Reform and Innovation of Graduate Education Training Model

DOI: https://doi.org/10.62517/jhet.202515238

Author(s)

Yue Zhao, Ya Han, Xin Su*

Affiliation(s)

School of Business, Guilin University of Electronic Technology, Guilin, Guangxi, China *Corresponding Author.

Abstract

Graduate education is responsible for cultivating high-level talents, solving key core technologies, and leading industrial upgrades. In response to the structural contradictions faced by graduate education in China, such as the lag of knowledge production paradigms behind the forefront of technology and interdisciplinary integration, educational technology remaining at the level of tool empowerment, and the imperfect integration mechanism of industry and education, this paper analyzes the current state of graduate education and proposes systematic reform and innovation strategies by combining multiple paths such as interdisciplinary training and the application of artificial intelligence technology, aiming to provide a reference for constructing a high-level talent training system that meets the needs of the new era.

Keywords

Graduate Education; Training Model; Reform and Innovation; Multidisciplinary Integration; Artificial Intelligence-Driven

References

[1]Ma Yonghong, Yu Yan. Innovative Choices for High-Quality Development of Graduate Education in the Era of Digital Intelligence. Tsinghua University Education Research, 2025, 46(1): 40-47. [2]Luo Zisheng, Wang Lei, Xu Yanqun. Exploration and Practice of Interdisciplinary Training for Graduate Students. China University Teaching, 2025, (Z1): 17-22. [3]Zhao Jun, Chen Jiayu, Li Bo. The structure of graduate education disciplines and industrial structure: An empirical study of coupling coordination degree. Research on Graduate Education, 2025, (1): 1-10. [4]Zhao Zhiqiang. The matching trend and impact degree of graduate education discipline structure and industrial structure. Higher Education Management, 2025, (2): 1-12. [5]Hu Dexin, Liu Chang. The spatiotemporal pattern and evolution characteristics of the scale of graduate education in China. Higher Education Development and Evaluation, 2025, 41(1): 107-118+134. [6]Zhao Y. Research on the incentive mechanism of scientific and technological talents in the high-tech industry//E3S Web of Conferences. EDP Sciences, 2021, 235. [7]Wang Xuhui, Li Jing. A High-Quality Graduate Education System: Scientific Connotation, Development Dilemmas, and Practical Approaches. Education and Examination, 2025, (1): 53-64. [8]Luo Zisheng, Wang Lei, Xu Yanqun. Exploration and Practice of Interdisciplinary Training for Graduate Students. China University Teaching, 2025, (Z1): 17-22. [9]Chen Jin, Wu Feng, Chen Qian, et al. A New National System from the Perspective of Meta-Governance and Its Development Path. Science of Science and Management of S&T, 1-22 [2025-03-08]. [10]Xu Xiaofei, Zhang Ce. Empowering Engineering Education and Student Competency Development, Assessment, and Certification Systems with Generative Artificial Intelligence. Research on Higher Engineering Education, 1-13 [2025-03-08]. [11]Zhou Dongbo, Zhao Shuai, Xiao Ming. Knowledge-Guided Intelligent Education Evaluation: Connotation, Mechanism, and Pathways. Educational Research and Experimentation, 2023, (4): 118-127. [12]Chen Dianbing, Zhu Xincan. Risk and Regulation: International Experience in Constructing Ethical Guidelines for Artificial Intelligence. Journal of Comparative Education, 2024, (1): 72-83. [13]Johnson J. The end of military-techno Pax Americana? Washington’s strategic responses to Chinese AI-enabled military technology. The Pacific Review, 2021, 34(3): 351-378. [14]Li X, Fan M, Liang Z. Identifying technological competition situations for artificial intelligence technology - a patent landscape analysis. International Journal of Technology Management, 2020, 82(3-4): 322-348. [15]Wei Baobao, Xi Hairong. The Triple Representation, Generative Logic, and Construction Path of High-Quality Teaching. Contemporary Education Forum, 2023, (6): 50-59. [16]Wang Yanfang, Di Chunshuo. Digital Technology Innovation, Traditional Technology Transformation, and Economic Growth. Science of Science and Management of S&T, 2025, 46(1): 109-121. [17]Chen Guoqing, Ren Ming, Wei Qiang, et al. Intelligent Empowerment: A New Leap in Information Systems Research. Management World, 2022, 38(1): 180-196. [18]Athey, S. and Imbens, G. W., 2019, "Machine Learning Methods That Economists Should Know About", Annual Review of Economics, 11, pp. 685-725. [19]Shi Xiaonan. The Training of Doctoral Students in China under the Context of Competition in Science and Education: Based on the Perspective of High-Quality Educational Development. Contemporary Education Forum, 1-14 [2025-03-08]. [20]Wan Yan, Li Minghui, Wu Fei, et al. Research on the Construction of an Ethical Review Framework for Artificial Intelligence Surveillance Technology—Based on Text Analysis. Soft Science, 2023, 37(4): 1-6.