An Analysis of Knowledge Organization and Semantic Processing of Educational Texts Using Artificial Intelligence Technologies

Abstract

Educational institutions produce vast amounts of text-based resources such as textbooks, courseware, lectures, syllabi, digital learning resources, etc., posing problems related to effective knowledge organization, searching and semantically analyzing those. The conventional text organization techniques tend to be incapable of capturing complicated conceptual relations, context-dependent meanings and instructional structure inherent in the large educational corpus. This paper will examine the part that artificial intelligence (AI) plays in knowledge organization and semantically analyzing educational texts using a comprehensive conceptual framework. The framework proposed here contains a four-layer system with the following layers: data acquisition & preprocessing, semantic features extraction, knowledge organization, and intelligent application. The proposed framework incorporates natural language processing, ontology-based knowledge representation, knowledge graph, and deep learning for semantic analysis for efficient educational content management. The comparison of symbolic ontology-based approaches, statistical embedding-based approaches, and hybrid approaches is provided with regard to interpretability, scalability, semantic understanding, maintenance, ability to map to the curriculum, and explainability. The study shows that ontology-based systems exhibit interpretability and structured knowledge representation, while embedding-based systems show better scalability and context comprehension. Hybrid systems are able to leverage the benefits of both approaches through integration of deep learning-enabled semantic extraction with structured knowledge representations. These results demonstrate that hybrid AI-enabled architectures can be seen as a feasible way to build scalable, interpretable, and semantically coherent educational knowledge management systems. Some future directions of research on the topic have been also outlined, such as multilingual processing of educational text, interpretable semantic models based on AI, privacy-preserving knowledge management, and empirical evaluation on big educational data sets.