A Hybrid Transformer–Graph Framework for Curriculum Sequencing and Prerequisite Optimization in Computer Science Education with Explainable AI

Curriculum redesign in Computer Science and Information Technology has become increasingly complex due to rapid technological advancements, interdisciplinary knowledge requirements, and evolving industry expectations. Recent progress in artificial intelligence, particularly Transformer-based language models, offers new opportunities for data-driven and scalable curriculum analysis. This study utilizes syllabus-level textual datasets collected from multiple universities, comprising structured and unstructured course descriptions across diverse CS and IT programs. The dataset enables semantic representation learning and prerequisite inference while supporting cross-institutional curriculum analysis. We propose a hybrid framework that combines Transformer-based semantic encoding with graph-based prerequisite optimization and constraint-aware curriculum sequencing. The novelty of this work lies in integrating semantic prerequisite discovery, optimization-driven curriculum structuring, and explainable AI within a unified decision-support framework. Experimental results demonstrate that the proposed approach consistently outperforms existing machine learning and deep learning baselines, achieving higher prerequisite prediction accuracy, improved curriculum feasibility, and more coherent course sequencing, thereby offering a scalable and interpretable solution for evidence-based curriculum redesign in higher education.