Research on the Dynamic Adjustment of College Curriculum Content and Innovative Discipline Development Models Driven by Intelligent Education Systems
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Abstract
Against the dual background of digital transformation in education and the construction of new engineering and liberal arts disciplines, the lagging update of university curriculum content and rigid disciplinary development models increasingly restrict the cultivation of interdisciplinary engineering talents. This problem is particularly relevant to fast-evolving fields such as advanced electromagnetics, electromagnetic waves, antennas, and propagation, where curriculum systems must respond rapidly to technological iteration and industry demand. Based on big data analysis, artificial intelligence, and knowledge graph technology, this study designs a dynamic adjustment mechanism for university course content and an innovative discipline development model driven by intelligent education systems. The study diagnoses current problems through questionnaires, interviews, and document analysis, and then constructs a closed-loop framework including demand perception, content adaptation, dynamic update, and effect feedback. Industry demand data, student learning behavior, disciplinary trend data, and teaching process data are integrated to support curriculum optimization and interdisciplinary resource allocation. Empirical research across five universities verifies that intelligent education systems can improve curriculum responsiveness, enhance the fit between learning content and competence requirements, and support open, collaborative, and data-driven disciplinary development. The framework provides a practical reference for engineering education reform in advanced electromagnetic and related technical disciplines.
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