Constructing a Multidimensional, Integrated, and Collaborative Education System for Cultivating New-Quality Talent in Painting Majors in the Era of AI Teachers - Yu, Zhaoxia; Pang, Yongqi (Journal of Educational Theory and Practice, 2026)
The rapid iteration of generative artificial intelligence has reshaped the creative modes, aesthetic forms, and industrial ecosystem of the traditional painting industry. The conventional talent cultivation model in painting programs, which emphasizes technical training while paying insufficient attention to innovation and interdisciplinary integration, can no longer meet the developmental needs of the art industry in the AI era. New-quality talent emphasizes creativity and innovation, human–AI collaborative competence, interdisciplinary integration literacy, and industry adaptability, and has become a core orientation for talent cultivation in painting programs at higher education institutions. Against the backdrop of AI-enabled art education, this paper takes multidimensional integration and collaborative education as its central approach, focuses on frontline teaching reform practices, and draws on real cases of curriculum reform, project-based training, and industry–education collaboration in university painting programs. From multiple dimensions, including curriculum integration, teacher–student collaboration, industry–education linkage, and the integration of scientific and technological innovation, this study explores pathways for constructing a new-quality talent cultivation system for painting majors. It aims to address such pain points in traditional painting education as homogenized teaching, rigid skill training, and insufficient innovation, thereby providing practical and implementable references for the digital transformation and high-quality talent cultivation of painting programs in higher education.