Integrating AI into education has the potential to transform the teaching of science and technology courses, particularly in the field of cybersecurity. AI-driven question-answering (QA) systems can actively manage uncertainty in cybersecurity problem-solving, offering interactive, inquiry-based learning experiences. Recently, Large language models (LLMs) have gained prominence in AI-driven QA systems, enabling advanced language understanding and user engagement. However, they face challenges like hallucinations and limited domain-specific knowledge, which reduce their reliability in educational settings. To address these challenges, we propose CyberRAG, an ontology-aware retrieval-augmented generation (RAG) approach for developing a reliable and safe QA system in cybersecurity education. CyberRAG employs a two-step approach: first, it augments the domain-specific knowledge by retrieving validated cybersecurity documents from a knowledge base to enhance the relevance and accuracy of the response. Second, it mitigates hallucinations and misuse by integrating a knowledge graph ontology to validate the final answer. Comprehensive experiments on publicly available datasets reveal that CyberRAG delivers accurate, reliable responses aligned with domain knowledge, demonstrating the potential of AI tools to enhance education.

翻译：将人工智能融入教育领域，有望变革科学与技术课程的教学方式，尤其在网络安全学科中。人工智能驱动的问答系统能够主动应对网络安全问题解决过程中的不确定性，提供交互式、探究式的学习体验。近年来，大语言模型在AI驱动的问答系统中日益突出，实现了高级的语言理解与用户互动能力。然而，这些模型仍面临幻觉问题和领域专业知识有限等挑战，降低了其在教育环境中的可靠性。为应对这些挑战，我们提出了CyberRAG，一种基于本体感知的检索增强生成方法，用于开发网络安全教育中可靠且安全的问答系统。CyberRAG采用两步策略：首先，通过从知识库中检索经过验证的网络安全文档来增强领域专业知识，以提高回答的相关性与准确性；其次，通过集成知识图谱本体来验证最终答案，从而缓解幻觉与误用问题。在公开数据集上的综合实验表明，CyberRAG能够提供准确、可靠且符合领域知识的回答，彰显了人工智能工具在教育增强方面的潜力。