Frontiers in Educational Research, 2025, 8(3); doi: 10.25236/FER.2025.080309.
Minling Zhu1, Zhiyun Yang2, Yonglin Liu3, Li Xiao4
1 College of Computer Science, Beijing Information Science and Technology University, Beijing, China
2 Office of Strategy and Planning, Beijing Information Science and Technology University, Beijing, China
3 School of Public Administration and Media, Beijing Information Science and Technology University, Beijing, China
4 School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
Given that the cultivation of talent in fundamental disciplines is a key strategy for educational and technological powerhouses worldwide, as well as China's high emphasis on it, this paper analyzes the factors that need to be considered in the cultivation of talent in fundamental disciplines and the existing issues in China’s talent development in these fields. It conducts research on large-scale, personalized education methods to promote individualized learning. The rapid development of computer technology and artificial intelligence provides both theoretical and data-based foundations for advancing personalized education on a large scale, as well as the feasibility of background technologies for automatically generating objective and scientific personalized training models. Based on this, the paper proposes a method for automatically constructing large-scale personalized training models, including paths, content, and methods. It also analyzes the key technical issues to be addressed and presents practical approaches and potential for future automatic generation of training models.
Fundamental Disciplines; Personalized Training; Scaling; Automatic Generation; Machine Learning
Minling Zhu, Zhiyun Yang, Yonglin Liu, Li Xiao. The Research on the Automatic Generation Method of Scaled and Personalized Training Model for Fundamental Disciplines. Frontiers in Educational Research(2025), Vol. 8, Issue 3: 61-67. https://doi.org/10.25236/FER.2025.080309.
[1] He, Canfei. "The Cultivation of Top-tier Talents in Geographical Science in the New Era under the Background of the 'Top-tier Plan' 2.0." Chinese University Teaching, 2024(1): 4-12.
[2] Ministry of Education. Notice on the Issuance of the "Summit Plan for Fundamental Research in Higher Education Institutions." State Council Bulletin of the People's Republic of China, 2018(35): 52-55.
[3] Gao, Wei, Zhu, Zhihan, Wu, Fengmin, et al. "Research on the Cultivation Model of Top-tier Talents in Basic Disciplines in Provincial Universities: A Case Study of Harbin University of Science and Technology's 'Da Heng Class'." Education and Teaching Forum, 2021(19): 1-4.
[4] Ministry of Education, et al. "Opinions on Implementing the 'Top-tier Students Cultivation Program 2.0' for Basic Disciplines." Ministry of Education Bulletin of the People's Republic of China, 2018(10): 29-31.
[5] Liu, Ji'an, Xu, Yanru. "The National Answer to Qian Xuesen's Question: A Review of the Policy and Practice of Talent Cultivation in Basic Disciplines." China Talent, 2022(07): 9-11.
[6] Hou, Qinqin, Yang, Chundi, Zou, Pei, et al. "Exploration of the Innovative Talent Cultivation Model Based on Basic Disciplines: A Case Study of University Chemistry Laboratory Courses." Yunnan Chemical Industry, 2022, 49(11): 112-115.
[7] Ministry of Education, et al. "Guiding Opinions on Promoting the Construction of New Educational Infrastructure to Build a High-Quality Educational Support System." Ministry of Education Bulletin of the People's Republic of China, 2021(09): 15-19.
[8] Zhou, Deqing, Yang, Xianmin. "Analysis of Policies Promoting Differentiated Education in China, Development Context, and Issues: A Study Based on 72 National Policy Documents Mentioning Differentiated Education from 2001 to 2021." Modern Educational Technology, 2022, 32(06): 15-24.
[9] China Electronics Standardization Institute. "Artificial Intelligence Standardization White Paper (2018 Edition)" [R] [EB/OL] http://www.cesi.cn/. 201801/3545.html.
[10] Liu, Xin, Yang, Juan. "Exploration of the Application Prospects of Machine Learning in the Field of Education." Software Guide (Educational Technology), 2019, 18(02): 1-3.
[11] Wu, Xiaoru, Wang, Zheng. "Development Trends and Practical Cases of Artificial Intelligence in Education Applications." Modern Educational Technology, 2018, 28(02): 5-11.
[12] Siyong, F. "A Reinforcement Learning-Based Smart Educational Environment for Higher Education." International Journal of e-Collaboration (IJeC), 2022, 19(6): 1-17.
[13] Yang, Lina, Gao, Yarong, Chai, Jinhui. "Empirical Study on the Multi-Source Influencing Factors of Online Learning Resource Adoption Behavior." Tianjin University of Technology Journal, 2020, 24(04): 16-22.
[14] Gui, Xiaolin. "Promoting University Computer General Education with Artificial Intelligence at its Core." China University Teaching, 2024(11): 4-9.
[15] Shaofei, S., Xuejun, Z., Aobo, X., Taisen, D. "An Adaptive Exploration Mechanism for Q-learning in Spatial Public Goods Games." Chaos, Solitons & Fractals, 2024, 189(1): 115705.
[16] Zgi, B., Zkaya, M., Re, N.K., et al. "A Holistic Matrix Norm-Based Alternative Solution Method for Markov Reward Games." Applied Mathematics and Computation, 2025, 488.
[17] Zhang, Enlai, Luo, Zhiwei, Yang, Yang. "Exploration of the Multi-Path Collaborative Innovation Talent Cultivation Model for Mechanical Engineering Undergraduates." China University Teaching, 2024(8): 10-15.