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News
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.
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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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Demographic characteristics as determinants of retail customers’ credit behavior. Evidence from Russian regions

Procedia Computer Science. 2023. Vol. 221. P. 1091–1098.
Arkhipova N., Karminsky A. M.

This paper investigates and defines the concept of "credit behavior". A number of factors influencing credit behaviour of the population were identified. Among them are economic factors, e.g. income and expenses, debt burden, unemployment, interest rates, etc. Another important group of factors impacting credit behavior is social aspects, i.e. level of education, social status, financial and digital literacy, and others. The next important group is demographic factors which include gender and age characteristics, family composition, etc. The research found rigid and positive dependence between the volume of per capita lending with per capita income, which can be explained by the fact that the amount of income directly determines “solvency” of a client. A positive relationship is also observed when assessing the link between the volume of per capita loans and availability of hospital beds.

Research target: Economics and Management
Language: English
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DOI
Text on another site
Keywords: credit behaviorRetail banking credit riskDemographic characteristics
Publication based on the results of:
Innovations in the banking sector, its financial stability and prudential regulation in conditions of permanent crises (2023)
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