Year: 2026
Type: article
Abstract: This study aims to evaluate the performance of artificial intelligence language models in the Industrial Engineer Certification Examination and to analyze their accuracy across different subjects. A total of 750 past exam questions (from 2019 to 2024) across five subjects, namely Quality Management, Production and Operations Management, Operations Research, Engineering Economics, and Human Factors Engineering, were tested using ChatGPT-3.5 and ChatGPT-4o. The models’ responses were compared with standard answers to compute accuracy rates and identify performance differences. Additionally, the Chain of Thought (CoT) and Tree of Thought (ToT) reasoning frameworks were applied to incorrect responses from GPT-4o to assess improvements in reasoning. The results show that ChatGPT-4o achieved significantly higher accuracy and reasoning coherence than ChatGPT-3.5, particularly in Quality Management and Production Management. Overall, this study demonstrates the potential of generative AI as an effective tool for professional education and exam preparation, offering insights for future model optimization and educational integration.
DOI: 10.14419/vet88k67
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W7119680196
FWCI:
Cites:
Cited by:
Related to: