A Competency-Based Model for Teaching Mathematics in Graduate Schools in the Era of Artificial Intelligence

Abstract

Currently, the dynamic development of artificial intelligence technologies has a significant impact on the content of the education system, learning technologies, and the organization of the educational process. In higher education, especially at the master's level, one of the most important areas of student training is increasing their professional competence, developing independent research skills, and fostering the ability to work in a digital environment. This article discusses the theoretical and methodological foundations of a competence-based model for teaching mathematics in graduate school in the context of artificial intelligence. The study analyzes the integration of mathematical knowledge with modern digital technologies, the didactic possibilities of using artificial intelligence tools, and the conditions for their effective implementation. The author proposes a learning model aimed at developing students' mathematical, research, digital, and professional competencies. The content of the target, substantive, technological, activity-based, and evaluative-effective components of the model is defined, and their interrelation is demonstrated. The results of the study show that the pedagogically sound use of artificial intelligence technologies contributes to improving the quality of mathematics teaching and developing students' analytical thinking, creative abilities, and problem-solving skills. This approach is considered an effective strategy for training graduate students in accordance with modern professional requirements.