Semantic AI-Driven Knowledge Networks for Enhancing Linguistic Competence in Educational Management

Rapid information growth and digital transformation demand advanced educational management systems to foster more effective learning. This study explores the role of semantic artificial intelligence (AI)-driven knowledge networks in enhancing linguistic competence within educational management. By leveraging AI's semantic capabilities, such networks organize, analyze, and visualize linguistic data to support deeper knowledge sharing and language development. This interdisciplinary research empirically investigates the approach. A semester-long intervention involved 69 Chinese junior students majoring in linguistics, who engaged with an AI-assisted education management system equipped with tools, such as Gephi, WORDij, and mathematical software. Triangulated data from linguistic assessments, structured interviews, and semantic network analysis demonstrates notable improvements in students' language proficiency and conceptual understanding. The findings suggest that semantic AI-driven knowledge networks hold significant potential in advancing linguistic competence and optimizing educational management practices.