Artificial intelligence in educational assessment in the age of generative AI: A bibliometric review

Abstract

The rapid diffusion of artificial intelligence (AI), particularly generative AI tools, has significantly reshaped educational assessment practices, creating new opportunities and challenges for feedback, academic integrity, and curriculum design. Despite the growing volume of scholarship, research in this area remains fragmented, making it difficult to discern dominant trends, key contributors, and emerging themes. This study employs a bibliometric review to map the intellectual structure and evolution of research on AI in educational assessment between 2015 and 2025. Using open-access journal articles indexed in the Dimensions.ai database and aligned with Sustainable Development Goal 4 (Quality Education), a curated dataset of 89 studies was analysed. Bibliometric techniques, including co-authorship, citation, co-citation, and keyword co-occurrence analyses, were applied using VOSviewer and descriptive statistics. The findings reveal a sharp growth in publications following the emergence of generative AI, with influential clusters focusing on assessment redesign, feedback, academic integrity, and higher education applications. While established institutions and authors dominate the field, collaboration networks remain fragmented, and contributions from non-Western contexts are comparatively limited. The study highlights the need for stronger international collaboration and context-sensitive research to support equitable and responsible integration of AI in educational assessment.

Keywords

Artificial intelligence / Generative AI / Educational assessment / Bibliometric analysis / Higher education / Academic integrity / Sustainable Development Goal 4

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