Аңдатпа
This study aims to address the challenges of unstructured learning and information overload in digital education by developing and analyzing a digital learning model integrated with artificial intelligence (AI) to enhance the flexibility and accessibility of education. The methodology is based on system analysis and simulation modeling. A mathematical model was created to describe the interaction between learners, the digital platform, and educational content, incorporating key parameters for flexibility, accessibility, and knowledge acquisition. This model was then modified to assess the impact of AI on platform adaptability, content interactivity, student motivation, and learning rate. The simulation was implemented in Python using the NumPy and Matplotlib libraries. The simulation results demonstrate that while AI tools for adaptability and interactivity increase system flexibility, their overall impact on effectiveness is limited by a plateau effect. The most significant improvements in overall effectiveness are driven by AI components that optimize knowledge acquisition (AIk) and student motivation (AIm). Accessibility remained constant throughout the simulations, indicating that it requires separate optimization strategies beyond the pedagogical scope of this model. The model provides practical recommendations for educational institutions, prioritizing investments in AI solutions for personalization and motivation to achieve the greatest impact on learning outcomes. The proposed framework can serve as a foundation for designing and implementing next-generation intelligent learning systems. This study confirms that the strategic integration of AI can significantly increase the flexibility and effectiveness of digital learning. The findings underscore the importance of focusing AI implementation not just on technological features but on core pedagogical drivers like student motivation and optimized knowledge acquisition.