CurricuLLM: Designing Personalized and Workforce-Aligned Cybersecurity Curricula Using Fine-Tuned LLMs

TL;DR

CurricuLLM introduces an automated, LLM-based framework for designing personalized and workforce-aligned cybersecurity curricula, addressing the labor-intensive nature of traditional curriculum development and aligning education with industry needs.

Contribution

The paper presents a novel LLM-driven approach for automated cybersecurity curriculum design, including a classification pipeline and validation with human experts, enabling personalized and industry-aligned education.

Findings

Effective classification of cybersecurity content using fine-tuned BERT.

Validation with human experts confirms efficiency and relevance.

Framework enables curriculum customization for specific job roles.

Abstract

The cybersecurity landscape is constantly evolving, driven by increased digitalization and new cybersecurity threats. Cybersecurity programs often fail to equip graduates with skills demanded by the workforce, particularly concerning recent developments in cybersecurity, as curriculum design is costly and labor-intensive. To address this misalignment, we present a novel Large Language Model (LLM)-based framework for automated design and analysis of cybersecurity curricula, called CurricuLLM. Our approach provides three key contributions: (1) automation of personalized curriculum design, (2) a data-driven pipeline aligned with industry demands, and (3) a comprehensive methodology for leveraging fine-tuned LLMs in curriculum development. CurricuLLM utilizes a two-tier approach consisting of PreprocessLM, which standardizes input data, and ClassifyLM, which assigns course content to nine Knowledge Areas in cybersecurity. We systematically evaluated multiple Natural Language Processing (NLP) architectures and fine-tuning strategies, ultimately selecting the Bidirectional Encoder Representations from Transformers (BERT) model as ClassifyLM, fine-tuned on foundational cybersecurity concepts and workforce competencies. We are the first to validate our method with human experts who analyzed real-world cybersecurity curricula and frameworks, motivating that CurricuLLM is an efficient solution to replace labor-intensive curriculum analysis. Moreover, once course content has been classified, it can be integrated with established cybersecurity role-based weights, enabling alignment of the educational program with specific job roles, workforce categories, or general market needs. This lays the foundation for personalized, workforce-aligned cybersecurity curricula that prepare students for the evolving demands in cybersecurity.