IDEAS home Printed from https://ideas.repec.org/a/sae/sagope/v14y2024i3p21582440241266370.html
   My bibliography  Save this article

A Global Outlook on AI-Predicted Impacts of ChatGPT on Contemporary Education

Author

Listed:
  • Sezer Kanbul
  • Idris Adamu
  • Yakubu Bala Mohammed

Abstract

This article presents a research investigation focusing on the effects of ChatGPT utilization on sustainable education and development. The study employed five machine learning (XGBoost, RF, SVM, GBDT, and ANN) models for predicting the impacts of ChatGPT usage in education, aiming at identifying the potential benefits of ChatGPT usage on learners, tutors, and possible future implications using the data collected via social networking sites. A total of 2,936 datasets concerning the impacts of ChatGPT utilization on sustainable education were analyzed. Four of the research AI-predictive models predicted the impacts of ChatGPT usage on sustainable education and development with greater accuracy with R 2 values of >.96. However, the XGBoost and RF models outperformed the other models with R 2 values >.98. The results indicate that the XGBoost model achieved the highest accuracy with R 2  > .98, training time(s) 8.4157, and testing time 0.0618 respectively. Furthermore, findings of the study revealed that utilization of ChatGPT increases students’ interest in learning, self-confidence, and ability to study independently. Also, the results of the study will provide valuable insights for education stakeholders in understanding the potential benefits, and future implications of ChatGPT usage on sustainable education, and provide direction for upcoming studies.

Suggested Citation

  • Sezer Kanbul & Idris Adamu & Yakubu Bala Mohammed, 2024. "A Global Outlook on AI-Predicted Impacts of ChatGPT on Contemporary Education," SAGE Open, , vol. 14(3), pages 21582440241, August.
  • Handle: RePEc:sae:sagope:v:14:y:2024:i:3:p:21582440241266370
    DOI: 10.1177/21582440241266370
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/21582440241266370
    Download Restriction: no

    File URL: https://libkey.io/10.1177/21582440241266370?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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:sae:sagope:v:14:y:2024:i:3:p:21582440241266370. 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: SAGE Publications (email available below). General contact details of provider: .

    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.