Personalised AI-Driven Conversational Agents for Adaptive Self-learning

This paper presents an AI-driven conversational agent to support self- directed learning through a personalised pedagogical framework. Leveraging the Visual, Aural, Read/Write, and Kinesthetic (VARK) model, the system adapts edu- cational content to learners’ individual preferences. Retrieval-Augmented Genera- tion (RAG) enhances response accuracy and minimises hallucinations commonly associated with Large Language Models (LLMs). A user study compared the chatbot’s performance to a traditional search engine in engagement, usability, and learning outcomes. The search engine was selected as a baseline to con- trast conventional keyword-driven information retrieval with adaptive, dialogue- based guidance. Results indicate that personalised, AI-supported interactions fos- ter greater engagement and knowledge retention. The study highlights the potential and limitations of VARK-driven AI tools in personalised learning.