Dialogical Learning Support in RAG-Based E-Learning

This paper presents a web-based learning platform built on a Retrieval-Augmented Generation (RAG) architecture, designed to support dialogical learning. Rather than treating learner questions as isolated inputs, the platform views learning as an ongoing dialogue, preserving context across interactions and grounding responses in curated and validated educational materials. The system uses a modular web-based design that clearly separates content management, retrieval and generation, and dialogue handling. This modularity enables the integration of various generative models – both open-source and commercial – and supports deployment in real e-learning environments without requiring local installation. A representative use case demonstrates how the platform can support learning at the university level. Overall, the study shows how dialogically grounded RAG-based systems can improve transparency, contextual coherence, and pedagogical value in AI-supported e-learning.