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<p>Artificial intelligence (AI) is revolutionising scholarly impact evaluation and prediction. By integrating AI and machine learning techniques, researchers can leverage diverse academic networks and multiple sources of academic big data. This integration transforms traditional evaluation methods that rely on structured measurements such as citation counts and journal impact factors, into more comprehensive and objective evaluations. In this paper, we dive deep into latest advancements in scholarly impact evaluation and prediction within the context of AI. We categorize existing models, highlighting their similarities and distinctions, with a particular emphasis on AI-enabled approaches. Building upon the analysis, we discuss the ongoing challenges in scholarly impact research and outline future directions in this field.</p>

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Revolutionizing scholarly impact: advanced evaluations, predictive models, and future directions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月15日

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  • Xiaomei Bai,
  • Fuli Zhang,
  • Jiaying Liu,
  • Xiaoxia Wang,
  • Feng Xia

摘要

Artificial intelligence (AI) is revolutionising scholarly impact evaluation and prediction. By integrating AI and machine learning techniques, researchers can leverage diverse academic networks and multiple sources of academic big data. This integration transforms traditional evaluation methods that rely on structured measurements such as citation counts and journal impact factors, into more comprehensive and objective evaluations. In this paper, we dive deep into latest advancements in scholarly impact evaluation and prediction within the context of AI. We categorize existing models, highlighting their similarities and distinctions, with a particular emphasis on AI-enabled approaches. Building upon the analysis, we discuss the ongoing challenges in scholarly impact research and outline future directions in this field.