THE EMPOWERMENT OF ARTIFICIAL INTELLIGENCE MANAGEMENT FOR ART EDUCATION IN GUANGXI UNIVERSITIES
Article Sidebar
Main Article Content
บทคัดย่อ
The rapid development of artificial intelligence (AI) has accelerated the digital transformation of higher education and created new opportunities for art education governance. However, university art education continues to face challenges related to resource allocation, personalized learning support, and management effectiveness. This study aimed to: (1) study the current and desirable status of Artificial Intelligence Management for Art Education in Guangxi Universities; (2) analyze the priority needs of Artificial Intelligence Management for Art Education in Guangxi Universities; and (3) propose the Empowerment of Artificial Intelligence Management for Art Education in Guangxi Universities.
This mixed-methods research collected quantitative data from 500 administrators, faculty members, and students in Guangxi universities using a structured questionnaire and qualitative data from 10 experts through semi-structured interviews. Data were analyzed using frequency, percentage, mean, standard deviation, Modified Priority Needs Index (PNI Modified), and content analysis.
The research results found that:
- The overall desirable status of Artificial Intelligence Management for Art Education in Guangxi Universities was at a very high level (M = 4.520), whereas the current status was at a moderate level (M = 3.240).
- The overall PNI Modified value was 0.395, indicating a substantial gap between the current and desirable conditions. Personalized Learning Management, Ethical and Risk Governance, and Institutional Policy and Regulation were identified as the highest priority needs.
- A Five-Level Eight-Dimension Framework of Artificial Intelligence Management for Art Education in Guangxi Universities was proposed. The framework integrates strategic governance, institutional governance, academic management, developmental support, and ethical governance to enhance educational decision-making, personalized learning, faculty development, and responsible AI implementation.
Article Details
เอกสารอ้างอิง
Alekseeva, L. L., Stukalova, O. V., Beregovaya, E. B., Aryabkina, I. V., & Vladimirova, S. V. (2019). Art education and its impact effects on modern students. 10th International Conference on Education and Educational Psychology. https://www.europeanproceedings.com/article/10.15405/ep sbs.2019.09.02.8
Cardona, M. A., Rodríguez, R. J., & Ishmael, K. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. U.S. Department of Education, Office of Educational Technology. https://www. ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf
Ellis, B. (2024). How to protect student privacy when using AI [Blog post]. TCEA. https://blog.tcea.org/how-to-protect-student-privacy-when-using-ai/
Henry, J., & Maxwell, T. (2024). Harnessing AI for personalized learning: Innovations in EdTech and pedagogy. Education Sciences, 14(5), 123–138.
Herath, D., Dinuwan, C., Ihalagedara, C., & Ambegoda, T. (2024). Enhancing educational outcomes through AI-powered learning strategy recommendation system. International Journal of Advanced Computer Science and Applications, 15(10), 75–88.
Langeveldt, D. C. (2024). AI-driven leadership: A conceptual framework for educational decision-making in the AI era. European Humanities and Social Sciences, 58(12), 1–15.
Qu, H. M., & Yang, Y. (2021). Study on four-steps promoting ways to develop the innovative capability for college students from the perspective of art education. Creative Education Studies, 9(4). https://www.hanspub.org/ journal/paperinformation?paperid=44382
Sun, P., Huang, T., Ma, W., Gao, Z., & Cai, J. (2024). Artificial intelligence enabling education governance: Value orientation, key issues, path optimization. Journal of Social Sciences, 15(12), 137395. https://doi.org/10.4236/jss. 2024.1211100
Tang, Y. (2024). Research on digital teaching models of art courses in Chinese universities. STEMM Press. http://stemmpress.com/uploadfile/202407/ 98108484e1a4047.pdf
Xiao, J., Xu, Z., Xiao, A., Wang, X., & Skare, M. (2024). Overcoming barriers and seizing opportunities in the innovative adoption of next-generation digital technologies. Technological Forecasting and Social Change, 196, 101616. https://doi.org/10.1016/j.techfore.2024.101616
Zhou, W. (2022). Art education management in higher vocational colleges of Hubei province. In Doctoral dissertation. Silpakorn University. iThesis.