Abstract
The key issue of the article is to determine readiness of HEIs to implement artificial intelligence and machine learning technologies for personalization of students’ individual educational trajectories (hereinafter – IET). In case of compliance of HEIs with the proposed groups of factors, including developed information architecture, there is an opportunity to use multidimensional structured and unstructured data more technologically to improve various types of university activities. High-tech marketing analytical tools facilitate the collection of contextual data and insights into various processes within university, as well as in-depth analysis of learners’ digital footprints. Methods of machine learning, predictive analytics, and modern generative neural networks allow to create recommendation services, with the help of which individual educational trajectories are formed by machine intelligence, simultaneously considering hundreds of parameters. Beyond the tasks of IET formation, machine intelligence can successfully solve tasks for other stakeholders of the university such as professors, researchers, and administration.
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