AI Engine for Engineering Talent Development: Automated Competency Graph Modeling and Validation

Universities and enterprises are using AI for engineering education and technical talent cultivation. For better education, we developed the competency engineering engine (CEE), an AI framework that models and validates engineering competencies in integrated circuit electromagnetic susceptibility (IC-EMS). CEE adopts natural language processing, knowledge graphs, and automated routing into engineering workflows to learn technical standards, tokenize texts, and build graphs linking electromagnetic theory to electronic design automation. Students can learn adaptive pathways in foundational, integrated, and advanced research tiers. CEE can enhance reliability, accuracy, and simulation performance in engineering education. Learners improved their problem analysis ability by 19.4%, simulation optimization ability by 21.7%, data-integration accuracy by 20.6%, and system integration level by 17.2% when using CEE. Student engagement increased by 20%, and lab participation by 19% when using electronic design automation (EDA). CEE can be used to effectively adopt AI technology to learn how to automate competency modeling and accelerate skill acquisition. Through an integration with open-source EDA and reinforcement learning agents, CEE can be used for adaptive learning to adapt to rapidly advancing engineering technologies.