Application of generative artificial intelligence in teaching Business Analysis to undergraduate students
Abstract
This article examines the pedagogical potential of generative artificial intelligence (GenAI) in teaching Business Analysis to undergraduate students in economics-related programs. The relevance of the study is driven by the digital transformation of higher education, the expanding use of generative models in teaching and learning, and the growing demand for students to develop analytical, digital, and professional competencies. The study demonstrates that generative artificial intelligence can be integrated into Business Analysis courses as a tool for preparing instructional materials, developing practice-oriented case studies, simulating professional scenarios, personalizing assignments, and supporting students’ independent learning. The use of GenAI is shown to be particularly effective in disciplines involving the analysis of problems, processes, requirements, stakeholders, and performance indicators. The study identifies the key areas in which GenAI can be applied in the education of students pursuing economics-related degrees. The benefits and risks associated with the integration of GenAI are examined, including enhanced practiceoriented learning, more timely and intensive feedback, and the development of analytical thinking, as well as the risks of superficial learning, reduced learner autonomy, violations of academic integrity, and reliance on inaccurate or unreliable AI-generated responses. The article also presents the results of a teaching and methodological pilot implementation of GenAI elements in the Business Analysis course. The findings indicate that the methodologically sound integration of generative artificial intelligence can improve the quality of the educational process, enhance personalized learning, and contribute to the development of competencies required in the digital economy, while preserving the leading role of the instructor as an organizer, expert, and moderator of educational interaction.