthreats and hazards. Cyberthreat intelligence data gives insight into a cyberattack and provides an opportunity to become proactively secure. Proactive cybersecurity has become dependent upon AI, making it essential that cybersecurity practitioners gain an understanding of both cybersecurity and AI. This project will combine research in AI, cybersecurity, education, and behavior science to investigate evidence-based and knowledge-generating approaches to cybersecurity education that emphasizes AI-human teams. The investigators will study trust and cooperation in AI-human teams to generate knowledge that will inform future educational efforts that lie at the intersection of AI and cybersecurity.This project will advance educational knowledge in the fields of AI, cybersecurity, and behavior science through three objectives, resulting in 1) AI-Enabled Defense Simulation Suite (AI-EDSimS), enabling hands-on learning for students and cybersecurity professionals, 2) a new course on AI and Cybersecurity transforming undergraduate education by integrating the two subjects with a focus on AI-human teaming for proactive cybersecurity practice, and 3) new knowledge in AI-Cybersecurity education as it relates to AI-human teaming, leading to improved understanding of the role of trust and cooperation in AI-human teams. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.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.