Marketing Priorities for Artificial Intelligence Technologies Implementation in Engineering and Technical Universities

University marketing priorities are often mistakenly associated with issues of promoting the university and its training programs. However, marketing the activities of a university involves a much wider range of responsibilities, including those related to managing a portfolio of educational programs and improving the educational process. The key issue of this contribution is the introduction of artificial intelligence and machine learning technologies to personalize individual educational trajectories of students in engineering and technical specialties (ETS). The development of artificial intelligence at the present stage al-lows the use of various technologies that perform tasks related to improving the educational process, research activities, managing a portfolio of educational programs, and optimizing university infrastructure. In this context, machine learning offers the potential of predictive analytics for developing recommendation services useful for students, teachers, methodologists, researchers, and university administration. However, not all engineering and technical universities are equally ready to implement such technologies. The key questions of the work are: identifying barriers to the use of digital analytical services, identifying factors for assessing the readiness of a university, as well as building the structure of a student’s educational trajectory.