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Effectiveness of Artificial Intelligence Empowering Innovation Education Management of Art Majors in Universities under Universities under LiaoningProvence |
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| รหัสดีโอไอ | |
| Creator | Zhang He He |
| Title | Effectiveness of Artificial Intelligence Empowering Innovation Education Management of Art Majors in Universities under Universities under LiaoningProvence |
| Contributor | Sutida Howattanakul, Vorachai Viphoouparakhot, Ntapat Worapongpat |
| Publisher | DR.KET Institute of Academic Development and Promotion |
| Publication Year | 2569 |
| Journal Title | Asian Journal of Humanities and Social Innovation |
| Journal Vol. | 3 |
| Journal No. | 3 |
| Page no. | 17-31 |
| Keyword | Effectiveness, Artificial Intelligence, Innovation Education Management, Art Majors, Universities in Liaoning Province |
| URL Website | https://so14.tci-thaijo.org/index.php/AJHSI |
| Website title | https://so14.tci-thaijo.org/index.php/AJHSI |
| ISSN | 3088-1897 |
| Abstract | This study develops a management framework to enhance the effectiveness of Artificial Intelligence (AI)-empowered innovation education management for art majors in universities in Liaoning Province, China. In response to the growing integration of AI technologies in higher education, particularly within creative disciplines, this research explores how AI can improve teaching innovation, educational management efficiency, and learner engagement. A mixed-methods research design was employed, integrating both quantitative and qualitative approaches. The target population consisted of 2,400 administrators, professors, and lecturers from five universities in Liaoning Province. A sample of 331 respondents was selected using stratified random sampling based on Krejcie and Morgan’s sampling framework. In addition, five key informants including university presidents, deans of art faculties, and program directors were purposively selected for in-depth interviews to provide qualitative insights. Data were collected using semi-structured interviews, a five-point Likert scale questionnaire, and focus group discussions. Quantitative data were analyzed using descriptive statistics and Exploratory Factor Analysis (EFA), while qualitative data were examined through content analysis.The findings identified a total of 24 AI-based educational management guidelines, organized into six key domains: (1) AI technology application scope, (2) innovation in teaching and learning management, (3) educational management effectiveness, (4) autonomy of thinking and learner satisfaction, (5) government and institutional resource support, and (6) leadership styles for AI-enabled learning systems.The study contributes both theoretical and practical insights into the integration of artificial intelligence in art education management. It provides a structured framework for improving educational innovation, enhancing institutional effectiveness, and supporting the modernization of higher education in Liaoning Province. |
