Abstract
This article presents an expanded methodology for the development and implementation of an AI-enhanced advanced mathematics course designed to strengthen the analytical, mathematical, and digital competencies of IT students. The course integrates key mathematical domains – linear algebra, mathematical analysis, and probability theory – with practice-oriented tasks from the field of information technology, such as machine learning, data analysis, algorithm optimization, and predictive modeling. This integrative framework enables students to apply theoretical mathematical concepts to realistic professional contexts and enhances their understanding of how abstract models underpin modern computational solutions. The instructional process is facilitated through a modular digital platform equipped with artificial intelligence. The system continuously analyzes learner behavior, including error patterns, task completion time, performance progression, and the frequency of interaction with supplementary materials. Based on these data, the platform adapts task difficulty, provides personalized recommendations, delivers contextual explanations, and generates individualized learning trajectories. Such adaptive support increases student engagement, promotes autonomous learning, and bridges the gap between theory and applied IT practice. A pedagogical experiment conducted with 120 first-year students demonstrated the effectiveness of the proposed course. The average final test score in the experimental group was 22% higher than in the control group, while students’ self-reported confidence in applying mathematical knowledge to real IT tasks increased by 35%. These findings confirm the significant potential of AI-enhanced mathematics courses to improve academic performance, support competency development, and strengthen the career readiness of future IT professionals.