Mapping the Landscape of Artificial Intelligence (AI)-Powered Assessment: A Bibliometric Analysis of Scopus and Web of Science (WoS)

The surge of interest in Artificial Intelligence (AI) in higher education has led to rapid growth in research regarding its potential applications in assessment. This study analyzes publication trends, document types, and citation patterns related to AI in educational assessment, alongside the emergence of AI-powered literature search platforms. Data from Scopus and Web of Science (WoS) databases (2020–2024) were retrieved for analysis. A total of 42 publications were analyzed using VOSviewer for keyword mapping and cluster identification, while Harzing’s Publish or Perish was utilized for citation metrics. The results show a consistent increase in publications related to AI-based assessment, with articles being the primary format. Keyword analysis revealed dominant clusters centered on student perceptions and automated grading systems. This study provides an updated bibliometric landscape that guides researchers in identifying research gaps and emerging directions in AI assessment, while highlighting how AI-powered search tools can enhance systematic literature mapping.