Artificial Intelligence in Assessment: A Bibliometric Review of Research Development, Thematic Patterns and Research Clusters

Abstract

This study presents a bibliometric analysis of 68 Scopus-indexed publications from 2011 to July 2025 on artificial intelligence (AI) in assessment. The dataset recorded 1,581 total citations, with an h-index of 22 and g-index of 38, indicating steady growth that intensified after 2021 as AI adoption expanded in post-pandemic education. The United States, Germany, and the United Kingdom emerged as the most productive contributors, supported by high-impact institutions such as Michigan State University, Purdue University, and the IPN Leibniz Institute for Science and Mathematics Education. Influential authors, including Haudek and Zhai (2024), Kubsch et al. (2023), and Wulff (2023), have advanced the field through interdisciplinary research on automated feedback, machine learning, and data-driven evaluation. Keyword co-occurrence analysis revealed three dominant thematic clusters: technology-enhanced learning and automated feedback, machine learning with ethical considerations, and AI-supported formative assessment in science and engineering education. While the findings highlight AI’s transformative potential for adaptive, feedback-oriented learning, challenges persist in ensuring accessibility, curricular integration, teacher readiness, and ethical governance. Overall, this study provides a comprehensive overview of global trends, leading contributors, and emerging themes that inform future research and policy directions in AI-driven educational assessment.