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Subject
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.
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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Влияние искусственного интеллекта на структуру и содержание вакансий на российском рынке труда

Вопросы экономики. 2025. № 1. С. 71–91.
Skorobogatov A., Свиридов О. И.

The paper studies the association between the artificial intelligence (AI) and employment characteristics. As a theoretical framework, we use the Acemoglu et al. model, which introduces opposing effects of the AI algorithms on labor employment on the firm level such as substitution effect and complimentary/ productivity effects. Depending on their relative strength, the AI algorithms may both decrease and increase employment. The overall effect is estimated using the data on 3.5 million of vacancies for about 35 thousand firms published up to 2022 spring on the HeadHunter website. According to the results, the existence of tasks realizable using AI is consistent with a higher AI employment, which implies the substitution effect. The other, less intuitive, result is that the same tasks suggest a higher non-AI employment, supports complimentary/productivity effects. The overall employment effect is positive as follows from the positive AI tasks—total employment association.

Research target: Economics and Management
Language: Russian
DOI
Text on another site
Keywords: искусственный интеллектавтоматизациявнутренний рынок трудаalgorithmsалгоритмы labor marketsautomatization artificial intelligenceHeadhunter
Publication based on the results of:
Nature in the optics of digital culture: paradoxes, hybrids, phantasms (2024)
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