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

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  • 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
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    References listed on IDEAS

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    1. Nadire Cavus & Yakubu Bala Mohammed & Mohammed Nasiru Yakubu, 2021. "An Artificial Intelligence-Based Model for Prediction of Parameters Affecting Sustainable Growth of Mobile Banking Apps," Sustainability, MDPI, vol. 13(11), pages 1-21, May.
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    5. Nadire Cavus & Yakubu Bala Mohammed & Abdulsalam Ya’u Gital & Mohammed Bulama & Adamu Muhammad Tukur & Danlami Mohammed & Muhammad Lamir Isah & Abba Hassan, 2022. "Emotional Artificial Neural Networks and Gaussian Process-Regression-Based Hybrid Machine-Learning Model for Prediction of Security and Privacy Effects on M-Banking Attractiveness," Sustainability, MDPI, vol. 14(10), pages 1-21, May.
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