Construction and Optimization Strategies of Prediction Model for Undergraduate Major Settings in Private Universities from the Perspective of Artificial Intelligence_Vol. 2 No. 2 (JHET 2025)_Journal of Higher Education Teaching (ISSN: 3005-5776)

Home > Journal of Higher Education Teaching (ISSN: 3005-5776) > Vol. 2 No. 2 (JHET 2025) >

Construction and Optimization Strategies of Prediction Model for Undergraduate Major Settings in Private Universities from the Perspective of Artificial Intelligence

Download PDF

DOI: https://doi.org/10.62517/jhet.202515210

Author(s)

Xiongfei Liu, Yu Ren, Qian Su, Lei Huang*, Weimin Wu, Wenqiang Du, Tong Yin

Affiliation(s)

Yinchuan University of Science and Technology, Yinchuan, Ningxia Hui, China *Corresponding Author.

Abstract

With the popularization of higher education in China, private universities play a crucial role in strengthening the educational framework and promoting fairness. However, they encounter multiple difficulties in the setting of undergraduate majors. This paper takes artificial intelligence as the entry point and presents a prediction model for undergraduate major allocation that takes into account multiple factors such as strategies, consensus, market acceptance, and educational resources. The model is constructed based on data sample collection and processing, integrating strategy orientation, market trends, educational resources, and social recognition. The aim is to accurately predict the multi-dimensional needs of private universities. Research shows that this model can enhance the efficiency of major allocation decisions and reduce blind investment and resource waste. This paper further improves the model algorithm and plans optimization schemes from the dimensions of strategy, market, and resources, providing guidance for the professional planning of private universities. This research not only helps to improve the scientific nature of undergraduate major allocation in private universities but also points out a new direction for the deep application of artificial intelligence in the field of education.

Keywords

Artificial Intelligence; Private Higher Education Institutions; Undergraduate Major Setting; Prediction Model; Optimization Strategy

References

[1]Xudoynazarovich S A. Challenges of Human Resource Management In Higher Education Institutions and Their Solutions. Ethiopian International Journal of Multidisciplinary Research, 2024, 11(12): 398-402. [2]Khachatryan K, Hakobjanyan A, Nikoghosyan K, et al. Development of university–industry partnership in Armenia: university perspective. Journal of International Education in Business, 2024, 17(1): 170-192. [3]Parycek P, Schmid V, Novak A S. Artificial Intelligence (AI) and automation in administrative procedures: Potentials, limitations, and framework conditions. Journal of the Knowledge Economy, 2024, 15(2): 8390-8415. [4]Sathish A S, Samuel Rajkumar V, Vijay V, et al. 2 The Significance of Artificial Intelligence. AI-Oriented Competency Framework for Talent Management in the Digital Economy: Models, Technologies, Applications, and Implementation, 2024: 28. [5]Qin Y, Xu Z, Wang X, et al. Artificial intelligence and economic development: An evolutionary investigation and systematic review. Journal of the Knowledge Economy, 2024, 15(1): 1736-1770. [6]Singh N, Jain M, Kamal M M, et al. Technological paradoxes and artificial intelligence implementation in healthcare. An application of paradox theory. Technological Forecasting and Social Change, 2024, 198: 122967. [7]Weber P, Carl K V, Hinz O. Applications of explainable artificial intelligence in finance—a systematic review of finance, information systems, and computer science literature. Management Review Quarterly, 2024, 74(2): 867-907. [8]Forero-Corba W, Bennasar F N. Techniques and applications of Machine Learning and Artificial Intelligence in education: a systematic review. RIED-Revista Iberoamericana de Educación a Distancia, 2024, 27(1). [9]Heng K. Challenges and developments in university research in Cambodia: A case study of two universities. Higher Education, 2024, 87(6): 1593-1613. [10]Duah J E, McGivern P. How generative artificial intelligence has blurred notions of authorial identity and academic norms in higher education, necessitating clear university usage policies. The International Journal of Information and Learning Technology, 2024, 41(2): 180-193. [11]Didier N. Educational mismatch, labor market completeness, and gender: Evidence from Chile. International Journal of Educational Development, 2024, 105: 102990. [12]Edwards-Fapohunda D M O. The role of adult learning and education in community development: A case study of New York. Iconic Research and Engineering Journals, 2024, 8(1): 437-454. [13]Ortiz B L, Gupta V, Kumar R, et al. Data preprocessing techniques for ai and machine learning readiness: Scoping review of wearable sensor data in cancer care. JMIR mHealth and uHealth, 2024, 12(1): e59587. [14]Zha W, Liu Y, Wan Y, et al. Forecasting monthly gas field production based on the CNN-LSTM model. Energy, 2022, 260: 124889.