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<p>Advances in artificial intelligence (AI) and machine learning (ML) are increasingly central to predicting student achievement in science and STEM education. This study uses a quantitative bibliometric design to map the conceptual, methodological, and thematic structure of research in this domain. The dataset comprises 1,073 English-language peer-reviewed journal articles indexed in the Web of Science (WoS) between 1993 and 2025, identified through PRISMA-informed screening and analyzed using Bibliometrix/Biblioshiny. Student achievement is operationalized via outcome-oriented constructs reflected in article metadata (e.g., grades, test performance, persistence/retention, and dropout risk), rather than through primary data synthesis. Findings show strong publication growth after 2010 and a recent decline in average citation impact, largely attributable to shorter citation windows for newer publications. The United States and China lead publication output, while Saudi Arabia, the United Kingdom, and Mexico show high international collaboration. Purdue University and Texas A&amp;M University are among the most productive institutions. IEEE Access, Computers &amp; Education, and Education and Information Technologies emerge as core outlets, and D. Gasevic is identified as the most influential author. Thematic and co-word analyses indicate three dominant knowledge clusters: AI/ML and predictive modelling methods, science/STEM learning contexts, and student-related factors, alongside an emerging axis focused on ethics, explainability, and large language models (LLMs). Overall, the map clarifies dominant themes, evolving trends, and gaps that can inform future comparative studies and responsible AI integration. Limitations include reliance on a single database (WoS) and English-only publications, which may constrain the representativeness of the findings.</p>

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Artificial intelligence and machine learning approaches for predicting student achievement in science and STEM education: a bibliometric analysis

  • 期刊论文

  • 发表日期：

    2026年5月20日

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  • Cihad Şentürk,
  • Ali Yağcı

摘要

Advances in artificial intelligence (AI) and machine learning (ML) are increasingly central to predicting student achievement in science and STEM education. This study uses a quantitative bibliometric design to map the conceptual, methodological, and thematic structure of research in this domain. The dataset comprises 1,073 English-language peer-reviewed journal articles indexed in the Web of Science (WoS) between 1993 and 2025, identified through PRISMA-informed screening and analyzed using Bibliometrix/Biblioshiny. Student achievement is operationalized via outcome-oriented constructs reflected in article metadata (e.g., grades, test performance, persistence/retention, and dropout risk), rather than through primary data synthesis. Findings show strong publication growth after 2010 and a recent decline in average citation impact, largely attributable to shorter citation windows for newer publications. The United States and China lead publication output, while Saudi Arabia, the United Kingdom, and Mexico show high international collaboration. Purdue University and Texas A&M University are among the most productive institutions. IEEE Access, Computers & Education, and Education and Information Technologies emerge as core outlets, and D. Gasevic is identified as the most influential author. Thematic and co-word analyses indicate three dominant knowledge clusters: AI/ML and predictive modelling methods, science/STEM learning contexts, and student-related factors, alongside an emerging axis focused on ethics, explainability, and large language models (LLMs). Overall, the map clarifies dominant themes, evolving trends, and gaps that can inform future comparative studies and responsible AI integration. Limitations include reliance on a single database (WoS) and English-only publications, which may constrain the representativeness of the findings.