USE OF ARTIFICIAL INTELLIGENCE TOOLS IN TRAINING ECONOMISTS

Abstract

The article substantiates the relevance of a systemic and methodologically grounded approach to the integration of artificial intelligence (AI) tools into higher education in the process of training future economists. AI is interpreted not as an alternative to traditional teaching, but as a complementary instrument that enhances the quality of learning outcomes, supports the formation of professional competencies, and expands the possibilities of data-driven analysis while preserving the decisive role of direct interaction between teachers and students. The study emphasizes that the use of AI technologies contributes to the modernization of the educational environment, increases the adaptability of educational programs to the requirements of the digital economy, and strengthens the analytical component of economic training.

A functional classification of AI tools used in the educational process is proposed. It includes text and analytical assistants for processing educational information, tools for data analysis and visualization that facilitate the interpretation of economic indicators, and modelling and forecasting systems that enable students to develop applied skills in scenario analysis and decision-making. Such differentiation makes it possible to structure the didactic use of AI according to the specifics of academic disciplines and the expected learning outcomes. The main areas of AI application in higher education are identified, including information and analytical support for learning, automation of routine data processing, individualization of learning trajectories, adaptive feedback, and the implementation of project- and case-based learning focused on solving real economic problems.

At the same time, the article highlights a number of limitations and risks associated with the use of AI in the training of economists. Among them are the fragmentation of methodological approaches to AI implementation, insufficient digital and pedagogical competencies of academic staff, threats to academic integrity due to uncontrolled use of generative systems, the possibility of algorithmic bias, and challenges related to personal data protection and cybersecurity. These factors necessitate the development of institutional policies and internal regulatory documents that would define clear rules for the responsible and pedagogically justified use of AI in the educational process.

The study proposes key directions for regulating the use of AI tools at the level of higher education institutions. These include defining principles of use, integrating AI into the content of academic disciplines, developing methodological recommendations for the design of educational tasks, updating approaches to assessing learning outcomes, ensuring mechanisms for maintaining academic integrity, and incorporating ethical standards and digital responsibility into educational practice. Special attention is paid to the need for professional development of teachers and the formation of students’ critical thinking skills in working with AI-generated outputs. The conducted TOWS analysis of strengths and weaknesses, opportunities and threats demonstrates that effective integration of AI into economic education requires a balanced combination of technological potential, pedagogical supervision, and ethical safeguards. The results confirm that AI can significantly enhance the analytical, research, and practical components of the training of economists, provided that its use is methodologically structured and institutionally regulated. The article concludes by outlining prospects for further research, in particular the development of competency-based models of AI use in economic education, the assessment of learning effectiveness in AI-supported environments, and the creation of unified standards for the responsible integration of AI into higher education.