Breaking Walls and Reconstructing: Practical Exploration of AIGC Empowering Non-Art Professional Aesthetic Education Courses

Authors

  • Chen Guangyu Kuala Lumpur University of Science and Technology, Jalan Ikram-Uniten,43000 Kajang, Selangor, Malaysia
  • Zahari Bin Ishak Kuala Lumpur University of Science and Technology, Jalan Ikram-Uniten,43000 Kajang, Selangor, Malaysia

DOI:

https://doi.org/10.66238/fsrma132

Keywords:

AIGC technology, Non-art majors, Aesthetic education, Practical application

Abstract

In the context of intelligent technology deeply embedded in the educational ecosystem, AIGC (Artificial Intelligence Generated Content) technology has brought profound transformation from concept to form in aesthetic education courses for non-art majors. This study approaches from the practical perspective of frontline university teachers, adopts qualitative research methods, and systematically examines the application of AIGC technology in aesthetic education courses for non-art majors through in-depth interviews with 12 instructors and participatory observation of four courses.The study found that the intervention of AIGC solved the long-standing structural dilemma of non-art students facing "strong aesthetic perception but weak creative expression" through "low-threshold creation," promoting the transformation of teaching from knowledge lecturing to practical generation. On this basis, this paper extracts three practical mechanisms: "pre-positioning aesthetic judgment," "process-based aesthetic dialogue," and "interdisciplinary aesthetic transfer," and proposes countermeasures to address risks such as the erosion of aesthetic subjectivity and narrowed aesthetic perception in technological applications, hoping to provide references for the practical transformation of aesthetic education in universities in the intelligent era.

References

Cao, H. (2026). The transformation of traditional Chinese painting in the digital art wave: The impact of AIGC and virtual reality. Frontiers in Virtual Reality, 6. https://doi.org/10.3389/frvir.2025.1716174

Hagman, G. (2025). The Psychoanalysis of Aesthetic Experience: Self, Relationship and Culture. Taylor & Francis.

Han, Y. M. (2026). The Cultivation of Innovative Modeling Design Ability in the Teaching of Art Courses with the Help of AI Technology. Advanced Electromagnetics, 15(3), 3879–3887. https://doi.org/10.7716/aem.v15i3.3448

Kanchon, Md. K. H., Sadman, M., Nabila, K. F., Tarannum, R., & Khan, R. (2024). Enhancing personalized learning: AI-driven identification of learning styles and content modification strategies. International Journal of Cognitive Computing in Engineering, 5, 269–278. https://doi.org/10.1016/j.ijcce.2024.06.002

Pan, J., Wu, E., & Li, G. (2026). Chasing the unseen real: The birth of a Xianniang animation and the post-Qualitative inquiry awakening. Visual Studies, 41(1), 115–127. https://doi.org/10.1080/1472586X.2025.2546357

Qi, S., & Peh, S. S. (2026). The Internal Logic and Practical Pathways of the Development of Aesthetic Education in Universities in the Era of Artificial Intelligence: From the Perspective of AIGC Empowerment. Journal of Literature Advances, 3(1). https://doi.org/10.26855/jla.2026.06.008

Sandhiya, V., & Bhuvaneswari, M. (2025). Qualitative Research Analysis: A Thematic Approach. In Design and Validation of Research Tools and Methodologies (pp. 289–310). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-1135-6.ch014

Tao, Y., & Wang, Z. (2026). The Position and Challenges of AIGC-Assisted Design in Modern Art and Design Education. Journal of Cases on Information Technology (JCIT), 28(1), 1–19. https://doi.org/10.4018/JCIT.401354

The Archive of Silence(s): Redistributing Archival Power Through Physical Spaces in Pretoria - ProQuest. (n.d.). Retrieved 7 September 2026, from https://www.proquest.com/openview/70ecf90df3b593c31546e946c47c9f4c/1?pq-origsite=gscholar&cbl=2026366&diss=y

Xiao, J., Wang, Z., Yu, Z., & Xiaocong, D. (2026). Exploration of an AIGC-Driven Intelligent Course Design and Automated Teaching Resource Generation Model. International Journal of High Speed Electronics and Systems, 35(05), 2540777. https://doi.org/10.1142/S0129156425407776

Zhao, X. (2025). Breakthroughs, lags, and contradictions: An analysis of the practices and effects of new media art aesthetic education in China. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1483559

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Published

2026-09-30