This paper presents ABEL (Artificial Buddy for Effective Learning), a modular, knowledge-graph-driven chatbot designed to enhance and support education in Data Science and Artificial Intelligence. At the core of ABEL, is a hybrid retrieval architecture that integrates a dynamic Knowledge Graph and a Retrieval-Augmented Generation (RAG) pipeline. The knowledge graph, constructed from curated learning resources, enables a concept-driven retrieval of semantically relevant and specific educational content through multi-hop graph queries and embedding-based similarity search. This approach enhances the contextual grounding and supports the generation of personalized, specific, and explainable responses by Large Language Models (LLMs). ABEL is also complemented by a Frequently Asked Questions (FAQ)-based RAG approach, thus offering flexible access to learning content while ensuring traceability and correctness. We present the system’s architecture, evaluate its performance using both retrieval and user-based metrics, and discuss the benefits of combining symbolic graph structures. Our results demonstrate that this approach can significantly improve the relevance and adaptability of chatbot-driven learning platforms.