Using artificial intelligence tools for citation analysis in scientific research

Abstract

The study’s relevance stems from the rapid growth in the number of scientific publications and the increasing need for effective analytical tools capable of processing large volumes of citation data in modern scientific environments. The purpose of the study is to characterize the potential of artificial intelligence tools for citation analysis in scientific research and to determine their role in improving the efficiency of scientific and educational activities. The following methods were used in the work: analysis of the scientific literature to review current developments in the work’s topic; generalization and systematization to present the study’s results. Results. The article considers theoretical and practical aspects of using artificial intelligence technologies for citation analysis and evaluation of scientific research. It is established that the integration of algorithmic tools significantly expands the capabilities of automated processing of scientific information and facilitates the identification of citation relationships between publications, authors and scientific institutions. Artificial intelligence algorithms allow the automatic extraction of references from scientific texts, the construction of citation networks, and the analysis of patterns of scientific knowledge dissemination across various fields of science. It is noted that modern digital platforms equipped with artificial intelligence technologies offer researchers advanced analytical capabilities, in particular, identifying influential publications, visualizing citation interactions and identifying new scientific trends. It is established that intelligent analytical systems help scientists find relevant sources, assess the impact of scientific publications and identify promising areas for further research. At the institutional level, the use of artificial intelligence in citation analysis supports strategic decision-making in the management of scientific research and the assessment of scientific productivity. It is substantiated that the integration of artificial intelligence technologies into bibliometric and scientometric analysis contributes to increasing the transparency, objectivity and analytical depth of scientific evaluation processes. Conclusions. The results confirm that artificial intelligence tools are an important component of modern scientific analytics and create new opportunities for developing evidence-based approaches to assessing the effectiveness and impact of scientific research.