Xuan-Quy Dao

Work place: School of Engineering, Eastern International University, Binh Duong, Vietnam

E-mail: quy.dao@eiu.edu.vn


Research Interests: Technology Enhanced Learning, Artificial Intelligence and Applications


Xuan-Quy Dao obtained the Ph.D. degree in signal processing and telecommunication at the French National Institute for Research in Computer Science and Automation in 2014. He received both the M.S. and B.S. degrees in automation engineering from the Grenoble Institute of Technology in France in 2010.
He is currently a lecturer at the Eastern International University in Vietnam. From 2015 to 2017, he was a lecturer at Quang Binh University in Dong Hoi, Vietnam. His research interests include the development Online Learning Technology, Teaching-based Artificial Intelligence, Voice Cloning, and Speech-driven Face.

Author Articles
LLMs Performance on Vietnamese High School Biology Examination

By Xuan-Quy Dao Ngoc-Bich Le

DOI: https://doi.org/10.5815/ijmecs.2023.06.02, Pub. Date: 8 Dec. 2023

Large Language Models (LLMs) have received significant attention due to their potential to transform the field of education and assessment through the provision of automated responses to a diverse range of inquiries. The objective of this research is to examine the efficacy of three LLMs - ChatGPT, BingChat, and Bard - in relation to their performance on the Vietnamese High School Biology Examination dataset. This dataset consists of a wide range of biology questions that vary in difficulty and context. By conducting a thorough analysis, we are able to reveal the merits and drawbacks of each LLM, thereby providing valuable insights for their successful incorporation into educational platforms. This study examines the proficiency of LLMs in various levels of questioning, namely Knowledge, Comprehension, Application, and High Application. The findings of the study reveal complex and subtle patterns in performance. The versatility of ChatGPT is evident as it showcases potential across multiple levels. Nevertheless, it encounters difficulties in maintaining consistency and effectively addressing complex application queries. BingChat and Bard demonstrate strong performance in tasks related to factual recall, comprehension, and interpretation, indicating their effectiveness in facilitating fundamental learning. Additional investigation encompasses educational environments. The analysis indicates that the utilization of BingChat and Bard has the potential to augment factual and comprehension learning experiences. However, it is crucial to acknowledge the indispensable significance of human expertise in tackling complex application inquiries. The research conducted emphasizes the importance of adopting a well-rounded approach to the integration of LLMs, taking into account their capabilities while also recognizing their limitations. The refinement of LLM capabilities and the resolution of challenges in addressing advanced application scenarios can be achieved through collaboration among educators, developers, and AI researchers.

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