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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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Repositioning People in Creative Futures: A Method to Create Sound Advice with Exploratory Scenarios

Foresight and STI Governance. 2021. Vol. 15. No. 2. P. 25–38.
Velasco G., Popper R., Miles I. D.

Foresight scenarios are not only useful presentational devices to show that many aspects of the future are open. Scenarios are means for generating advice that helps policymakers initiate actions in the present or near future that will be of long-term significance. Despite the influence that such advice may have on policy decisions, the Foresight literature has paid very little attention to the creation of policy recommendations. Though reports of scenario exercises frequently conclude with lists of recommendations that follow from the study, there is very little explication of the process whereby advice is elicited from the examination of these future scenarios. This paper addresses this gap, examining how the generation of recommendations is related to the development of scenarios within multiple future repositioning workshop settings. It focuses on the fluency and originality of these recommendations, and how this is influenced by repositioning participants in highly transformational scenarios. Repositioning is the process whereby participants are invited to imagine themselves playing roles in hypothetical future contexts, and on that basis to make decisions or devise strategies as if they actually were immersed in these circumstances. The method proposed and the findings of the case study have implications for why and how this future repositioning approach can be incorporated as a ‘key feature’ in the design of Foresight activities. The aim is also to raise awareness of the need for more exploration of Foresight recommendation methodology.

Research target: Economics and Management
Language: English
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Text on another site
Keywords: форсайтforesightкреативностьcreativityfuturesоригинальность soundnessoriginalityfluencyscenariosrepositionEuropean Research Area (ERA)погружение в будущеебудущие инновацииразработка сценариев Европейское исследовательское пространствообоснованностьплодотворность
Publication based on the results of:
Development of a methodological base for a comprehensive study of global trends using neural network language models (2021)
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