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September 25, 2026
AI Users Earn Up to 41.8% More Than Non-Users
Research conducted by economists at HSE University has revealed a significant correlation between the regular use of GenAI in the workplace and higher pay among Russian employees. The study found that individuals who frequently use GenAI in their professional activities earn notably more than those who reject these new tools or resort to them occasionally. The salary premium for highly qualified specialists reaches 41.8%. The article was published in the Voprosy Ekonomiki journal.
September 24, 2026
‘Feedback and Constructive Criticism Are Essential in Our Profession
Vincent Fardeau, Associate Professor at HSE ICEF, has reached a major career milestone: he recently published his paper ‘Asymmetric Thin Markets’ in the Journal of Financial Economics, successfully passed his major academic review, and received tenure. In this interview, Vincent discusses the story behind the paper, explains the concept of asymmetric thin markets, and shares his advice for young scholars aiming to publish in top-tier journals.
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Personal Interest in Doctoral Thesis Topic Most Important for Confidence in Successful Defence
A researcher at HSE University analysed data on 1,539 doctoral students from 161 Russian universities to identify which features of a thesis topic are associated with academic success and engagement. The most important factor was found to be personal interest in the research topic, which was associated with almost all key aspects of doctoral programme experience—from engaging with the academic supervisor to research activity and confidence about successfully defending the thesis. The findings have been published in Higher Education.

 

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Following the Template: Transferring Modeling Skills to Nonstandard Problems

Russian Education and Society. 2017. Vol. 59. No. 5-6. P. 298–318.
Tyumeneva Y. A., Goncharova M.

This study seeks to analyze how students apply a mathematical modeling skill that was previously learned by solving standard word problems to the solution of word problems with nonstandard contexts. During the course of an experiment involving 106 freshmen, we assessed how well they were able to transfer the mathematical modeling skill that is used to solve standard problems to the solution of nonstandard ones that had an analogous structure. The results of our research show that students had varying degrees of success applying the different stages of modeling depending on whether they were solving a familiar problem (involving near transfer) or one that had an unfamiliar context (involving far transfer): in cases of near transfer, students applied the template formally even though it did not align with the text of the new word problem, which complicated further interpretation. In cases of far transfer, students chose to solve the problem by using an ordinary method of selecting a solution by trial and error in preference to the use of modeling. Thus, the application of the modeling skill as a multistage process is complicated when solving nonstandard problems involving either near or far transfer.

Research target: Education Psychology
Priority areas: humanitarian
Language: English
Full text
DOI
Keywords: математическое моделированиепереносtransferatypical contextтекстовая задачанетипичный контекстtextual math problemmathematical modelling
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