Today, the effective use of artificial intelligence technologies in education is becoming an urgent problem in various fields of activity. Scientific and pedagogical research should be focused on developments in the field of digitalization of education. This article discusses the possibilities of personalizing adaptive learning based on results using artificial intelligence. The purpose of the article is to study the methods of using artificial intelligence technologies for data analysis by a teacher and the use of a large language model (LLM) to make recommendations for certain groups of students. Such methods allow the teacher to identify students at risk, identify knowledge gaps in advance, and transform the content of educational platforms to integrate learning materials and verify knowledge acquisition. 100 students participated in this study, the results of their observations and the activity of their participation in the educational process were analyzed using the K-Means clustering algorithm and three clusters with different levels of knowledge and needs were identified. The LLM then forms learning strategies using recommendations on the selection of educational content for each cluster. Based on these recommendations, the teacher reviews and supplements educational materials, implements them into educational platforms and gives feedback. This increases the effectiveness of the personalization of training at its stage. The implementation of this approach will increase student engagement based on the capabilities of artificial intelligence and pedagogical experience, support them, significantly improve the quality of education, increase interest in learning, and ensure timely and accurate responses. the teacher's response to the student's needs.