Strategic Framework for Evaluating Curriculum-Job Fit via Knowledge-Injected Large Language Models

Abstract

This study proposes and empirically validates a large-language-model(LLM) driven framework that aligns undergraduate majors and course portfolios with the competency requirements of specific job roles. Using Samsung Electronics' Device Solutions division as a testbed, we analyzed two highly representative semiconductor positions System LSI circuit-architecture design and R&D Center process-architecture integration because they respectively demand the narrowest and broadest disciplinary scopes in the industry. The 4o LLM was sequentially prompted with structured job descriptions, 6 STEM majors at KAIST, and 248 associated courses. A two-tier evaluation quantified major-level (Level 1) and course-level (Level 2) relevance both before and after Knowledge Injection (KI) of the 'Recommended Subject' field in each job description. KI reinforced Electrical Engineering's dominance in circuit design by + 1.1% total score but markedly broadened multidisciplinary suitability for process integration by + 7.7%. As a result, Mechanical Engineering and Chemistry were elevated into a higher relevance tier according to the evaluation criteria. At the course level, all Recommended Subjects and their advanced courses consistently ranked among the highest within each major, and they were found to be most susceptible to the effects of KI. This confirms their utility as anchors for automated job education mapping. The results demonstrate that KI granularity should mirror role characteristics core-focused for design-centric jobs and dispersive for integration-oriented jobs. Beyond extending LLM use to curriculum design and hiring-fit assessment, the framework offers a scalable pathway for universities, firms, and policymakers to co-engineer evidence-based talent pipelines and mitigate looming bachelor-level workforce shortages in semiconductors.