Research on the Development Mechanisms and Implementation Pathways of AI-Empowered Productive Teaching Projects

Abstract

Against the backdrop of deepening industry-education integration in vocational education and AI-driven digital transformation, productive teaching projects face not only the perennial challenge of transforming authentic production tasks into teachable activities, but also new challenges such as rapid resource obsolescence, heterogeneous learner foundations, and insufficient evidence for process-based assessment. Taking the “Deployment and Maintenance of a Navigation Data Storage Platform” task from the Yangtze River Trunk-Line Ship Water Pollutant Joint Supervision and Service Information System as a case, this study builds on the six-stage process of Select→Deconstruct→Define→Transform→Integrate→Extend and develops a human–AI collaborative mechanism characterized by real projects as the carrier, job competencies as the main line, and AI-supported cognition as the support. To strengthen AI empowerment across project development and implementation, AI is embedded in project selection, work-process deconstruction, competency definition, teaching-task transformation, curriculum integration, and resource expansion, and is integrated with IDEAL project-based learning, multi-stakeholder whole-process assessment, “dual-teacher + AI assistant” collaboration, and a blended virtual–real training environment. This study provides a reference for IT-related programs seeking to build replicable and iteratively improvable AI-empowered productive teaching projects.