Innovation and Practice of AI-Empowered Mathematics Course Teaching Models in Private Universities from the Perspective of Smart Education

DOI: https://doi.org/10.62517/jhet.202615316

Author(s)

He Yu*, Ru Hu

Affiliation(s)

Liaoning University of International Business and Economics, Dalian, China *Corresponding Author.

Abstract

In private universities, mathematics courses face challenges such as uneven student foundations, low learning motivation, and difficulty in personalized instruction due to large class sizes. From a smart education perspective, this study follows a “theoretical analysis–model construction–practical verification–countermeasure suggestions” logic to explore AI-empowered teaching. It proposes a “three-stage, four-element” model centered on pre-class intelligent diagnosis, in-class human-AI collaboration, and post-class data feedback. A semester-long controlled experiment in an Advanced Mathematics course shows that compared with traditional teaching, the AI-empowered model significantly improves grade distribution, pass rate, excellence rate, and learning engagement. Practical countermeasures are offered in platform construction, teacher competence, resource governance, and evaluation mechanisms.

Keywords

Smart Education; Private Universities; Artificial Intelligence; Mathematics Courses; Teaching Model Innovation

References

[1]Zhu Zhiting, Hu Jiao. An Exploration of the Essence and Research Prospects of Educational Digital Transformation [J]. China Educational Technology, 2022(04): 1–8+25. [2]Huang Ronghuai, Liu Dejian, Hu Yongbin. Theoretical Connotations and Practical Pathways of Artificial Intelligence Empowering Education and Teaching [J]. China Educational Technology, 2023(05): 1–8. [3]Gu Xiaoqing, Du Hua, Peng Hongchao, Zhu Zhiting. The Theoretical Framework, Practical Pathways, Development Trajectory, and Future Vision of Smart Education [J]. Journal of East China Normal University (Educational Sciences), 2021, 39(08): 20–32. [4]Yu Shengquan, Wang Fancong. The Cognitive Outsourcing Trap in Educational Applications of Artificial Intelligence and Ways to Transcend It [J]. E-Education Research, 2023, 44(12): 5–13. [5]Guo Liming, Yang Xianmin, Zhang Yao. Analysis of the New Development and Value Orientation of Precision Teaching in the Era of Big Data [J]. E-Education Research, 2019, 40(10): 76–81+88. [6]Jiang Qiang, Zhao Wei, Wang Pengjiao, Wang Liping. An Analysis Model and Implementation of Personalized Adaptive Online Learning Based on Big Data [J]. China Educational Technology, 2015(01): 85–92.