IDEAS home Printed from https://ideas.repec.org/a/igg/jcini0/v18y2024i1p1-14.html
   My bibliography  Save this article

The Influence of Artificial Intelligence Technology on the Optimization of the Teaching Model of Higher Education in the Context of the Pandemic

Author

Listed:
  • Kaiyu Xiang

    (Shaanxi Normal University, China & Northwest University of Politics and Law, China)

  • Yuanyuan Zhu

    (Shanghai Normal University, China & Jiangsu Normal University, China)

Abstract

The environment of the epidemic is a life changer for everyone, and specifically for the educational system is progress. In order to overcome the challenges in the educational system, this study proposes a new intelligent approach to teaching and learning. Teaching plays a vital role in higher education and can be advanced by utilising certain tools and technologies such as internet enabled mobile applications, automated scheduling of courses, assessment etc. The Emergency Distance Learning Methodology (EDLM) is proposed to improve the teaching-learning process by implementing artificial intelligence. Comparing the proposed system with the existing qualitative response analysis methods, it is observed that the proposed system provides 98.56% accuracy over the existing models. This study aims to assess the optimisation of teaching and learning models in higher education.

Suggested Citation

  • Kaiyu Xiang & Yuanyuan Zhu, 2024. "The Influence of Artificial Intelligence Technology on the Optimization of the Teaching Model of Higher Education in the Context of the Pandemic," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 18(1), pages 1-14, January.
    • Handle: RePEc:igg:jcini0:v:18:y:2024:i:1:p:1-14
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJCINI.343519
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:igg:jcini0:v:18:y:2024:i:1:p:1-14. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.