Personalized Instruction in Mathematics at Institutions of Higher Education in the Context of the Digital Transformation of the Educational Process

Abstract

This article examines the issue of personalizing the teaching of mathematical disciplines, particularly higher and applied mathematics in institutions of higher education amid the widespread adoption of adaptive systems, intelligent tutors, and artificial intelligence technologies. The purpose of the article is to justify an integrated model for personalizing the teaching of mathematical disciplines based on the systematization of digital technologies, tools, and the pedagogical functions of their application in accordance with the individual needs and level of preparation of students. The methodological framework consisted of a comparative analysis of 21 scientific publications from 2021–2026, systematization, structural-functional analysis, and secondary analysis of open statistical data from the Digital Education Council's Global AI Faculty Survey 2025. The quantitative data set included survey results from 1,681 faculty members at 52 higher education institutions across 28 countries. To compare areas of AI integration, a "positive readiness" indicator was calculated as the sum of the proportions of respondents who are willing to test a particular area or fully support its use. It was found that modern personalization is most effectively implemented through a combination of adaptive adjustment of task complexity and sequence, intelligent tutoring, learning analytics, and generative AI support. Adaptive, intelligent tutoring, AI-oriented, and integrative approaches were systematized, and their functions, advantages, and limitations were identified. According to the results of a secondary analysis, the highest level of positive readiness among instructors was observed regarding fostering students' critical use of AI 71%; creating individualized assignments and instructional materials 70%; and increasing student engagement 68%; the lowest was regarding assessment and providing feedback 46%. The scientific novelty lies in the refinement of the systematization of personalization tools based on the adaptation mechanism and pedagogical function, as well as in the further development of the proposition regarding the necessity of combining automated adaptation with the instructor's pedagogical oversight. The practical significance of the results lies in the possibility of using them to select adaptive systems, intelligent tutors, and AI services; to design individualized educational trajectories; to modernize university courses; and to define the limits of automated assessment.