Proceedings of the 2025 7th International Conference on Economic Management and Model Engineering (ICEMME 2025)

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Data-driven Interdisciplinary Teaching of University Mathematics Research on Adaptive Learning Closed Loop Based on AI and Big Data

Authors
Yujing Wang1, *, Chen Yu1, Yue Zhao1, Haoyang Song2, Bing Wang1
1Space Engineering University, Beijing, 101400, China
2Nanjing Panda Handa Technology Co., Ltd., Nanjing, 210014, China
*Corresponding author. Email: wang-yujing@foxmail.com
Corresponding Author
Yujing Wang
Available Online 13 March 2026.
DOI
10.2991/978-94-6239-602-9_15How to use a DOI?
Keywords
Artificial intelligence; Computer technology; College Mathematics; Interdisciplinary integration
Abstract

With the rapid development of AI and big data technologies, the traditional teaching model of university mathematics is facing unprecedented opportunities. This paper proposes an innovative solution of embedding AI and big data into university mathematics teaching, aiming to break through the traditional linear teaching structure of “teacher - textbook - classroom” and build a data-driven adaptive learning closed loop. By introducing a dual-wheel drive model of “intelligent systems + interdisciplinary projects”, this paper utilizes AI technology to achieve personalized path recommendation, automatic question generation and real-time learning diagnosis, while big data dynamically adjusts the course structure and teaching strategies through in-depth analysis of group learning data. This teaching approach that combines AI and big data not only enhances students’ learning efficiency but also builds an effective knowledge transfer bridge between mathematics and disciplines such as computer science and data science, providing theoretical support and practical guidance for the future transformation of educational models.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2025 7th International Conference on Economic Management and Model Engineering (ICEMME 2025)
Series
Advances in Economics, Business and Management Research
Publication Date
13 March 2026
ISBN
978-94-6239-602-9
ISSN
2352-5428
DOI
10.2991/978-94-6239-602-9_15How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yujing Wang
AU  - Chen Yu
AU  - Yue Zhao
AU  - Haoyang Song
AU  - Bing Wang
PY  - 2026
DA  - 2026/03/13
TI  - Data-driven Interdisciplinary Teaching of University Mathematics Research on Adaptive Learning Closed Loop Based on AI and Big Data
BT  - Proceedings of the 2025 7th International Conference on Economic Management and Model Engineering (ICEMME 2025)
PB  - Atlantis Press
SP  - 148
EP  - 157
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-602-9_15
DO  - 10.2991/978-94-6239-602-9_15
ID  - Wang2026
ER  -