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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
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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
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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Network rivalry, Competition and Innovation

Technological Forecasting and Social Change. 2020. Vol. 161. No. article 120253. P. 1–10.
Galvin P., Burton N., Singh P., Sarpong D., Bach N., Teo S.

Strategy, structure and rivalry across an industry has an impact upon innovation outcomes at the industry level. However, when patterns of rivalry are altered through the presence of strategic networks (sets of firms that cooperate closely on the basis of their web of strategic alliances) it is not clear what impact this has upon product market (price) competition and in factor markets (patents). Using data from the motor vehicle industry, we find only limited support for the notion that competitive responses vary with changes in network-level rivalry most likely due to co-opetition and a lack of stability across the networks. The results suggest that firms are likely to engage in co-opetition, thus changes in innovation outcomes can only be observed at the network level. When the presence of strong strategic networks leads to lower levels of rivalry on the basis of at least some cooperative behavior within the network (and competitive actions being focused on firms in other networks) we see a reduction in innovation at the network level. However, as the strategic networks changed consistently over time, the change in patenting behavior was limited.

Priority areas: economics
Language: English
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DOI
Text on another site
Keywords: конкуренцияинновацииinnovationcompetitionсетиstrategic alliancesстратегические альянсыnetworks
Publication based on the results of:
Measuring and modelling innovation in the context of evidence-based policymaking (2020)
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