Using artificial intelligence to create mathematical modeling educational tasks

the aim of this research is to investigate the potential applications of artificial intelligence (AI) technologies in developing adaptive and interactive educational tasks in mathematical modeling specifically tailored for students of railway engineering institutions. This study examines contemporary AI methods, including machine learning and neural networks, and their capabilities in adaptive education. The primary objectives encompass the analysis of practical implementations of tasks related to train schedule optimization, passenger flow forecasting, and railway station operation modeling. Particular emphasis is placed on a humanized approach to teaching that fosters creative and critical thinking while personalizing the educational process. This approach is designed to develop key competencies essential for professionals in the transportation sector. The presented findings demonstrate the significant effectiveness of AI integration into the educational process, indicating promising avenues for further research in this domain. The humanized approach, based on personalization and modern technology integration, not only enhances educational quality but also contributes to the formation of professionals capable of addressing complex practical challenges in the transportation industry.

artificial intelligence, mathematical modeling, adaptive learning, railway engineering education, personalized learning, train scheduling optimization, critical thinking