Artificial intelligence in assessment processes in higher education: a bibliometric analysis (2014-2024)
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INTRODUCTION. Artificial Intelligence (AI) in higher education represents a significant innovation that is transforming teaching, learning, and academic assessment. The latter is essential for providing feedback to students, evaluating their understanding, and fostering continuous learning improvement. The aim of this study is to analyze the scientific production related to AI in assessment in higher education over the last decade. This analysis covers chronological and geographical productivity, as well as a detailed study of sources, keywords, and citation counts of the most prominent articles in this field. METHOD. A bibliometric and systematic review was conducted following the guidelines of the PRISMA Statement. RESULTS. Initially, 2,275 studies were identified in the Web of Science database, and after applying eligibility criteria, 130 empirical studies were selected for further analysis. DISCUSSION. The research reviewed concludes that the primary methods of integrating AI in the assessment process include automated feedback, prediction of academic performance through AI-based data analysis, the use of language models such as ChatGPT, and the consideration of associated ethical issues. A significant increase in the volume of publication was observed during the 2023-2024 period, with the United States leading scientific production and Education Sciences being the main source of dissemination. The co-word analysis reveals a lack of terminological uniformity, suggesting a need to standardize language to improve clarity in the field.
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