From Textbook to Talkbot: A Case Study of a Greek-Language RAG-Based Chatbot in Higher Education

TL;DR

This paper explores the design and application of a Greek-language RAG-based AI chatbot in higher education, demonstrating its potential to improve educational support, content creation, and learner autonomy.

Contribution

It introduces a novel Greek-language RAG chatbot tailored for higher education, addressing linguistic challenges and enhancing reliability in educational contexts.

Findings

RAG architecture improves response accuracy and context-awareness.

The chatbot supports both students and educators effectively.

Positive usability and reliability feedback from initial evaluations.

Abstract

The integration of AI chatbots into educational settings has opened new pathways for transforming teaching and learning, offering enhanced support to both educators and learners. This study investigates the design and application of an AI chatbot as an educational tool in higher education. Designed to operate in the Greek language, the chatbot addresses linguistic challenges unique to Greek while delivering accurate, context grounded support aligned with the curriculum. The AI chatbot is built on the Retrieval Augmented Generation (RAG) framework by grounding its responses in specific course content. RAG architecture significantly enhances the chatbots reliability by providing accurate, context-aware responses while mitigating common challenges associated with large language models (LLMs), such as hallucinations and misinformation. The AI chatbot serves a dual purpose: it enables students to access accurate, ondemand academic support and assists educators in the rapid creation of relevant educational materials. This dual functionality promotes learner autonomy and streamlines the instructional design process. The study aims to evaluate the effectiveness, reliability, and perceived usability of RAG based chatbots in higher education, exploring their potential to enhance educational practices and outcomes as well as supporting the broader adoption of AI technologies in language specific educational contexts. Findings from this research are expected to contribute to the emerging field of AI driven education by demonstrating how intelligent systems can be effectively aligned with pedagogical goals.