Work place: Asia Metropolitan University (AMU), 63000 Cyberjaya, Selangor
E-mail: truongchu08@gmail.com
Website: https://orcid.org/0009-0009-4843-9868
Research Interests:
Biography
Van Truong Chu was born in Bac Ninh, Vietnam, on December 8, 1995. He received the Master of Business Administration from Swiss UMEF, Geneva, Switzerland, in management sciences. He is currently pursuing the Ph.D. degree in Management at Kuala Lumpur Metropolitan University College, Kuala Lumpur, Malaysia, under the Centre for Postgraduate Studies. His major field of study is management and organizational studies.
He has been actively engaged in interdisciplinary research spanning management science, educational technology, and industrial systems innovation. His recent publications include ―Use of Virtual Reality for Inclusive Education: Assessing the Availability and Adaptation of Educational Materials‖ (Premier Journal of Science, 2025), ―Examining Measurement Invariance of a C-Test Across Gender Using Multiple-Group Item Response Theory‖ (International Journal of Language Testing, 2025), and ―Deep Learning Enhanced Predictive Maintenance Framework Using Industrial Internet of Things Sensors for Smart Manufacturing Systems‖ (International Journal of Industrial Engineering and Management, 2025). His research interests include educational innovation, artificial intelligence applications in management, predictive maintenance systems, industrial Internet of Things, and digital transformation in higher education.
Mr. Van Truong is currently a Ph.D. Candidate in Management at Kuala Lumpur Metropolitan University College, Kuala Lumpur, Malaysia. He actively contributes to interdisciplinary research initiatives and scholarly publications in management, education, and smart manufacturing systems.
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.Subscribe to receive issue release notifications and newsletters from MECS Press journals