Personalized E-Learning Assistant Using Knowledge Graph and LangGraph-Orchestrated Multi-Agent Framework
Chitresh Gyanani · International Journal for Research in Applied Science and Engineering Technology · 2025
The design of an e-learning system incorporating a personalized instructional approach, surpassing conventional pedagogical methodologies, is presented in this study. Many adaptive tutorial systems don't consider students' existing knowledge and past learning well enough. To fix this, the system uses knowledge graphs and multi-agent systems. The main focus is to build an academic system that can record students' likes learning styles, and grades in a clear way. A semantic graph model has been set up in Neo4j to store and manage this data. The system uses modular agents run by Lang Graph, to work with the knowledge graph. This allows it to change learning paths, teaching content, and make extra materials that fit each student. As students use the system more and take quizzes, each round of learning gets better to stay relevant and teach well. Adding Django makes the system more flexible and able to work with different front-end apps. This approach stands out because it's forward-thinking, can change, and aims to make learning better over time making it a strong choice compared to other options.