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

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.