Knowledge Graphs (KG) are vital for organizing information from large datasets, enabling efficient knowledge extraction. However, manual KG construction is time-consuming and challenging due to data diversity and complexity, especially in domain-specific applications. Massive Open Online Courses (MOOCs) platforms provide abundant, high-quality Information Technology (IT) education resources, yet these remain unstructured for effective knowledge extraction and use. This study proposes an automated method for constructing KG with high accuracy, scalability, and optimal resource utilization to address this problem. The proposed approach integrates multiple components into a comprehensive, automated KG construction pipeline, including contextual database creation, entity and relation extraction from diverse data formats, and graph completion via hidden link discovery. This study’s key contributions include: (1) a fully automated process to extract and structure IT MOOC data for KG construction; (2) an approach to improve Large Language Models (LLMs) performance in Named Entity Recognition (NER) tasks; (3) comprehensive empirical evaluations exploring LLM capabilities in NER and Knowledge Graph Completion. The study yields a comprehensive knowledge graph for the IT MOOC domain, comprising 16 entity types, 1,923 unique entities, and 32 relation types with more than 3,590 triples. Experimental results indicate that the majority of LLMs achieve F1-score enhancements between 2% and approximately 10% in NER tasks while proficiently identifying relationships among entities. The results underscore the promise of automated knowledge graph construction for IT MOOCs, enhancing useful and semantically enriched knowledge resources for question-answering systems, learning advisory platforms, and IT educational applications.