Authors: Tianyu Wang, Nianjun Zhou, Zhixiong Chen
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.
Source: http://arxiv.org/abs/2501.09709v1
