Author
Listed:
- Yulin Zhao
- Junke Li
- Kai Liu
- Jiang’e Wang
- Jehad Ali
Abstract
The Internet is a tool for free expression of will, primarily reflecting the public’s willingness to pay attention. Therefore, it is of great significance to use network attention to guide the implementation of “Artificial Intelligence (AI) + Education.†First, this study takes the “AI + Education†network attention of 31 provinces and cities in China as the research object and selects the relevant data from the Baidu Index and the National Bureau of statistics from 2012 to 2020. Then, the study uses the methods of elasticity coefficient, geographical concentration index, and panel model to analyze the spatiotemporal characteristics and influencing factors of “AI + Education.†Finally, the future development trends in “AI + Education†is predicted. The results show that the time characteristics of “AI + Education†are apparent, and there are specific interannual differences. The spatial difference between “AI + Education†attention is narrowing, and the spatial balance is gradually improving. The Internet, level of economic development, education funding, and vocational education are the main factors influencing the attention of “AI + Education.†According to the forecast results, the attention to “AI + Education†in eastern and central China will generally rise in the next 2 years, while some parts of western China will slightly decline. Therefore, in the future development, national and regional governments should pay attention to the policy guidance of regional differences, strengthen the promotion of new teaching methods, and attach importance to the intelligent construction of vocational education, to promote the integrated development of AI and Education.
Suggested Citation
Yulin Zhao & Junke Li & Kai Liu & Jiang’e Wang & Jehad Ali, 2022.
"Analyzing the Spatio-Temporal Characteristics and Influencing Factors of “AI + Education†Network Attention in China,"
Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-17, May.
Handle:
RePEc:hin:jnlmpe:5101967
DOI: 10.1155/2022/5101967
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