AI-driven Assessment in Education: A Bibliometric Analysis and Framework for Assessment Design and Governance
Keywords:
artificial intelligence; generative AI; educational assessment; bibliometric analysis; human-AI collaboration; assessment governance; educationAbstract
The rapid advancement of artificial intelligence (AI), particularly generative AI and large language models, has transformed educational assessment practices, raising concerns regarding assessment validity, academic integrity, human judgement, and governance. This study aims to map the intellectual landscape of AI-driven assessment research and synthesise key developments that inform the design, implementation, and governance of AI-supported assessment. A bibliometric analysis was conducted on 820 Scopus-indexed publications published between 2015 and 2025. Using VOSviewer, publication trend analysis, keyword co-occurrence analysis, and bibliographic coupling analysis were employed to examine research productivity, thematic evolution, and intellectual structures within the field. The findings reveal a substantial increase in publications following the emergence of generative AI technologies, particularly between 2023 and 2025. Four major thematic areas were identified: generative AI and educational innovation, assessment and evaluation, AI technologies and learning systems, and disciplinary applications. Bibliographic coupling analyses further indicate growing intellectual convergence around assessment redesign, human-AI collaboration, academic integrity, and responsible AI use. These findings suggest that AI-driven assessment has evolved beyond technological implementation toward a broader socio-technical discourse encompassing pedagogical, ethical, and governance considerations. Based on the synthesis of the findings, this study proposes the Human-AI Governance and Design (HAGD) Framework, comprising three interdependent dimensions: Assessment Design, Human-AI Role Allocation, and Governance and Oversight. The framework provides a structured foundation for the responsible integration of AI into educational assessment while maintaining educational quality, fairness, transparency, and public trust. The study contributes to the growing discourse on AI-driven assessment and offers practical insights for educators, assessment designers, and policymakers.
https://doi.org/10.26803/ijlter.25.7.31
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