Constructing a Multidimensional, Integrated, and Collaborative Education System for Cultivating New-Quality Talent in Painting Majors in the Era of AI Teachers
DOI:
https://doi.org/10.62177/jetp.v3i3.1443Keywords:
AI Era, Painting Major, Multidimensional Integration, Collaborative Education, New-Quality Talent CultivationAbstract
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.
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References
Chen, L. M. (2025). Innovative pathways for empowering “new business” talent cultivation through digital intelligence. Contemporary Educational Theory and Practice, 17(5), 17–21.
Dong, C. F., Fang, J. S., & Pan, W. F. (2025). Five-dimensional integration and empowerment: Innovative practice of the evaluation system for art design education in the era of artificial intelligence—A case study of the School of Art and Design, Guangdong Baiyun University. Art Education Research, (19), 135–137.
Wang, K., & Ma, G. F. (2023). Exploration of the university–enterprise collaborative education model based on industrial colleges. Knowledge Window (Teacher Edition), (7), 87–89.
Amabile, T. M. (1996). Creativity in context: Update to the social psychology of creativity. Westview Press.
Boden, M. A. (1998). Creativity and artificial intelligence. Artificial Intelligence, 103(1–2), 347–356. https://doi.org/10.1016/S0004-3702(98)00055-1
Cetinic, E., & She, J. (2022). Understanding and creating art with AI: Review and outlook. ACM Transactions on Multimedia Computing, Communications, and Applications, 18(2), 1–22. https://doi.org/10.1145/3475799
Colton, S., & Wiggins, G. A. (2012). Computational creativity: The final frontier? In ECAI 2012: 20th European Conference on Artificial Intelligence (pp. 21–26). IOS Press.
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018). Generative adversarial networks: An overview. IEEE Signal Processing Magazine, 35(1), 53–65. https://doi.org/10.1109/MSP.2017.2765202
Darling-Hammond, L., Hyler, M. E., & Gardner, M. (2017). Effective teacher professional development. Learning Policy Institute.
Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). CAN: Creative Adversarial Networks, generating “art” by learning about styles and deviating from style norms. In Proceedings of the 8th International Conference on Computational Creativity (pp. 96–103).
Epstein, Z., Hertzmann, A., Herman, L., Mahari, R., Frank, M. R., Groh, M., Schroeder, H., Smith, A., Akten, M., Fjeld, J., Farid, H., Leach, N., Pentland, A., & Russakovsky, O. (2023). Art and the science of generative AI. Science, 380(6650), 1110–1111. https://doi.org/10.1126/science.adh4451
Gillotte, J. (2020). Copyright infringement in AI-generated artworks. UC Davis Law Review, 53, 2655–2691.
Guadamuz, A. (2017). Do androids dream of electric copyright? Comparative analysis of originality in artificial intelligence generated works. Intellectual Property Quarterly, (2), 169–186.
Hertzmann, A. (2018). Can computers create art? Arts, 7(2), Article 18. https://doi.org/10.3390/arts7020018
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Kadel, R., Mishra, B. K., Shailendra, S., Abid, S., Rani, M., & Mahato, S. P. (2024). Crafting tomorrow’s evaluations: Assessment design strategies in the era of generative AI. arXiv. https://arxiv.org/abs/2405.01805
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
Manovich, L. (2018). AI aesthetics. Strelka Press.
McCormack, J., Bown, O., Dorin, A., McCabe, J., Monro, G., & Whitelaw, M. (2014). Ten questions concerning generative computer art. Leonardo, 47(2), 135–141. https://doi.org/10.1162/LEON_a_00533
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0
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Copyright (c) 2026 Zhaoxia Yu, Yongqi Pang

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
DATE
Accepted: 2026-05-25
Published: 2026-07-09








