Comparative analysis of artificial intelligence tools for creating adaptive learning courses

Abstract

The growing role of artificial intelligence in education creates a need for effective tools for adaptive learning for schoolchildren and students. Artificial intelligence enables the personalization of the educational process, automation of content creation, and enhancement of learning platform efficiency. However, the implementation of these technologies presents challenges, including algorithmic transparency, integration with traditional learning systems, and the need to adjust artificial intelligence models to the individual characteristics of learners. The aim of the study is to conduct a comparative analysis of artificial intelligence tools used for creating adaptive learning courses, focusing on their effectiveness, level of content personalization, and impact on the quality of the educational process. Methods include comparative analysis, systematization, and forecasting the development of artificial intelligence tools in education. A structural analysis of adaptive platforms was conducted to determine their level of integration with learning systems, capabilities for automated knowledge assessment, and the flexibility of personalization parameters. Results indicate that AI-based adaptive learning systems significantly improve the personalization of the learning process by analyzing behavioral patterns of students, predicting learning difficulties, and optimizing educational tasks. The most effective platforms use hybrid learning methods that combine machine learning, natural language processing, and cognitive modeling. The main challenges in implementing such systems include algorithmic biases, high dependence on large educational datasets, and technical constraints. Recommendations include improving the accuracy of adaptive models through the enhancement of educational data analysis algorithms, expanding artificial intelligence integration into learning management systems, and developing mechanisms for decision explainability.Conclusions. Artificial intelligence tools for adaptive learning enhance personalized education and optimize the learning process. However, their implementation requires overcoming technical and ethical challenges, particularly concerning algorithmic transparency and the use of personal data. Further research should focus on integrating augmented and virtual reality technologies into adaptive learning, developing ethical standards for artificial intelligence use, and refining algorithms for adapting educational platforms to individual learning styles.