CyberMentor: AI Powered Learning Tool Platform to Address Diverse Student Needs in Cybersecurity Education

2025-01-16

AI Summary

Non-traditional cybersecurity learners frequently encounter challenges including insufficient mentorship, poor domain-specific adaptation of AI assistants, delayed responses, and overly generic recommendations. To address these issues, this paper introduces the first AI-powered tutor platform specifically designed for this cohort, integrating agent-based workflows with Retrieval-Augmented Generation (RAG) to support three core educational scenarios: knowledge Q&A, skill practice, and career development. Built upon a large language model (LLM) agent architecture, the platform leverages LangChain-based prompt engineering and multi-scenario prompt-driven evaluation to balance educational equity, disciplinary depth, and open-source sustainability. Experimental results demonstrate statistically significant improvements over baseline methods in helpfulness, correctness, and completeness—enhancing learning accessibility and pedagogical sustainability. The proposed framework establishes a transferable paradigm for interdisciplinary AI-supported education.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: AI for Accessibility
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• non-traditional students
• cybersecurity education
• AI assistance limitations

Innovation

• AI Learning Platform
• Personalized Education
• Cybersecurity Education