Data and language intelligence: Revolution in education

The present work explores the application of Large-Scale Language Models (LLMs), Augmented Data Retrieval (RAG), and Knowledge Graphs (KGs) in the educational context. The objective is to discuss the definitions, components, and functioning of these technologies, as well as their integration to optimize teaching results. LLMs are models that understand and generate text naturally, using deep neural networks. RAG combines data retrieval with text generation, providing more accurate and contextual responses. KGs structure and connect information graphically, facilitating the representation of knowledge. The methodology used includes the analysis of the combination of these technologies, where LLMs generate text, RAG retrieves data from KGs, and generates detailed responses, enriching the content with structured information. Practical applications in education include virtual assistants that provide personalized responses, research platforms that use RAG and KGs to present relevant data, and tutoring tools that offer personalized feedback. Key benefits include personalization and efficiency in accessing structured information, while challenges involve data quality, privacy, and integration complexity. Final considerations indicate that these technologies are transforming education, with future trends aimed at more interactive and personalized educational environments.