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Effectiveness of Artificial Intelligence Practices in the Teaching of Social Sciences: A Multi-Complementary Research Approach on Pre-School Education

Author

Listed:
  • Yunus Doğan

    (School of Foreign Languages, Fırat University, Elazig 23119, Türkiye)

  • Veli Batdı

    (Nizip Education Faculty, Gaziantep University, Gaziantep 27310, Türkiye)

  • Yavuz Topkaya

    (Education Faculty, Hatay Mustafa Kemal University, Hatay 31060, Türkiye)

  • Salman Özüpekçe

    (Department of Geography Education, Education Faculty, Dicle University, Diyarbakır 21280, Türkiye)

  • Hatun Vera Akşab

    (Department of Curriculum and Instruction, Gaziantep University, Gaziantep 27310, Türkiye)

Abstract

The aim of this study is to evaluate artificial intelligence applications in the preschool education level within the framework of the multi-complementary approach (McA). The McA is designed as a comprehensive approach that encompasses multiple analysis methods. In the first phase of the study, the pre-complementary knowledge process, meta-analysis, and meta-thematic analysis methods were used; in the post-complementary knowledge process, an experimental design with a control group and pre-test/post-test was applied. Finally, in the complementary knowledge phase, the findings of the first two phases were combined, providing an opportunity to evaluate the effectiveness of artificial intelligence applications in preschool education from a more comprehensive and broader perspective. The study provides information about the McA, and then the methodological process and findings of the research are presented in detail within this framework. After providing information about the McA, the methodological process and results of the study are presented step by step within this framework. A literature review based on document analysis in the context of social sciences and teaching in preschool education using artificial intelligence applications has shown that the application of artificial intelligence has positive and significant effects on both student performance and various variables supporting teaching. The complementary results favoring artificial intelligence applications encourage the increased use of such technologies in preschool education, promoting their more widespread and systematic use in the teaching environment.

Suggested Citation

  • Yunus Doğan & Veli Batdı & Yavuz Topkaya & Salman Özüpekçe & Hatun Vera Akşab, 2025. "Effectiveness of Artificial Intelligence Practices in the Teaching of Social Sciences: A Multi-Complementary Research Approach on Pre-School Education," Sustainability, MDPI, vol. 17(7), pages 1-29, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:7:p:3159-:d:1626810
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    References listed on IDEAS

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    1. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    2. Jonathan A.C. Sterne & Roger M. Harbord, 2004. "Funnel plots in meta-analysis," Stata Journal, StataCorp LLC, vol. 4(2), pages 127-141, June.
    3. Sue Duval & Richard Tweedie, 2000. "Trim and Fill: A Simple Funnel-Plot–Based Method of Testing and Adjusting for Publication Bias in Meta-Analysis," Biometrics, The International Biometric Society, vol. 56(2), pages 455-463, June.
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