Advancements in large language models (LLMs) have enabled the development of intelligent educational tools that support inquiry-based learning across technical domains. In cybersecurity education, where accuracy and safety are paramount, systems must go beyond surface-level relevance to provide information that is both trustworthy and domain-appropriate. To address this challenge, we introduce CyberBOT, a question-answering chatbot that leverages a retrieval-augmented generation (RAG) pipeline to incorporate contextual information from course-specific materials and validate responses using a domain-specific cybersecurity ontology. The ontology serves as a structured reasoning layer that constrains and verifies LLM-generated answers, reducing the risk of misleading or unsafe guidance. CyberBOT has been deployed in a large graduate-level course at Arizona State University (ASU), where more than one hundred students actively engage with the system through a dedicated web-based platform. Computational evaluations in lab environments highlight the potential capacity of CyberBOT, and a forthcoming field study will evaluate its pedagogical impact. By integrating structured domain reasoning with modern generative capabilities, CyberBOT illustrates a promising direction for developing reliable and curriculum-aligned AI applications in specialized educational contexts.
View on arXiv@article{zhao2025_2504.00389,
title={ CyberBOT: Towards Reliable Cybersecurity Education via Ontology-Grounded Retrieval Augmented Generation },
author={ Chengshuai Zhao and Riccardo De Maria and Tharindu Kumarage and Kumar Satvik Chaudhary and Garima Agrawal and Yiwen Li and Jongchan Park and Yuli Deng and Ying-Chih Chen and Huan Liu },
journal={arXiv preprint arXiv:2504.00389},
year={ 2025 }
}