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  • Использование генеративных моделей в высшем гуманитарном образовании: опыт Института медиа НИУ ВШЭ
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News
August 12, 2026
‘I Would Like My Research to Help Make the World a Calmer and Better Place
Whatever task Saraa Ali, Junior Research Fellow at the Laboratory of Methods for Big Data Analysis (LAMBDA) of the AI and Digital Science Institute (HSE Faculty of Computer Science), is working on, she thinks about how it can benefit people. She told the Young Scientists of HSE University project about her large family, diagnosing three-phase motors, and her dream of building a children’s home in her native country.
August 11, 2026
‘The Peak of Stupidity and ‘The Valley of Despair: HSE Economists Propose an Explanation for the Dunning–Kruger Effect
The Dunning–Kruger effect, which describes a sharp surge in self-confidence among beginners followed by an equally rapid decline as they gain experience, can be explained by the nature of the learning process and the acquisition of new knowledge. This conclusion was reached by Andrey Vorchik of the HSE Faculty of Economic Sciences together with independent researcher Murat Mamyshev. They developed a mathematical model of learning and demonstrated how subjective confidence is formed and changes as knowledge accumulates, as well as how teachers can reduce the ‘valley of despair’ experienced by learners.
July 24, 2026
‘I Like Self-Fulfilling Prophecies
Andrey Vorchik studies happiness, delivers popular science lectures, and believes that science should address social issues as well. In an interview for the Young Scientists of HSE University project, he spoke about how emotions influence decision-making, the Bermuda Triangle formed by the bathroom, refrigerator, and bed, and the ideal formula for education.

 

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Использование генеративных моделей в высшем гуманитарном образовании: опыт Института медиа НИУ ВШЭ

Коммуникации. Медиа. Дизайн. 2025. Т. 10. № 1. С. 5–37.
Sharikov A., Dzhura A., Magera T., Onegov D., Khasavov A.

The article delves into the theoretical foundations and practical applications of generative models in higher humanitarian education in Russia. It explores some ethical concerns surrounding the use of generative artificial intelligence. The paper describes how HSE University has been setting up a regulatory framework for its use since 2023. This includes legalizing the use of generative models for educational purposes. One of the key findings of the article is a study conducted among graduate students at the Institute of Media, Faculty of Creative Industries, HSE University. The study aimed to investigate the students' experiences with neural networks. A survey of students from four master's degree programs (n=98) showed that most of them had worked with neural networks before their legalization at the university. It also revealed that students most often used neural networks not to generate texts but to solve problems related to systematizing and structuring textual data. Some students refused to use neural networks. Their position was expressed by the phrase "This devalues my own work, I prefer to do tasks at the university on my own." This attitude was also linked to a general distrust of neural networks. Based on the conducted research, methodological provisions were formulated expressing a rational attitude towards the use of neural networks by students, namely: the interpretation of the generative model as a tool for solving a certain class of problems; understanding the boundaries of the applicability of a neural network: understanding the limited resources when using it; knowledge of the general scheme (algorithm) of its application; the need to verify the result. The article concludes with a positive example of how a generative model was used in the final qualifying work of a student in the master's program "Media Management" by B.A. Podrozhantseva.

Research target: Media and Communications Education
Language: Russian
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DOI
Text on another site
Keywords: искусственный интеллектнейросетьadaptive neural networksгенеративная модельgenerative model
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