Global research on AI-empowering academic evaluation: advances, trends, and prospects

Purpose This paper aims to systematically review the current research on artificial intelligence (AI)-empowering academic evaluation, and subsequently construct a theoretical framework that aligns with the era of digital intelligence so as to enrich intelligent academic evaluation theory and promote its practical exploration, thereby providing a reference for future studies in the field of AI-empowering academic evaluation. Design/methodology/approach This paper uses bibliometric and content analysis methods. Using the SSCI database within the Web of Science Core Collection as the data source, 587 valid bibliographic records of journal articles in the field of AI-empowering academic evaluation are retrieved and screened. CiteSpace software is used to create keyword clustering knowledge mapping and time period knowledge mapping for research topics from 2015 to 2025. Through these visualizations, the current research landscape and development trends in this field are analyzed in depth. A theoretical framework for enhancing AI-empowering academic evaluation is constructed, and future research prospects are proposed. Findings This paper elaborates on the research progress in the field of internationally AI-empowering academic evaluation across six clustered themes: AI and academic evaluation theories and methods, AI and academic evaluation objects, AI and academic evaluation indicators, AI tools empowering academic evaluation, AI-empowering academic evaluation practices, and AI triggering academic evaluation risks and governance. By analyzing the time period knowledge mapping, the study divides the research on AI-empowering academic evaluation into three developmental stages: the initial exploration phase, the integration and expansion phase, and the GenAI-driven paradigm reshaping and reflection phase, and further examines its future trends. Based on this analysis, the paper constructs a theoretical framework for enhancing AI-empowering academic evaluation and proposes six key research prospects and development strategies: drive the intellectualization and efficiency of academic evaluation theories and methods; promote the diversification and dynamics of academic evaluation objects; assist the meaning and integration of academic evaluation indicators; enhance the contextualization and interactivity of AI tools; achieve consensus-based and flexible academic evaluation systems; Promote the systematization and globalization of AI governance. Originality/value This paper enriches the theoretical research on AI-empowering academic evaluation, identifies key leverage points that can be translated into policy instruments, provides insights into the application directions and practical pathways of AI technology in academic evaluation research, and contributes to promoting the deep integration of academic evaluation systems with AI technology and their intelligent transformation.