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<p>Artificial intelligence (AI) is rapidly transforming science education by enabling adaptive learning, virtual experimentation, and generative applications such as ChatGPT. Yet, research in this area remains fragmented and often descriptive. This study adopts an integrated approach, combining bibliometric analysis, meta-analysis, and predictive modeling, to map the research landscape, synthesize empirical impacts, and forecast future directions of AI in science education. The bibliometric analysis of 3217 studies (2015–2024) identified four major thematic clusters: intelligent tutoring and adaptive learning, virtual and remote laboratories, generative AI and ethics, and teacher professional development. The meta-analysis of 39 empirical studies revealed a significant overall positive effect of AI on science learning outcomes (Hedges’ g = 0.63, <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44217_2025_906_Article_IEq1.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="68" /> </InlineMediaObject> <EquationSource Format="TEX">\(I^2 = 89\%\)</EquationSource> </InlineEquation>), with stronger effects in higher education (g = 0.75) and low- and middle-income country (LMIC) contexts (g = 0.82). Predictive modeling using Support Vector Regression (SVR) projected continued growth in AI and science education research until 2030, with signs of thematic saturation after 2028. The findings extend constructivist, sociocultural, and Bloom’s frameworks by showing how AI fosters individualized inquiry, collaborative learning, and higher-order cognitive tasks. Practically, they highlight the need for teacher training frameworks such as Technological Pedagogical Content Knowledge (TPACK) and the 5E model to ensure meaningful integration of AI tools. Policy implications include the development of AI literacy curricula, certification standards for educational AI applications, and ethical governance guidelines for data use and privacy. This study contributes uniquely by moving beyond descriptive reviews, offering predictive foresight that informs educators, policymakers, and researchers about both current trends and future trajectories of AI in science education.</p>

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Bibliometric analysis and predictive modeling map the role of artificial intelligence in science education from research trends to classroom integration

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月31日

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  • Kadir Kesgin

摘要

Artificial intelligence (AI) is rapidly transforming science education by enabling adaptive learning, virtual experimentation, and generative applications such as ChatGPT. Yet, research in this area remains fragmented and often descriptive. This study adopts an integrated approach, combining bibliometric analysis, meta-analysis, and predictive modeling, to map the research landscape, synthesize empirical impacts, and forecast future directions of AI in science education. The bibliometric analysis of 3217 studies (2015–2024) identified four major thematic clusters: intelligent tutoring and adaptive learning, virtual and remote laboratories, generative AI and ethics, and teacher professional development. The meta-analysis of 39 empirical studies revealed a significant overall positive effect of AI on science learning outcomes (Hedges’ g = 0.63, \(I^2 = 89\%\) ), with stronger effects in higher education (g = 0.75) and low- and middle-income country (LMIC) contexts (g = 0.82). Predictive modeling using Support Vector Regression (SVR) projected continued growth in AI and science education research until 2030, with signs of thematic saturation after 2028. The findings extend constructivist, sociocultural, and Bloom’s frameworks by showing how AI fosters individualized inquiry, collaborative learning, and higher-order cognitive tasks. Practically, they highlight the need for teacher training frameworks such as Technological Pedagogical Content Knowledge (TPACK) and the 5E model to ensure meaningful integration of AI tools. Policy implications include the development of AI literacy curricula, certification standards for educational AI applications, and ethical governance guidelines for data use and privacy. This study contributes uniquely by moving beyond descriptive reviews, offering predictive foresight that informs educators, policymakers, and researchers about both current trends and future trajectories of AI in science education.