This article analyzes the development model of an adaptive testing system for educational platforms based on dynamic assessment of the user's knowledge level. The study examines Item Response Theory (IRT), the Bayesian Knowledge Tracing (BKT) model, and the application of the Elo rating system in education. The article provides a detailed description of the proposed system's architecture, functional modules, question difficulty ranking mechanism, adaptive algorithm, and monitoring processes. Experimental results are compared with traditional testing systems, demonstrating that the adaptive system improved assessment accuracy by 21% and reduced test duration by 45%. The prospects for integrating the system with artificial intelligence technologies are also discussed.