IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i4p1524-d1337267.html
   My bibliography  Save this article

Constructing and Testing AI International Legal Education Coupling-Enabling Model

Author

Listed:
  • Yunyao Wang

    (School of Law, Chongqing University, Chongqing 400044, China
    School of Information and Business Management, Chengdu Neusoft University, Chengdu 611844, China)

  • Shudong Yang

    (School of Law, Chongqing University, Chongqing 400044, China)

Abstract

In this paper, we aim to assess the coupling capability of artificial intelligence in international legal education, delving into crucial aspects of its implementation and effectiveness. This paper constructs a coupling empowerment model of AI international legal education by using artificial intelligence technology. It also discusses the application of Pearson product–moment correlation coefficient in correlation analysis, the implementation of AI knowledge mapping in the help of intelligent parents, and the application of BP neural algorithm in artificial neural networks in order to establish a cognitive student model. This teaching mode can provide personalized learning experience and intelligent teaching support and allow accurate assessment of students’ learning level and cognitive ability. The results show that the employment rate of students is increased from 75% to 100%, and the evaluation of practicability is maintained at 10 points. It proves that AI technology provides an innovative approach to international law education, which is expected to promote the efficient use of educational resources and improve students’ performance and employment rate.

Suggested Citation

  • Yunyao Wang & Shudong Yang, 2024. "Constructing and Testing AI International Legal Education Coupling-Enabling Model," Sustainability, MDPI, vol. 16(4), pages 1-16, February.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:4:p:1524-:d:1337267
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/4/1524/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/4/1524/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:4:p:1524-:d:1337267. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.