At RUDN University, an intellectual assistant will help professors at classes
“The intelligent teaching assistant analyses recorded lecture materials thoroughly. Based on the analysed data, it can create an outline of the lecture and key words of the class, assess the emotional state of students, analyse the dynamics of the lecture and identify the most difficult topics for the audience,” Anton Poddubsky, Dean of the Faculty of Artificial Intelligence at RUDN University
The algorithm of the programme operation is described below:
The first stage involves using a neural network model to process the audio recording: dividing the sound into time segments (silence, teacher's speech, students' speech). This makes it possible to construct a chronology of the lesson and analyse the dynamics of the pronounced syllables per unit of time.
The second stage involves a deep textual model to create an outline, test questions and a knowledge tree structure.
Additionally, a linguistic analysis of the text is performed to identify the frequency of keywords and stop words.
The analysis of students' emotional state is performed as an addition to the first stage of the algorithm. After identifying the time intervals in which students speak, classification models are applied to this data to determine the emotional colouring of the statements.
“The programme has already shown high efficiency. The text model successfully creates lecture notes for humanities and exact sciences, highlights subtopics of classes and main theses. The processing time for an hour and a half lecture is only 3 minutes, which is significantly higher than existing analogues. Also, during the testing of the assistant, we found that during distance classes the share of silence time is significantly higher than in face-to-face classes. This indicates a decrease in the effectiveness of the distance learning format,” Anton Poddubsky, Dean of the Faculty of Artificial Intelligence at RUDN University.
In the forthcoming future, the programme plans to add a function to generate a 5-10 minute podcast in which two people discuss lecture material.
“Students are also using AI in their studies. Is it possible that one day two AIs will communicate with each other? Generative models have been developing rapidly in recent years. Almost every week we hear about the launch of new tools integrated into the educational process. Artificial intelligence technologies are tools that humans have created to help themselves. The danger arises when AI replaces the thinking process rather than helping to develop it. The use of AI does not cancel the fact that the student must have some knowledge, because the verification of the results of generative model activity is left to a human being. In addition, today all generative models have ‘illusions’ in the AI world, this is called the generative model hallucination problem. Without the intervention of human intelligence, we will not be able to evaluate and differentiate the results obtained by artificial intelligence,” Rezaian Naeem, head of the Artificial Intelligence Laboratory, Faculty of Artificial Intelligence at RUDN University.
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Researchers from the Faculty of Artificial Intelligence at RUDN University conducted a large-scale study that revealed systemic errors in large language models (LLMs) when diagnosing depression based on text. This work, carried out in collaboration with colleagues from AIRI, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow Institute of Physics and Technology, and MBZUAI, not only identifies the problem but also lays the foundation for the creation of more reliable and secure tools for detecting depression and anxiety.
Alexandra Sentyabreva, a junior researcher at the Laboratory of Cell Technologies and Tissue Engineering at RUDN Research Institute of Molecular and Cellular Medicine at the Russian University of People's Friendship, won the competition for young scientists at the All-Russian Scientific Conference “Topical Issues of Morphogenesis in Norm and Pathology.” She was awarded the Academician A.P. Avtsyn Prize.
Imagine a world where everyone has enough food, clean water, access to education, and decent work. A world where nature is protected and the future of our planet is cared for. These are the Sustainable Development Goals—to achieve a sustainable future for all! To this end, in 2015, the United Nations (UN) defined 17 Sustainable Development Goals (SDGs). The SDGs are a global plan that helps countries and people work together towards a better future. All 193 UN member states have joined the plan.
Researchers from the Faculty of Artificial Intelligence at RUDN University conducted a large-scale study that revealed systemic errors in large language models (LLMs) when diagnosing depression based on text. This work, carried out in collaboration with colleagues from AIRI, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow Institute of Physics and Technology, and MBZUAI, not only identifies the problem but also lays the foundation for the creation of more reliable and secure tools for detecting depression and anxiety.
Alexandra Sentyabreva, a junior researcher at the Laboratory of Cell Technologies and Tissue Engineering at RUDN Research Institute of Molecular and Cellular Medicine at the Russian University of People's Friendship, won the competition for young scientists at the All-Russian Scientific Conference “Topical Issues of Morphogenesis in Norm and Pathology.” She was awarded the Academician A.P. Avtsyn Prize.