Exploration of Paths for Artificial Intelligence to Empower Civil Affairs Vocational Education

Abstract

The evolution of artificial intelligence technology is driving profound changes in the forms of vocational education. Civil affairs vocational education, due to the composite nature of its knowledge system and the contextual characteristics of its job situations, imposes differentiated requirements on the embedding methods of intelligent technologies. This study explores the paths for artificial intelligence to empower civil affairs vocational education from three dimensions: the reconstruction of technology embedding and educational ecosystem adaptability, the construction of intelligent collaborative models among teaching subjects, and the coupling mechanism between training objectives and intelligent technologies. At the level of technology embedding, the study analyzes the mapping relationships between the technological spectrum-including machine learning, natural language processing, and knowledge graphs-and teaching scenarios, and proposes a resource matching optimization mechanism based on semantic fusion. At the level of subject collaboration, the study constructs a framework that includes adaptive path generation for learners, intelligent decision support for instructors, and knowledge discovery tools for researchers. At the level of objective coupling, the study explores the intelligent construction of vocational competency profiles, the modality transformation mechanism of skill training, and the value transformation path of educational technology investment. This research aims to provide a systematic theoretical reference for the intelligent transformation of civil affairs vocational education.