Artificial Intelligence in Education: From Automation to Personalization

Abstract

In the context of rapid technological change and the spread of big data, education is the sector most sensitive to the impact of innovative developments. The introduction of artificial intelligence makes it possible to move from traditional, standardized approaches to learning to the personalization of each student’s learning path, thereby ensuring high effectiveness of the learning process and improved knowledge acquisition. With the expansion of the use of algorithms and neural networks, the question arises of ensuring the intelligent coexistence of technology and humans in the educational process, which creates a number of problems related to the loss of interpersonal connections, understanding the educational needs of each student, and developing personalized educational content. The purpose of the study is to analyze the possibilities of applying artificial intelligence in education and to develop practical recommendations for the most effective scenarios for personalizing the educational process while preserving the leading role of the human mentor. The study uses the following methods: systematic analysis of scientific sources, comparative analysis of the practical experience of educational institutions, statistical analysis of student academic performance, and development of scenarios for personalizing learning trajectories using neural network algorithms. In particular, algorithms for personalizing learning tasks and testing knowledge levels were developed and applied using neural networks and big data on student academic performance. The study solved the following tasks: analyzed the current state of the application of algorithms and neural networks in education; developed algorithms for personalizing learning trajectories based on the analysis of students’ academic results; determined the most effective scenario for the coexistence of human mentors and artificial intelligence algorithms in the learning process. As a result of the study, it was concluded that the personalization of learning trajectories using big data algorithms and neural networks is highly effective, which makes it possible to improve students’ academic performance and unlock the potential of each of them.