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Ontology-Based Learning Assistant Chatbot: Enhancing Accurate and Explanatory Knowledge Provision in Myanmar’s Primary Education
Author(s)
Myo, Su Wai
Anutariya, Chutiporn
Date Issued
January 1, 2025
Type
Conference Paper
Abstract
Large language models (LLMs) and LLM-based generative AI tools have demonstrated considerable effectiveness in educational settings by challenging traditional classroom dynamics. They generate answers based on knowledge acquired during pre-training, making the answer construction process and the sources of information ambiguous. This uncertainty in responses complicates the assurance of appropriateness and reliability for young students, particularly in primary education. This paper, therefore, proposes an ontology-based learning assistant chatbot designed to address students’ inquiries using Subject Ontology (SO), which was developed for primary school teachers to model and verify subject knowledge. The chatbot aims to alleviate common academic challenges in Myanmar’s primary education. From the evaluation, teachers valued the chatbot’s transparency and reliability, as they could maintain direct control over the underlying knowledge base, enabling them to efficiently verify the accuracy of the chatbot’s responses and their sources.
Citation
Lecture Notes in Networks and Systems, 1390 LNNS, 102-112, 2025