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News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Исследование потенциала генеративных моделей для оценивания эссе и обеспечения обратной связи

Отечественная и зарубежная педагогика. 2024. Т. 1. № 5(101). С. 123–137.
Bogolepova S., Жаркова М. Г.

In the era of rapid development of generative language models these tools are increasingly being used by both students and instructors. This paper aims to investigate the potential of generative models interacting with users via chatbots ChatGPT и PerplexityAI for the evaluation of standardised essays in English and the provision of feedback on their quality. Accounting for the specific features of each chatbot and standardised assessment criteria, we developed prompts which were consequently fed to the chatbots together with 19 students’ essays. The chatbots both awarded overall grades and gave points and feedback on specific aspects. The chatbots’ grades were compared to the ones provided by the instructor, and to each other. Cronbach’s alpha was used to measure the consistency of grading, whereas Koen’s and Fleiss’s kappas helped to evaluate inter-rater agreement. Though the consistency of grading among the raters was shown to be from acceptable to excellent on different aspects, which indicates similar interpretations of assessment criteria by the instructor and the chatbots, inter-rater agreement was slight. Qualitative analysis revealed such features of feedback from chatbots as ignoring instructions in the prompt, finding non-existent errors, or awarding different grades in consecutive inquiries. We conclude that chatbots can be used for rough evaluation of standardised essays; however, their output cannot be considered reliable and needs expert editing. 

Research target: Education Philology and Linguistics
Language: Russian
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Keywords: обратная связьискусственный интеллектfeedbackэссеassessmentоцениваниеessaygenerative modelsгенеративные модели artificial intelligenceautomatic text evaluationавтоматическая проверка текста
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