Empowering and Reshaping Vocational Education: A Theoretical Framework and Pathways for AI-Driven Transformation

Abstract

Against the backdrop that Artificial Intelligence (AI) is rapidly reshaping the ecosystem of Vocational Education (VE), the fast iteration of industrial skill demands has exposed deep-rooted bottlenecks in traditional VE models: outdated curricula, disjointed practical training, and structural mismatch between talent cultivation outputs and market needs. While governments across China and globally have rolled out intensive policy initiatives to advance VE digital transformation, existing studies mostly focus on scattered cases of ad-hoc technology application, lacking systematic analytical frameworks, with notable research gaps in inquiries into ethical risks and long-term collaborative governance mechanisms. This study aims to clarify the core operational logic of AI-empowered VE transformation, identify risk boundaries during the transition, develop actionable systematic solutions, and address the longstanding talent supply-demand mismatch. Adopting sociotechnical systems theory as its meta-framework, the study integrates mediation theory from educational technology and work process theory in vocational education to construct the Vocational Augmentation Education (VAE) model and the "technology-education-industry" double-helix analytical framework. Drawing on cross-sector empirical cases, it unpacks the internal mechanisms and practical barriers of VE transformation. The study finds that AI can systematically enhance VE efficacy through three core mechanisms—cognitive augmentation, contextual expansion, and step-change efficiency improvement—yet the transformation faces deep-seated risks including the polarizing effect of the digital divide. Accordingly, it proposes targeted transformation pathways across four dimensions: top-level policy design, deep industry-education collaboration, stakeholder capacity building, and embedded ethical governance, to provide theoretical references and evidence-based decision support for building a new human-AI collaborative VE paradigm.