LLM-Augmented Approach for Learning of Human-Centric Cybersecurity

: Isoaho, Jouni; Rahman, Naeemur; Mohammad, Tahir

: Arabnia, Hamid R.; Deligiannidis, Leonidas; Amirian, Soheyla; Mohammadi, Farid Ghareh; Shenavarmasouleh, Farzan

: International Conference on The AI Revolution: Research, Ethics, and Society

Publisher: Springer Nature Switzerland

: 2026

Communications in Computer and Information Science

: AI Revolution : Research, Ethics and Society : International Conference, AIR-RES 2025, Las Vegas, NV, USA, April 14–16, 2025, Proceedings, Part I

: 2721

: 157

: 172

: 978-3-032-12312-1

: 978-3-032-12313-8

: 1865-0929

: 1865-0937

DOI: https://doi.org/10.1007/978-3-032-12313-8_12

: https://doi.org/10.1007/978-3-032-12313-8_12

Artificial intelligence, particularly Large Language Models (LLMs), is rapidly transforming the field of cybersecurity. This transformation introduces new challenges and security risks while simultaneously providing powerful and easily accessible tools for building and teaching cybersecurity. This article addresses the human-centric training of cybersecurity professionals to analyze and mitigate risks arising from human activity. This article evaluates the integration of large language models into the pedagogical curriculum for various thematic content areas of the training: threats and assets, privacy and surveillance, risk management, and human differences. The performance was assessed from both the cybersecurity learning and AI usage perspectives among the students. Furthermore, the article identifies key areas for improvement in the continued development of cybersecurity education.