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Dinh Van Tran

Work place: Department of Mechanical Engineering, East Asia University of Technology, 100000, Hanoi, Vietnam

E-mail: trandinhvan1221@gmail.com

Website: https://orcid.org/0000-0001-9348-8890

Research Interests:

Biography

Dinh Van Tran was born in Binh Ngoc, Tuy Hoa, Phu Yen, Vietnam, on February 25, 1996. He received the bachelor’s degree in mechanical engineering from Moscow Polytech University, Moscow, Russian Federation, in 2019, and the master’s degree in mechanical engineering from Moscow Polytech University, Moscow, Russian Federation, in 2021. His major field of study is mechanical engineering.
He worked as a Research Assistant and Teaching Assistant in the Department of Mechanical Engineering at Bauman Moscow State Technical University from 2019 to 2021. From 2021 to 2022, he served as a Design Solutions Engineer at Vietnam Auto Solutions. Since March 2022, he has been a Lecturer with the Department of Mechanical Engineering, East Asia University of Technology, Hanoi, Vietnam. He has authored and co-authored several scientific publications in the fields of robotic assembly, mechanical system modeling, and sensor-based automation, including ―Analysis of Jamming Conditions in Robotic Assembly Using the Force-Torque Sensor‖ (MSTU STANKIN, 2021) and ―Study Robotic Assembly Conditions of Profile Shafts‖ (Lecture Notes in Mechanical Engineering, 2022). His current research interests include robotic assembly systems, force–torque sensing applications, intelligent manufacturing, sheet metal forming, and AI-assisted educational quality management.
Mr. Dinh Van is actively involved in academic research and curriculum development in mechanical engineering. He serves as a reviewer and contributor to institutional research projects and educational quality assurance initiatives.

Author Articles
AI-Assisted Evaluation of Course Learning Outcomes and Program Quality Management in Automotive Engineering Education

By Dinh Van Tran Van Truong Chu Minh Vu Hoang

DOI: https://doi.org/10.5815/ijmecs.2026.02.07, Pub. Date: 8 Apr. 2026

Consistent and objective assessment of Course Learning Outcomes remains a challenge in every engineering program. This paper develops EAUT-OBE, an AI-supported system that utilises OCR, Vietnamese NLP, and Bloom's Taxonomy classification to extract, categorize, and map CLOs to Program Learning Outcomes across the entire Automotive Engineering program at East Asia University of Technology. Using 71 preprocessed syllabi, the system extracted 301 CLOs, which were mapped to 12 PLOs. The EAUT-OBE system was developed on and fine-tuned with the GPT-OSS-20B, resulting in approximately 91% accuracy in Bloom-level classification. It also reduced processing time by about 85%, compared to the baseline models PhoGPT-4B and EraX-7B. The results indicated better curriculum transparency and the achievement of accreditation and consistency in staff evaluation. Limitations could be due to OCR quality and dataset scale. Future work will expand the OBE dataset in Vietnamese and integrate predictive learning analytics.

[...] Read more.
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