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Journals / Ordu Üniversitesi Bilim ve Teknoloji Dergisi / 2026 / Cilt: 16 Sayı: ISRIS 2026 Özel Sayı

Artificial Intelligence in Science Education: A Data-Driven Mapping of Research Trends, Knowledge Structure, and Collaboration Networks

Fen Eğitiminde Yapay Zekâ: Araştırma Eğilimlerinin, Bilgi Yapısının ve İş Birliği Ağlarının Veri Odaklı Haritalanması

Hacı Mehmet Yeşiltaş(SULEYMAN DEMIREL UNIVERSITY)0000-0002-3359-3450
Pages
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Publication Date
2026-10-07
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Findings reveal that the field is in an exploratory growth phase, with an annual growth rate of 40.42%; the year 2023 emerged as a pivotal milestone in both publication output and citation impact.

📌 3 Key Takeaways

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Objective & Hypothesis:This study presents a bibliometric mapping of the global landscape of artificial intelligence (AI) research in science education over the 2020–2026 period.
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Key Findings & Data:A corpus of 140 articles was retrieved from the Web of Science Core Collection following PRISMA 2020 guidelines and analyzed using Bibliometrix, VOSviewer, and Biblioshiny.
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Conclusion & Impact:Findings reveal that the field is in an exploratory growth phase, with an annual growth rate of 40.42%; the year 2023 emerged as a pivotal milestone in both publication output and citation impact.
🔬Design:Modeling & Simulation
📍Scope:2020–2026 Verileri
⚙️Variables:yapay zekâ · fen eğitimi · bilgi haritalama

Abstract

This study presents a bibliometric mapping of the global landscape of artificial intelligence (AI) research in science education over the 2020–2026 period. A corpus of 140 articles was retrieved from the Web of Science Core Collection following PRISMA 2020 guidelines and analyzed using Bibliometrix, VOSviewer, and Biblioshiny. Findings reveal that the field is in an exploratory growth phase, with an annual growth rate of 40.42%; the year 2023 emerged as a pivotal milestone in both publication output and citation impact. Geographically, the United States (n=109), China (n=75), and Germany (n=46) dominate production, while Africa and Latin America remain virtually absent—a pattern indicative of geographic representational inequality rooted in database bias, language barriers, and funding disparities. Bradford's law identified only three core journals, with the Journal of Chemical Education's inclusion reflecting the disciplinary distinctiveness of science education. Intellectually, Cooper (2023) occupies a seminal position with 676 citations; ArXiv's second-place ranking in local citation analysis reveals that science education researchers draw directly from machine learning literature. Thematic mapping shows "artificial intelligence" and "education" as motor themes, while "chatgpt" and "generative ai" remain widespread yet theoretically underdeveloped. The 2026 thematic shift toward teacher and student themes signals a transition from a tool-centered to a human-centered research agenda.

Özet

Bu çalışma, fen eğitiminde yapay zekâ araştırmalarının 2020–2026 dönemindeki küresel yapısını bibliyometrik yöntemlerle haritalamaktadır. Web of Science Core Collection'dan PRISMA 2020 yönergelerine uygun biçimde derlenen 140 makale; Bibliometrix, VOSviewer ve Biblioshiny araçlarıyla çok katmanlı bir analize tabi tutulmuştur. Bulgular, alanın %40,42'lik yıllık büyüme hızıyla keşif evresinde olduğunu ortaya koymaktadır; 2023 yılı hem yayın zirvesi hem de atıf etkisi bakımından bir dönüm noktası oluşturmaktadır. Yayın coğrafyasında ABD (n=109), Çin (n=75) ve Almanya (n=46) belirleyici konumdayken Afrika ve Latin Amerika neredeyse temsil edilmemekte; bu durum coğrafi temsil dengesizliğine işaret etmektedir. Bradford yasası çerçevesinde yalnızca üç çekirdek dergi belirlenmiş, Journal of Chemical Education'ın bu grupta yer alması fen eğitiminin disipliner özgünlüğünü yansıtmaktadır. Entelektüel yapı analizinde, Cooper (2023) 676 atıfla seminal konuma yerleşmiş; ArXiv'in yerel atıf listesinde ikinci sıraya girmesi, fen eğitimcilerinin makine öğrenmesi literatürünü doğrudan kaynak olarak kullandığını göstermektedir. Callon tematik haritasında "artificial intelligence" ve "education" motor temalar olarak konumlanırken "chatgpt" ve "generative ai" yaygın ancak teorik derinlikten yoksun temalar olarak belirginleşmektedir. 2026 döneminde öğretmen ve öğrenci temalarının öne çıkması, araştırma gündeminin teknoloji odağından insan odağına evrildiğine işaret etmektedir.

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Cite This Article

Yeşiltaş, H. M. (2026). Artificial Intelligence in Science Education: A Data-Driven Mapping of Research Trends, Knowledge Structure, and Collaboration Networks. Ordu Üniversitesi Bilim ve Teknoloji Dergisi, Cilt: 16 Sayı: ISRIS 2026 Özel Sayı. https://doi.org/10.54370/ordubtd.1960695

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