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Mapping the educational Frontier: Unleashing the Potential of artificial intelligence talents through cooperative planning in the Guangdong-Hong Kong-Macao greater bay area.绘制教育前沿:通过粤港澳大湾区的合作规划释放人工智能人才的潜力。
Heliyon. 2024 Jan 7;10(2):e24168. doi: 10.1016/j.heliyon.2024.e24168. eCollection 2024 Jan 30.
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本文引用的文献

1
An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers.分层kappa型统计量在评估多个观察者之间的多数一致性中的应用。
Biometrics. 1977 Jun;33(2):363-74.

绘制教育前沿:通过粤港澳大湾区的合作规划释放人工智能人才的潜力。

Mapping the educational Frontier: Unleashing the Potential of artificial intelligence talents through cooperative planning in the Guangdong-Hong Kong-Macao greater bay area.

作者信息

Zhang Zhuo, Li Jie, Chen Yansheng, Chen Fajun, Liu Zhonghao

机构信息

School of Business, Macau University of Science and Technology, Macau, 999078, China.

School of Finance and Trade, Guangdong Industry Polytechnic, Guangzhou, 510300, China.

出版信息

Heliyon. 2024 Jan 7;10(2):e24168. doi: 10.1016/j.heliyon.2024.e24168. eCollection 2024 Jan 30.

DOI:10.1016/j.heliyon.2024.e24168
PMID:38293422
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10825421/
Abstract

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has become an important hub for technological innovation and economic development in China. With the growing demand for artificial intelligence (AI) and big data technology talents, it is essential to develop educational cooperation within the GBA to develop a talent pool that can meet the changing needs in the region. This paper focuses on the development of dynamic demand for AI talents and proposes a strategic planning framework for educational cooperation in the GBA. We use the research idea of common attributes and key chain clustering-factor association selection-analysis of the driving force and subordination among factors-the key characteristics of AI talents. Using collinear analysis of citations and grounded theory methods, an operational definition of the influencing factors of AI talent literacy characteristics is constructed. Using the Interpretative Structural Modeling(ISM) and MICMAC (Matrice d'Impacts Croises-Multipication Applique A Classement), analyze and identify the driving force and subordination of the influencing factors of key traits of talents, and present the combined effect of multi-level factors of key traits of talents. Combined with the educational differences and complementary advantages in the GBA, five strategies and seven implementation suggestions for the GBA's AI talent education cooperation plan are formulated to establish a collaborative ecosystem that promotes the growth and integration of AI in the GBA.

摘要

粤港澳大湾区已成为中国技术创新和经济发展的重要枢纽。随着对人工智能(AI)和大数据技术人才的需求不断增长,在大湾区开展教育合作以培养能够满足该地区不断变化需求的人才库至关重要。本文聚焦于人工智能人才的动态需求发展,并提出了大湾区教育合作的战略规划框架。我们运用共同属性和关键链聚类——因素间驱动力与从属关系的关联选择分析这一研究思路——人工智能人才的关键特征。通过引文共线分析和扎根理论方法,构建了人工智能人才素养特征影响因素的操作性定义。运用解释结构模型(ISM)和MICMAC(交叉影响矩阵乘法应用于分类),分析并识别人才关键特质影响因素的驱动力和从属关系,呈现人才关键特质多层次因素的综合效应。结合大湾区的教育差异和互补优势,制定了大湾区人工智能人才教育合作计划的五项策略和七条实施建议,以建立一个促进人工智能在大湾区成长与融合的协同生态系统。