Systemic Reconstruction and Risk Governance of Higher Vocational Digital Art Education from the Perspective of Human-AI Collaboration

Abstract

Background: The rapid development of generative artificial intelligence has transformed digital art production towards human-AI collaborative workflows, creating new requirements for higher vocational digital art education. In the future, artificial intelligence will serve as a more efficient and innovative collaborative partner for human artists, with its development centred on fostering deep integration and innovation in human-machine co-creation. Specifically, this involves two key aspects: first, the development of more refined, interpretable, and controllable generative models that enable artists to transform creative concepts into digital works with greater precision; and second, the expansion of applications throughout the entire creative process, including artistic conception, style blending, interactive experiences, and critical evaluation, thereby continuously redefining and extending the boundaries and possibilities of digital art creation. This study focuses on how educational systems can adapt to these technological and industrial changes. Objective: This study aims to identify the major adaptability challenges facing higher vocational digital art education and to construct a systematic reconstruction framework from the perspective of human-AI collaboration. Methods: This study adopts a conceptual qualitative research approach based on literature analysis, policy interpretation, and theoretical framework construction. The analysis integrates perspectives from vocational education, industry-education integration, and AI governance studies. Results: The conceptual analysis identifies four major dimensions requiring transformation: skill supply, creative competency development, institutional regulation, and teacher capacity. On the basis of these dimensions, the study proposes corresponding strategies for curriculum reconstruction, collaborative teaching, practical training, and risk governance. Contribution: This study provides a theoretical framework for understanding the intelligent transformation of higher vocational digital art education and offers directions for future empirical investigation.