Implementation of Generative Artificial Intelligence Models in Students’ Research Activities as a Factor in the Modernization of Research Training in Ukraine

Abstract

The purpose of the study is to substantiate the pedagogical potential of integrating generative artificial intelligence tools into the scholarly activity of higher education students as a factor in the modernization of research training in Ukraine. The article highlights the transformation of traditional approaches to organizing student research under conditions of digitalization and the rapid development of artificial intelligence technologies. Particular attention is paid to the need for updating methodological frameworks that regulate the use of AI-driven instruments in academic environments while preserving fundamental educational values. The research methods are based on systemic, competence-based, and activity-oriented approaches. The study includes analysis and generalization of contemporary scholarly publications, examination of regulatory documents in higher education, comparative analysis of existing practices of AI-assisted learning and research support, and modeling of integration strategies within institutional settings. Content analysis, structural-functional analysis, and logical generalization were applied to determine the instructional capacities of generative tools and to outline the principles of their responsible implementation in research training. The results of the study demonstrate that the pedagogically guided use of generative artificial intelligence enhances the effectiveness of students’ research preparation, improves information retrieval and analytical processing, and supports the development of academic writing skills, critical thinking, and digital competence. It has been established that structured integration of such tools promotes personalization of scholarly tasks, facilitates interdisciplinary knowledge synthesis, and increases motivation for research engagement. At the same time, the study identifies risks related to academic integrity, overreliance on automated systems, and the necessity of clear institutional regulations governing AI application in education. The conclusions confirm that the systematic incorporation of generative AI instruments into research training represents a significant direction for higher education development. Their implementation contributes to curriculum renewal, competence-oriented instruction, and the strengthening of institutional innovation capacity. The findings may be applied in the design of educational standards, quality assurance procedures, and strategic policies aimed at sustainable digital transformation in higher education.