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October 1, 2026
HSE Researchers Show How Congenital Motor Disorders Affect Brain Development
Researchers from HSE University’s Institute for Cognitive Neuroscience have synthesised the findings of their previous studies on brain development in children with obstetric brachial plexus palsy and arthrogryposis. Their analysis shows that impaired motor function in early childhood not only limits children’s motor experience but also affects memory, categorical thinking, and information processing. The study has been published in Frontiers in Psychology.
October 1, 2026
Window into the Body: Scientists Develop Neural Network to Detect Risk of 15 Diseases from Retinal Images
Russian universities, with the participation of HSE University, Sber, and Z-union, have developed a neural network that can simultaneously assess the risk of 15 types of pathology from retinal photographs, including not only eye diseases but also cardiovascular conditions. The AI system can help clinicians detect potentially concerning changes at an early stage, identify signs reflecting the condition of retinal blood vessels, and determine whether a patient may need further examination. The paper has been published in Frontiers in Medicine.
September 30, 2026
'We Did Not Limit the Time for Questions'
The International Laboratory for Supercomputer Atomistic Modelling and Multi-Scale Analysis at HSE University held a major conference on molecular dynamics. Participants had the opportunity to attend all the presentations, while speakers were given as much time as they needed to answer questions. The HSE News Service interviewed Grigory Smirnov, Head of the Laboratory, and Genri Norman, Chief Research Fellow, about the conference preparations and the discussions it generated.

 

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Когда чаще не значит лучше: сценарии использования генеративного ИИ в высшем образовании

Высшее образование в России. 2026. Т. 35. № 6. С. 127–148.
Tarasova K., Talov D., Ivanova A.

The article examines the relationship between the use of generative artificial intelligence in learning and several AI-related characteristics of students at Russian universities. The study is based on the assumption that the frequency of AI use should not be treated as a direct indicator of AI literacy or competence in working with generative systems. Four characteristics are considered separately: functional reliance on AI in decision-making, anthropomorphization of AI, ethical sensitivity to its use, and prompting skill assessed through a performance-based task. The empirical analysis draws on data from 1,647 third- and fourth-year undergraduate students from 10 Russian universities, representing STEM, humanities, and social science fields. The results show that the overall frequency of generative AI use is positively associated with functional reliance on AI and anthropomorphization, but is not significantly related to ethical sensitivity or prompting skill after adjustment for multiple comparisons. The use of AI for preparing course papers and project work is associated with both functional reliance on AI and anthropomorphization. More instrumental scenarios, including programming and searching for learning materials, are associated with lower evels of anthropomorphization. Ethical sensitivity is more strongly related to field of study: students in the humanities and social sciences demonstrate higher levels of ethical concern than students in STEM fields. No robust predictors of prompting skill were identified in the final model. The findings suggest that the frequency of generative AI use cannot serve as a sufficient indicator of students’ AI literacy. The article argues for a more differentiated approach to teaching, assessment, and institutional regulation of AI use in higher education.

Research target: Education
Language: Russian
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Keywords: высшее образованиеhigher educationэтика ИИгенеративный ИИgenerative AIAI literacypromptingИИ-грамотностьпромптингантропоморфизация ИИ
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