Revolutionizing Cybersecurity Education in Undergraduate Curriculum by Leveraging Retrieval-Augmented Generation with Large Language Models

As cybersecurity threats become increasingly complex, traditional educational models struggle to maintain relevance, leaving students unprepared for the evolving challenges of the field.To address this gap, this work aims to enhance cybersecurity education in Electrical and Computer Engineering (ECE) undergraduate curricula by integrating Retrieval-Augmented Generation (RAG) technology with Large Language Models (LLMs).Leveraging the unique combination of RAG with LLMs, this approach provides a leap beyond existing frameworks by dynamically generating context-specific educational content in real time.The RAG-LLM integrated platform incorporates the latest research, real-world incidents, and case studies into the curriculum, enriching the learning experience and keeping students at the forefront of the field.The continuous adaptation of content ensures students receive up-to-date insights on the latest vulnerabilities, cyber defense strategies, and emerging attack vectors.This is a significant improvement over traditional models, which fail to keep pace with the rapid changes in the cybersecurity landscape.This approach also enables the creation of personalized learning pathways, which are tailored to meet the diverse needs of students.A primary focus of this work is the inclusion of specialized modules developed with the proposed RAG-LLM framework in Cyber-Physical Systems (CPS) security courses.These modules will also incorporate considerations of the ethical and societal implications of cybersecurity, which aim to enhance not only students' technical capabilities but also help in producing a socially responsible and well-rounded cybersecurity workforce.In its broad sense, this work addresses the integration of cutting-edge developments in Artificial Intelligence (AI) into the undergraduate engineering curriculum by devising a path that helps in identifying courses that benefit most from this integration.