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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.
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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
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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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Скоринг в розничном кредитовании: распространенные ошибки и их стоимость

Финансы и бизнес. 2021. Т. 17. № 4. С. 3–17.
Маракуева М. А.

 

The article discusses the most common mistakes in banking practice when constructing scoring models for assessing the credit risk of individuals. For a detail description, we have selected the most significant ones from the point of view of generating retail profit. With the example of logistic regression we show how much the price of each of the described mistakes. We propose some solutions to avoid common mistakes and losses of the bank when using scoring models. The article can be useful for employees of credit institutions, both those involved in building models, and middle and senior managers.

Research target: Economics and Management
Language: Russian
DOI
Text on another site
Keywords: кредитный рисккредитный скорингскоринговая модельвнутренний кредитный рейтингбанковские кредитные решениякредитная политика банкаcredit risk assessment modelcredit risk modelsBank credit decisionsмодельный рискscore calibration credit riskmodel riskAccuracy scoreскоринговая оценкастоимость ошибки скорингамодель оценки кредитного риска
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