North Caucasus Federal University is actively integrating artificial intelligence into educational activities and scientific developments. Students study neural networks, and scientists apply AI-based technologies to analyze and process big data in medicine and the agricultural sector.

In 2026, Russian President Vladimir Putin instructed to develop and approve a national plan for the introduction of artificial intelligence technologies. The Federal Artificial Intelligence Development Project aims to accelerate technology development in a wide variety of industries, including higher education.

NCFU uses machine learning technologies in scientific research. For example, a group of scientists from the Faculty of Mathematics and Computer Science, led by Pavel Lyakhov, Head of the Department of Mathematical Modeling and Candidate of Physico-Mathematical Sciences, is developing a number of neural network platforms for analyzing medical data, such as skin cancer detection, a program for decoding cardiograms, etc. Scientists from the Faculty of Advanced Engineering at the Federal University have patented a unique AI-based soil and crop analysis system. It allows you to analyze the physico-chemical properties of soil and ice. NCFU Faculty of Medicine and Biology actively uses AI technologies to process big data in the process of DNA analysis, and develops AI platforms for assessing the risk of type 2 diabetes. 

"We consider artificial intelligence as a transformative technology that can and should be used to solve problems in the field of science and higher education. In addition, it is just a tool that helps researchers reduce routine tasks: it analyzes data arrays, models complex physical or biological processes, and generates process development options. To increase the pace of AI implementation in the economy, public and social spheres, it is necessary to train specialists who have digital skills in addition to basic professional education," commented Acting Rector of NCFU Professor Tatiana Shebzukhova.

Thus, at NCFU, at the "Digital Department" (a project of the Ministry of Education and Science and the Ministry of Finance of the Russian Federation, which is implemented under the Priority 2030 program), more than 2,000 students receive digital competencies in parallel with the basic specialty every year. For example, the following disciplines are studied in related fields: "Computational Linguistics and artificial intelligence", "Artificial Intelligence and AR/VR in Education", "Intelligent Information Legal Systems", etc.

Mikhail Babenko, Head of the Department of Computational Mathematics and Cybernetics at the NCFU Faculty of Mathematics and Computer Science named after Professor N.I. Chervyakov, spoke about the experience of staff training and the implementation of scientific projects. 

"The study of AI at our faculty begins already in the second year in the format of familiarization with the capabilities of machine learning (ML) and artificial neural networks (ANN) using the example of popular datasets, that is, training data sets that have already become traditional. Further, as the necessary basic knowledge and skills are acquired from the field of higher algebra and geometry, mathematical analysis, probability theory, mathematical statistics, and, of course, programming, the mathematical apparatus of basic methods and algorithms, and later ensemble algorithms, are mastered. It is important for a student to deeply understand the process of intelligent data processing, and not just teach him to "press the right buttons on the keyboard," said Mikhail Babenko, Doctor of Physico—Mathematical Sciences, Head of the Department of Computational Mathematics and Cybernetics at the NCFU Faculty of Mathematics and Computer Science named after Professor N.I. Chervyakov.

Along with the work of neural networks, NCFU students study the tasks associated with intelligent data processing. Many choose AI-related topics for their final qualifying papers.

"Within the framework of the master's degree, approaches to solving more rare but important tasks of clustering, ranking, and anomaly detection are studied; principles of building recommendation systems, special ANN architectures such as ultra-precise neural networks for working with images, recurrent neural networks for working with natural language and time series analysis, generative-adversarial networks for synthesis are studied. realistic data, transformers, auto encoders, etc., the list is continuously updated with the development of AI," Mikhail Babenko added.

The expert emphasized that graduate students also work with AI, who, as part of their dissertations, carry out research in areas of neural networks that require additional knowledge and skills from related fields. Joint research is also conducted with colleagues from other faculties, institutes, and universities who need advice and support from their AI solutions.