Construction of a Dynamic Update Mechanism for the Loose-Leaf Textbook “Community Nursing Practice” Based on AI and Big Data Analysis, Adapting to “On-the-Job, Course, Competition, Certification, and Innovation” Model
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Abstract
With the evolution of community nursing service models and vocational education reform, the static content of traditional textbooks can no longer respond effectively to rapid changes in job competence requirements. Taking the loose-leaf textbook Community Nursing Practice as the carrier, this study constructs a dynamic update mechanism adapted to the “job-course-competition-certification-innovation” model based on AI and big data analysis. Multisource data are collected and integrated, including job competency mapping data, skills competition data, vocational qualification certificate data, and innovation and entrepreneurship project data. Natural language processing and machine learning algorithms are used to identify competency clusters, map textbook units to job requirements, analyze competition and certification content, and mine emerging innovation demands. The mechanism supports scientific matching and dynamic iteration between textbook knowledge and industry requirements. Results show that the proposed mechanism improves the timeliness and adaptability of textbook content, especially in emerging competency areas such as intelligent health monitoring, data management, and integrated community nursing tasks. The study provides an operable technical path for digital transformation of vocational textbooks.
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