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

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

Author

Listed:
  • Yujing Wang

    (Space Engineering University)

  • Chen Yu

    (Space Engineering University)

  • Yue Zhao

    (Space Engineering University)

  • Haoyang Song

    (Nanjing Panda Handa Technology Co., Ltd.)

  • Bing Wang

    (Space Engineering University)

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.

Suggested Citation

  • Yujing Wang & Chen Yu & Yue Zhao & Haoyang Song & Bing Wang, 2026. "Data-driven Interdisciplinary Teaching of University Mathematics Research on Adaptive Learning Closed Loop Based on AI and Big Data," Advances in Economics, Business and Management Research, in: Touria Benazzouz & Sandeep Saxena & Hui Nee Au Yong & Nor Zafir Md Salleh (ed.), Proceedings of the 2025 7th International Conference on Economic Management and Model Engineering (ICEMME 2025), pages 148-157, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6239-602-9_15
    DOI: 10.2991/978-94-6239-602-9_15
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