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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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Conflicting impacts of shadow AI usage on knowledge leakage in metaverse-based business models: A Yin-Yang paradox framing

Technology in Society. 2025. Vol. 81. P. 1–9.
Chin T., Li Q., Mirone F., Papa A.

The accelerated evolution of artificial intelligence (AI) technology enables firms to create metaverse-based business models (MBMs) where the use of shadow AI is prevalent. However, employees' use of shadow AI not only provides benefits but also poses risks concerning organizational knowledge leakage (OKL). How to properly manage the usage of shadow AI has become a critically important yet under-researched issue for firms within MBMs. To fill this gap, this paper considers the paradoxical nature of shadow AI usage and thereby adopts a Yin-Yang dialectical frame to investigate the conflicting mechanisms between shadow AI usage and OKL and their impacts on the sustainability of MBMs. Using a questionnaire survey method, we gathered from enterprises within metaverse-related businesses in China and employed moderated hierarchical regression and bootstrap analysis to test the hypotheses. Results show that there existed an inverted U-shaped relationship between shadow AI usage and OKL and a negative link between shadow AI usage and the sustainability of MBMs. We also examined the mediating role of OKL and the moderating role of AI knowledge-oriented human resource management practices. Theoretically, this article conducts an unorthodox Yin-Yang paradox frame to address the dynamic tensions among shadow AI usage, OKL, and the sustainability of MBMs, enriching the theoretical underpinning of cross-disciplinary research at the intersection of technological change, knowledge management, and business sustainability. Practically, our findings allow practitioners and policymakers to gain a better understanding of the paradoxical interplay between employees' unethical behavior toward AI usage and its consequences. This indicates the unconventional yet crucial challenges facing firms within MBMs in the process of fulfilling the United Nation's Sustainable Development Goals.

Research target: Economics and Management
Language: English
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Keywords: paradox artificial intelligenceKnowledge leakageMetaverse-based business models (MBMs)Yin-Yang
Publication based on the results of:
National innovation system 2.0: involving key actors in transformation processes (2025)
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