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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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Stratified Ambitions: Family Social Origin and Unequal Early Career Outcomes among Russian Graduates

Journal of Youth Studies. 2026. P. 1–20.
Nikitin M., Maltseva V.

This article examines how family social origin shapes early career outcomes among university graduates in Russia, focusing on differences in job task content rather than conventional indicators such as wages or occupational status. The study combines longitudinal panel data from the Trajectories in Education and Careers project with biographical interviews to analyse both structural patterns and the mechanisms underlying them. The quantitative findings show that graduates from highly educated families are significantly more likely to enter non-routine jobs characterised by greater cognitive complexity, autonomy, and opportunities for professional development. The qualitative analysis demonstrates that these patterns are reinforced through classed differences in career decision-making under conditions of uncertainty. Graduates from more advantaged backgrounds draw on parental guidance and cultural resources to pursue long-term, risk-tolerant strategies, whereas those from less privileged families tend to prioritise stability and immediate employment. By focusing on job task content as an indicator of job quality, the article shows how social inequality is reconfigured within the graduate population. It contributes to research on youth transitions and social reproduction by demonstrating that family background continues to shape the qualitative nature of work performed at the start of professional careers.

Research target: Sociology (including Demography and Anthropology Education
Language: English
Full text
DOI
Keywords: социальная мобильностьsocial mobilityобразовательные переходыsocial originjob-task contentyouth transitionsgraduate employmentlabour market inequality
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