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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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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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Прогнозная оценка потенциального рынка электромобилей и эффектов снижения выбросов парниковых газов в России

Journal of Applied Economic Research. 2023. Т. 22. № 3. С. 497–521.
Колян Н. С., Plesovskikh A., Гордеев Р. В.

In recent decades, the global adoption of alternative fuel vehicles, which may contribute to carbon emission reduction due to the use of alternative energy sources has stirred particular interest. Despite a significant body of scientific literature in Russia about electric vehicle adoption, the approaches used in papers lack quantitative estimates of the Russian market's potential. This paper aims to fill this gap as it provides a long-term electric vehicle market forecast in Russia as well as assesses the environmental effects. The following hypotheses are tested: (1) the Bass model is applicable to predict the long-term electric vehicle diffusion process in Russia; (2) the transition to electric cars will have a significant impact on greenhouse gas emission reduction. The Bass model, a widely used tool for predicting the innovation diffusion process, serves as a methodological base for the research. The long-term forecast of the Russian electric car fleet includes several scenarios. The most realistic scenario suggests that the Russian electric vehicle market is estimated to grow, reaching 5.62 million units by 2060. Furthermore, the environmental effects associated with electric vehicle adoption were identified. Two scenarios for changes in the energy generation structure were taken into consideration. The expected carbon emission reduction is estimated to reach 14.08 million tons in CO2-eq. if an accelerated transition to low-carbon energy sources is implemented, the baseline scenario suggests 12.86 million tons in CO2-eq. carbon emission reduction. The estimates of the transport diffusion in Russia as well as of environmental effects associated with this process form the theoretical value of the study. The practical significance of the study suggests developing electric vehicle demand forecasts that might be utilized while implementing measures to achieve goals stated in the Strategy of Social and Economic Development with a Low Level of Greenhouse Gas Emissions until 2050 in the Russian Federation.

Language: Russian
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Keywords: диффузия инновацийнизкоуглеродная экономикаэлектромобилиelectric vehiclesInnovation diffusionLow-carbon economyelectric vehicle fleet forecastingpotential marketBass modelпрогнозирование численности автопаркапотенциальный рынокмодель Басса
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