An AI-Powered Chatbot for Enhancing Learner-Centered Support in Moodle-Based Learning Environments

Learning Management Systems (LMS) such as Moodle are widely adopted in higher education; however, they primarily function as structured repositories requiring manual navigation. This limits learner interaction and creates inefficiencies in accessing relevant content, particularly in large-scale courses. This paper presents a learner-centered conversational AI framework that enables natural language interaction and context-aware content retrieval within LMS environments. The proposed system adopts a Retrieval-Augmented Generation (RAG) architecture that combines semantic search over course materials with large language model–based response generation. Course content is transformed into vector embeddings and indexed within a Qdrant database, enabling retrieval based on semantic similarity rather than keyword matching. The architecture integrates modular components including query processing, embedding generation, semantic retrieval, and response generation. A scenario-based evaluation demonstrates reduced navigation effort, faster access to information, and improved interaction simplicity. Conversational AI can significantly enhance usability and accessibility in LMS platforms, supporting more intuitive and learner-centered digital learning environments.