SLR-Thematic Comparison of key Literature in LLM and KG Integration for Education.xlsx

This dataset contains the detailed data extraction matrix from a systematic literature review (SLR) investigating the integration of Large Language Models (LLMs), Knowledge Graphs (KGs), and Retrieval-Augmented Generation (RAG) for personalized learning applications in education. The review systematically analyzed a core set of 18 seminal and state-of-the-art papers published between 2017 and 2025. Each entry in the dataset deconstructs a research paper according to its primary described application, its methodology for prerequisite identification or learning path generation, the specific roles of the LLM and KG within its architecture, its reported challenges and limitations, and its key contribution or finding.This dataset is a valuable resource for researchers, students, and practitioners in the fields of AI in Education, Educational Technology, and Human-Computer Interaction who are seeking a structured and detailed overview of this rapidly evolving research area.