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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.
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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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The Assessment of Risk Tolerance of Hungarian Travellers Towards Abroad Trips

Journal of Business Economics and Management. 2024. Vol. 25. No. 1. P. 104–128.
Solyomfi A. H., Skačkauskienė I., Igor Borisov, Vinogradov S.

This study identified and examined significant factors affecting the risk tolerance of Hungarian travellers towards abroad trips. Data for the empirical research was derived from an online survey. The final questionnaire included 45 statements describing aspects of travel safety and travel preferences. There was a total of 496 responses, which enabled them to be analyzed. A structural equation modeling (SEM) approach was used to develop the model of risk tolerance assessment. As a result of the study, physical security, price sensitivity, mood-driven factor, and destination's image in the media may be considered as substitutes for determining travellers' risk tolerance. This research contributes to the body of knowledge by supporting the proposition that inner personality traits and image factors may influence tourism preferences, suggesting future studies should use a multivariate approach to explain and predict tourism choices. Tourism managers and private companies operating in this sector can find the results of this study very helpful in planning and promoting tourism to risky destinations.

Research target: Economics and Management Other social sciences
Language: English
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Keywords: perceived risktravellers’ risk tolerance risky destinationsmood-driven
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