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

2026-01-08

arXiv.org

AI Summary

This study addresses the high cost and slow iteration of cybersecurity curriculum design, which often results in a misalignment between graduates’ competencies and rapidly evolving industry skill demands. To bridge this gap, the authors propose CurricuLLM, a novel framework that leverages fine-tuned large language models for automated curriculum development. The approach first standardizes input data using PreprocessLM, then employs a fine-tuned BERT model (ClassifyLM) to accurately categorize course content into nine established knowledge domains. By integrating job-market weightings, the system dynamically generates personalized, labor-market-aligned curricula. An expert validation mechanism further enhances both the efficiency of curriculum development and its relevance to industry needs. CurricuLLM thus offers a scalable and customizable intelligent solution for modern cybersecurity education.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Application Domains: Humanities & Computational Social Science
• Computer Vision: Large Vision Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• cybersecurity curriculum
• workforce alignment
• skills gap
• curriculum design
• educational personalization

Innovation

• CurricuLLM
• fine-tuned LLMs
• cybersecurity curriculum
• BERT-based classification
• workforce-aligned education