A Practice-Based Study of the Digital and Intelligent Upgrading of a Big Data Technology Specialty Cluster

Abstract

The growing integration of artificial intelligence into shipping is reshaping the occupations served by big data technology specialty clusters, bringing together data processing, intelligent analysis, and industry applications. To address gaps between AI competency development and occupational tasks, weak connections between curricula and industry scenarios, and insufficient support for faculty capabilities and teaching resources, this study combines literature analysis with a case study of Jiangsu Maritime Institute to examine how AI can be integrated into specialty cluster development. The practice restructures competencies around typical smart shipping tasks, establishes a three-tier curriculum progressing from digital and AI literacy to AI algorithms and scenario-based applications and then to integrated industry innovation, and develops faculty AI application and instructional design capabilities through the Three Engagements initiative. Human–AI collaboration is explored in lesson preparation, instruction, assessment, and teaching management. The study argues that upgrading should connect AI learning, industry task practice, and instructional improvement. Scenario-based design, task restructuring, differentiated implementation, and continuous assessment can support students in developing the ability to apply digital and intelligent technologies in smart shipping.