GenAI in the Digital Transformation of Vocational Education: Applications, Challenges, and Development Strategies

Abstract

Abstract The rapid advancement of Generative Artificial Intelligence (GenAI) is precipitating a profound digital transformation in vocational education. Building upon existing digital infrastructures, GenAI functions as an enabling cognitive technology that reshapes teaching paradigms, skill training processes, and talent cultivation objectives. This study systematically examines the applications of GenAI in vocational education, including personalized learning and adaptive skill development, intelligent support for complex technical operations, and AI-enhanced instructional design and assessment. In particular, the paper highlights GenAI’s capacity to bridge foundational knowledge gaps and promote the cultivation of “Skills + R&D” integrated talent by empowering vocational students to engage in technical optimization, data-driven problem-solving, and innovation-oriented practices. At the same time, the deployment of GenAI introduces context-specific challenges in vocational education, notably AI-facilitated technological plagiarism, the erosion of practical reasoning and diagnostic competence, and heightened risks of information leakage in industry-embedded training environments. To address these issues, the study proposes three targeted countermeasures: governance-oriented regulatory frameworks for ethical and transparent GenAI use, a collaborative instructional model integrating “GenAI + Practical Reasoning”, and industry-aligned information security safeguards emphasizing on-premises deployment and data protection. Collectively, these strategies provide a structured reference for the responsible and sustainable integration of GenAI into vocational education, supporting high-quality digital transformation while preserving skill authenticity, cognitive development, and industrial confidentiality.

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