Analysis of the structure of mathematical training of specialists in the field of artificial intelligence based on the curricula of the bachelor's degree

Modern educational programs aimed at training specialists in artificial intelligence and data analysis require a well-structured and consistent mathematical foundation, which underpins the development of algorithmic and computational skills. The lack of unified approaches to curriculum design leads to significant variability in both the structure and content of mathematical courses. This complicates the comparison of educational trajectories, the formulation of common graduate requirements, and the assessment of training quality. The conducted study focuses on identifying typical structures of mathematical training and classifying them using data analysis methods. An analysis of 46 bachelor’s degree curricula allowed for the determination of the frequency of key mathematical disciplines, their semester-wise distribution, and recurring combinations present in the majority of programs. To formalize educational trajectories, a graph-based model was implemented, where vertices represent disciplines and edges reflect the sequence of their study. Based on a defined distance metric between programs, clustering was performed, resulting in two stable groups of curricula with varying depth of mathematical training, as well as one outlier trajectory that deviates from the typical structure. The findings of this study may serve as a foundation for developing recommendations to standardize curriculum design approaches and for implementing tools for automated analysis and comparison of educational programs.