<p>This study presents the design, implementation, and evaluation of an intelligent tutoring system tailored for dynamic learning domains such as computer science. The objective was to create a system that adapts not only to rapidly changing curricula, but also to diverse learner trajectories and self-regulated learning behaviors. The proposed system is built on the Evolving Knowledge Space Graph model, a graph-based knowledge representation framework that supports dynamic curriculum structure. The model was combined with a relational database architecture to facilitate real-time learning tracking, adaptive content delivery, and data-driven decision making. Furthermore, a generative AI-based assistant was used to automatically generate domain-specific content, including knowledge units, prerequisite relations, and quiz questions, thus significantly reducing the author’s time. The system incorporates multiple adaptive learning algorithms. The model uses intuitionistic fuzzy logic to represent learner knowledge states and forgetting over time. A classroom study with 45 participants was conducted to evaluate structural clarity, usability, learning outcomes, and algorithm performance. The results indicate that the integrated approach, combining graph-based modeling, database-driven tracking, and generative content creation, successfully supports the navigation of the learners and the comprehension of the domain. The Bayesian knowledge propagation algorithm demonstrated the highest knowledge gains, while log-data analysis revealed significant differences in self-regulated learning patterns between learners. These findings highlight the potential of hybrid ITS architectures that leverage structured domain models, relational data infrastructure, and generative AI to support personalized and scalable learning in dynamic educational settings.</p>