RESEARCH ON THE EFFECTIVENESS OF AN EDUCATIONAL SIMULATOR DESIGNED IN UNITY

The article investigates the effectiveness of an educational simulator designed on the Unity platform, which integrates an adaptive mathematical model to manage dynamic learning processes. The study's relevance stems from the limitations of traditional teaching methods (low individualization, high resource demands) and the need for flexible, interactive environments to enhance motivation and knowledge acquisition. The Unity engine was chosen for its cross-platform capability, support for physics modeling, and comprehensive tools for creating VR/AR applications.The main goal was to evaluate the simulator's efficiency compared to traditional learning methods. To achieve this, an adaptive model based on logistic and differential equations was developed, which dynamically adjusts task parameters–specifically difficulty (α), player level (L), and experience (EXP)—according to the user's success rate (S). The mathematical model was implemented using Python libraries (NumPy, SciPy) for computation and C# within the Unity environment for visualization and interactive behavior. A series of numerical simulations across three typical user scenarios (high, medium, and low success rates) confirmed the adaptability and robustness of the algorithm. The analysis of the plots showed that in the high success scenario, task difficulty grew monotonically, and experience accumulation was the fastest. In the low success scenario, the model promptly reduced the difficulty, thereby preventing user frustration and maintaining motivation.Model validation using metrics indicated high accuracy: the coefficient of determination R2 was ≈0.92, and the Mean Absolute Error (MAE) was 0.15–0.25, confirming a high correlation between the modeled curve and the empirical pattern of learning progress. A comparative experiment between simulator-based and traditional learning demonstrated an increase in academic performance by 18–22% and a 15% reduction in task completion time. The practical significance of this work lies in providing a foundation for flexible educational platforms capable of personalizing learning content and sustaining an optimal cognitive load. Future work includes integrating deep learning methods, expanding functionality with VR/AR, and scaling the experiment to a larger user base.