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September 11, 2026
How to Assess Students Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
September 9, 2026
‘Balkan Hospitality Opens Doors: Studying Dialects on the Verge of Extinction
You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.
September 9, 2026
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.

 

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Роль высшего учебного заведения в пространственной поляризации молодежи

Экономика региона. 2019. Т. 5. № 4. С. 1103–1114.
Gabdrakhmanov N., Никифорова Н. Ю.

The expansion of applicants’ opportunities for choosing a university, unfortunately, did not completely eliminate the spatial factor in the process of selecting an educational institution. The majority of students are still graduates of local schools. The conducted analysis allows considering university as an element of the implementation of the strategic goal aimed at preserving and attracting human resources, as well as accumulating human capital. Thereby, it confirms that universities play an important role in the region’s socio-economic development by preserving and attracting youth of youth. For analysing the demographic consequences of the spatial polarization of youth we used the Lexis grid. The curve of the dynamics of the number of cohorts, built on the basis of the grid, revealed the most mobile group among the population aged from 17 to 19. The analysis of the existing Russian and foreign approaches to assessing the university’s role in regional development made possible making an analogy with the law of universal gravitation concerning this study. We proposed a gravity model and tested it at Kazan Federal
University, one of ten federal Russian universities. The results of the study allowed assessing the spatial effect of the university on the polarization of youth. In this study, we hypothesised that there is a neighbourhood effect in higher education, namely an educational migration between neighbouring regions with different levels of socio-economic development. We tested this hypothesis in the geographical framework of the Volga Federal District using the official statistics and admission data of Kazan Federal University. The constructed gravity model of the university allowed assessing the scale and strength of this influence on attracting applicants from neighbouring subjects. Moreover, it allowed determining the regions of potential growth of influence. Such regions include the Republic of Chuvashia, the Orenburg Region and the Republic of Bashkortostan due to demographic conditions, logistics and the positioning of Kazan Federal University in the Volga Federal District.

Priority areas: economics management state and public administration
Language: Russian
Full text
DOI
Keywords: высшее образованиеПриволжский федеральный округобразовательная миграцияаттракторгравитационная модель миграцииVolga Federal districteducational trajectoryneighborhood effectshigher education gravity modeleducational migrationсоседствоРеспублика Татарстанterritorial accessibility Republic of TatarstanКазанский федеральный университетсетка ЛексисаKazan federal universityLexis diagram
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