Artificial Intelligence-Based Teaching Reform of College Mathematics in Application-Oriented Universities

Geng Liu

doi:10.6918/IJOSSER.202611_9(11).0028

Authors

Geng Liu Harbin University of Science and Technology, Weihai, 264300, China

DOI:

https://doi.org/10.6918/IJOSSER.202611_9(11).0028

Keywords:

Application-oriented universities, artificial intelligence, college mathematics, human–AI collaboration, teaching reform.

Abstract

The continuing development of artificial intelligence is reshaping higher education, and industry’s changing requirements for graduates’ competencies pose new challenges to college mathematics courses in application-oriented universities. This article reviews relevant research in China and analyzes the practical problems of college mathematics teaching in these universities along five dimensions: teaching objectives, course content, classroom organization, teachers’ digital competence, and course assessment. It then explains the internal logic and implementation principles of integrating artificial intelligence into course reform. Based on the positioning of application-oriented talent cultivation, it proposes reform measures: optimizing course objectives and content, building a human–AI collaborative classroom, developing intelligent teaching resources, cultivating teachers’ intelligent teaching competence, improving multi-dimensional assessment, and preventing technology application risks. Safeguards are proposed in three respects: organization and institutions, platform operation and maintenance, and teaching and research teams. The article offers a reference for similar institutions that use artificial intelligence to advance college mathematics course development and improve the quality of application-oriented talent cultivation.

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References

[1] Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutym, A., Lajevardi, R., Laux, L., Liebner, M., Lindner, F., Pfeiffer, J., Sailer, M., Schlaile, M., … Zierke, M. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274.

[2] Knox, J. (2020). Artificial intelligence and education in China. Learning, Media and Technology, 45(3), 298–311. https://doi.org/10.1080/17439884.2020.1724078

[3] Lu, X., & Lü, X. (2020). Implications of the development of artificial intelligence for college mathematics teaching in the big data context (in Chinese). College Mathematics, 36(4), 60–67.

[4] Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11, 28. https://doi.org/10.1186/s40561-024-00292-7

[5] Shao, H., Shao, F., & Zhu, S. (2025). Reform practice and exploration of the teaching content of college mathematics public foundation courses assisted by artificial intelligence (in Chinese). College Mathematics, 41(3), 26–31.

[6] Frieder, S., Pinchetti, L., Griffiths, R., Salvatori, T., Lukasiewicz, T., Petersen, P., Chevalier, A., & Berner, J. (2023). Mathematical capabilities of ChatGPT. Advances in Neural Information Processing Systems, 36. https://doi.org/10.48550/arXiv.2301.13867

[7] Gao, Y., & Wang, C. (2026). Research on the construction of integrated-intelligence courses in college mathematics (in Chinese). Heilongjiang Education, (2), 50–52.

[8] Yuan, H. (2026). Research on constructing an integrated smart teaching model for college mathematics empowered by digital intelligence (in Chinese). In Proc. 2026 Symp. Interdisciplinary Education in Artificial Intelligence.

[9] Hillmayr, D., Ziernwald, L., Reinhold, F., Hofer, S. I., & Reiss, K. M. (2020). The potential of digital tools to enhance mathematics and science learning in secondary schools: A context-specific meta-analysis. Computers & Education, 153, 103897. https://doi.org/10.1016/j.compedu.2020.103897

[10] Deng, R., Yang, Y., & Zhang, Y. (2025). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, 105224. https://doi.org/10.1016/j.compedu.2025.105224

[11] Bastani, H., Zhang, C., & Dubrawski, A. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences of the United States of America, 122(26), e2422633122. https://doi.org/10.1073/pnas.2422633122

[12] Holmes, W., Bektik, D., Dillenbourg, P., & Blanchard, E. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(3), 504–526. https://doi.org/10.1007/s40593-022-00299-0