The rapid expansion of academic programs, interdisciplinary courses, and outcome-based education frameworks across universities and educational boards has significantly increased the complexity of syllabus management and curriculum analysis. Institutions are required to continuously update course content to align with industry standards, accreditation requirements, and evolving technological trends. However, traditional manual methods of reviewing and categorizing syllabus documents are time-consuming, inconsistent, and prone to human error. To address these challenges, this research proposes an AI-Based Syllabus Analysis and Topic Classification System that automates the process of analysing, organizing, and classifying syllabus content using advanced Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques The proposed system is designed to extract textual information from digital syllabus documents in formats such as PDF and DOCX and convert them into machine-readable structured data. The extracted text undergoes multiple preprocessing stages, including tokenization, stop-word removal, stemming, and lemmatization, to enhance data quality and reduce noise. Feature extraction techniques such as Term Frequency–Inverse Document Frequency (TF-IDF) [9] are employed to convert textual data into numerical vectors that represent the importance of terms within the syllabus corpus. These feature vectors are then used to train and evaluate various machine learning classification algorithms The system incorporates supervised learning models such as Naïve Bayes [15], Support Vector Machines (SVM), and selected Deep Learning [5] architectures to categorize syllabus content into predefined academic domains such as Artificial Intelligence, Data Science, Computer Networks, Software Engineering, and others. Comparative performance analysis is conducted to determine the most efficient model in terms of accuracy, precision, recall, and F1-score. Experimental results indicate that machine learning–based approaches significantly improve classification accuracy while efficiently handling large-scale syllabus datasets. Deep learning models, in particular, demonstrate strong performance in capturing contextual relationships among topics in addition to topic classification, the system provides similarity analysis between syllabi from different institutions. This feature enables curriculum comparison, identification of content gaps, detection of redundancies, and benchmarking against standardized frameworks. The automated analysis supports curriculum designers and academic administrators in maintaining uniformity, ensuring compliance with accreditation bodies, and facilitating outcome-based education (OBE) planning. By reducing manual workload and enhancing consistency, the system contributes to improved academic governance and data-driven decision-making.
Artificial Intelligence (AI), Natural Language Processing (NLP), Machine Learning (ML), Text Classification, Topic Modeling, Educational Data Mining, Curriculum Analysis, Syllabus Classification.
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