arXiv subjects

Arthur Nijdam

Publications and source records attributed to Arthur Nijdam.

3 recordsLinked to original sources

arXiv2026

CourseGraph: Finding overlaps and differences in Computer Science courses across universities

Student mobility programs such as Erasmus+ enable students to take courses at other universities, broadening their academic and cultural horizons. However, this flexibility also leads to a practical challenge: ensuring that students do not take courses elsewhere that substantially overlap with courses in their home curriculum. In this work, we propose CourseGraph, a methodology that automates the evaluation of external courses based on insights obtained from the process followed by curriculum administrators when assessing courses for inclusion in a degree program. Course- Graph extracts information such as course titles, descriptions, and learning outcomes from the course webpage. Then, this information is represented semantically using a BERT-based language model, after which the pair-wise similarity between courses can be computed. This information is then used by a Random Forest classifier to determine whether a candidate course abroad overlaps with a course already contained in the student's curriculum. We evaluate CourseGraph using (1) the Computer Science program at Eindhoven University of Technology, which contains information about courses with substantial overlap, and (2) six approved international programs from students enrolled in the Computer Science program at Lund University, including the corresponding decisions made by a curriculum administrator. The experimental results indicate that CourseGraph provides an effective approach for identifying overlapping courses and supporting curriculum alignment across universities.

cs.AI↗

arXiv2026

CyberBridge: Bridging the Gap Between Cybersecurity Education and Industry

This full research paper presents CyberBridge, a framework that automatically maps cybersecurity job descrip- tions to professional role profiles. As the cybersecurity landscape evolves rapidly, it is difficult for academic programs to align curricula with the competencies expected in practice. CyberBridge addresses this gap by decomposing a given vacancy description into its constituent Knowledge, Skill, and Task (KST) statements, embedding them using a sentence- BERT model, and matching them to the most semantically similar workforce profiles. A key contribution of our approach is its interpretability. Rather than functioning as a black box, CyberBridge enables users to trace recommendations back to the specific competencies driving each match, providing a human- readable justification for the resulting mappings. CyberBridge supports three primary use cases; (1) job recom- mendation, providing students with vacancies and closely match- ing professional roles based on their completed curriculum, (2) market analysis, enabling educators and curriculum developers to analyze which roles are currently in demand, and (3) curriculum planning, assessing which course program best prepares students for in-demand cybersecurity roles. As such, CyberBridge can be used by educational institutions as a practical tool for career guidance and evidence-based curriculum development in cybersecurity education.

cs.CR↗

arXiv2026

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

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.

cs.CR↗