CyberMentor: AI Powered Learning Tool Platform to Address Diverse Student Needs in Cybersecurity Education
Tianyu Wang, Nianjun Zhou, Zhixiong Chen
TL;DR
CyberMentor tackles the access gap faced by non-traditional cybersecurity students by delivering 24/7 AI-assisted mentoring through a three-component framework: Knowledge Base, Skill Base, and an agentic LLM using Retrieval-Augmented Generation. The system supports knowledge acquisition, skill development, and career guidance, with tools like CryptoSolver, ScriptCoder, and MLClassifier that guide users through structured problem solving and coding workflows. Three use cases and LangChain-based evaluation demonstrate robust performance (helpfulness 0.85, correctness 0.83, completeness 0.90) while highlighting cryptography's ongoing challenges in precise mathematical reasoning. The open-source, modular design enables adaptation to other disciplines, promising scalable, equitable, and sustainable enhancements to higher education pedagogy and resources.
Abstract
Many non-traditional students in cybersecurity programs often lack access to advice from peers, family members and professors, which can hinder their educational experiences. Additionally, these students may not fully benefit from various LLM-powered AI assistants due to issues like content relevance, locality of advice, minimum expertise, and timing. This paper addresses these challenges by introducing an application designed to provide comprehensive support by answering questions related to knowledge, skills, and career preparation advice tailored to the needs of these students. We developed a learning tool platform, CyberMentor, to address the diverse needs and pain points of students majoring in cybersecurity. Powered by agentic workflow and Generative Large Language Models (LLMs), the platform leverages Retrieval-Augmented Generation (RAG) for accurate and contextually relevant information retrieval to achieve accessibility and personalization. We demonstrated its value in addressing knowledge requirements for cybersecurity education and for career marketability, in tackling skill requirements for analytical and programming assignments, and in delivering real time on demand learning support. Using three use scenarios, we showcased CyberMentor in facilitating knowledge acquisition and career preparation and providing seamless skill-based guidance and support. We also employed the LangChain prompt-based evaluation methodology to evaluate the platform's impact, confirming its strong performance in helpfulness, correctness, and completeness. These results underscore the system's ability to support students in developing practical cybersecurity skills while improving equity and sustainability within higher education. Furthermore, CyberMentor's open-source design allows for adaptation across other disciplines, fostering educational innovation and broadening its potential impact.