Larger Groups of Students Use AI More Effectively in Learning
Researchers at the Institute of Education and the Faculty of Economic Sciences at HSE University have studied what factors determine the success of student group projects when they are completed with the help of artificial intelligence (AI). Their findings suggest that, in addition to the knowledge level of the team members, the size of the group also plays a significant role—the larger it is, the more efficient the process becomes. The study was published in Innovations in Education and Teaching International.
Group projects are an essential and common part of higher education, but there is still uncertainty about what makes teamwork effective.
The situation has become more interesting since the emergence of AI, which students have started to actively use in their studies. Experts from HSE University, including Galina Shulgina, Aleksandra Getman, Ilya Gulenkov, and Jamie Costley, explored how the characteristics of groups—the size and level of participants’ knowledge—affect the outcomes of work when AI is involved.
The study included 196 second-year undergraduate students, 55% of whom were male and 45% female. They had to solve problems as part of a team in a 16-week macroeconomics course. The students were divided into groups of five to eight people with varying levels of knowledge and experience. At first, the students worked independently on tasks. Then, they attended four seminars where they used ChatGPT 3.5 as a group tool. The goal was not simply to receive an answer from the AI, but to critically analyse it, apply economic models from the course, and present a comprehensive solution.
Researchers evaluated the quality of solutions based on the accuracy and detail of students' responses. Teams that not only used AI correctly but also revealed its limitations earned the highest scores, demonstrating a deeper understanding of the material.
The scientists identified several patterns in the use of AI by groups. Firstly, the best results were achieved by teams with members of similar levels of expertise. However, teams with a wider range of knowledge often performed less effectively. This is despite the fact that, in pedagogy, it is often believed that diversity of knowledge can help rather than hinder a team's performance.
Galina Shulgina
‘We were surprised to discover that the wider the range of student grades, the lower the quality of the final decision. This may be because the more prepared students spent time discussing and reaching an agreement on a solution, rather than focusing on the task itself, while less prepared students were unable to fully utilise the AI capabilities available to them. More skilled students are better at interacting with AI, as they can formulate more complex queries, critically evaluate the responses, and use this information to reason through problems,’ explains Galina Shulgina, junior researcher at the International Laboratory of Research and Design in eLearning at HSE University.
Secondly, the data showed a clear positive correlation between a larger team size and better performance when working with AI. Larger teams, with seven to eight members, performed better on average compared to teams with five to six members. Each additional member contributed to the final score, contrary to the common belief in pedagogy that smaller teams are more effective. Scientists argue that larger teams have more intellectual resources and a variety of perspectives, which help them interact more productively with neural networks.
Aleksandra Getman
‘However, this does not mean that efficiency gains will continue infinitely. After a certain point, negative effects may start to appear, such as difficulty in coordination and increased time to coordinate and maintain shared understanding of the task,’ explains Aleksandra Getman, junior researcher at the International Laboratory of Research and Design in eLearning at HSE University.
Despite the need for further research, the authors believe that in order to optimise the use of AI in education, students with similar educational levels should be grouped together in large classes. The researchers suggest that AI could be applied to the study of any subject.
Ilya Gulenkov
‘There is a potential for incorporating AI into group work in any course, regardless of the field of study or level of training. The key task of the teacher in organising such work is to set students’ expectations in advance about how and why AI can be used in their coursework. If students see examples of successful application of AI, then it can become an additional team member in any subject. We observe how students are using more advanced versions of the models (ChatGPT 5, ChatGPT 5 Thinking, etc), and we see great potential for student–AI collaboration. This applies not only to simple, standardised tasks, but also to complex ones that require in-depth understanding, working with multiple sources, and advanced reasoning. The role of students' own expertise in interacting with these models is becoming increasingly important. All models now provide plausible answers, but it is essential to critically evaluate their content,’ says Ilya Gulenkov, lecturer at HSE University’s Faculty of Economic Sciences.
See also:
Neurolinguists Assist in Awake Surgery on 11-Year-Old Patient with Epilepsy
Researchers at the HSE Centre for Language and Brain took part in a rare awake neurosurgical procedure performed on an 11-year-old patient with drug-resistant epilepsy. Working alongside surgeons at the Voyno-Yasenetsky Centre of Specialised Medical Care for Children in Solntsevo, they monitored the resection of a portion of the left temporal lobe, where the epileptic focus had been identified.
Scientists Explain How Emotions Shape Attitudes Toward Digital Governance
Today, interactions between citizens and government increasingly take place through digital governance platforms, including digital public services, AI-powered systems, and algorithmic decision-making tools. Until now, however, these technologies have largely been viewed as technical instruments, with their effectiveness assessed primarily in terms of efficiency and user-friendliness. The authors of a new study propose a broader perspective, arguing that digital governance should also be understood as an emotional experience that directly shapes citizens' trust in public institutions.
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.
Pocket Money, Personal Interest, and Family Practices: What Shapes Students’ Economic Literacy?
University students' economic literacy depends not only on their field of study but also on their interest in economics, the learning environment, and family financial practices. For example, students who received pocket money irregularly tend to perform better on economic literacy tests than their peers who received financial support on a regular basis. These findings come from a study conducted by HSE University involving more than 1,100 students from five Russian universities. The findings have been published in Cakrawala Pendidikan.
Creative Work as a Remedy for Burnout
The creative, supportive atmosphere and innovative methods at the Centre for Sociocultural Research make it appealing to early-career scholars. Over years of working at HSE University, they grow into researchers and lecturers recognised both in Russia and abroad. Chief Research Fellow Zarina Lepshokova and Leading Research Fellow Ekaterina Bushina spoke about their journey at the centre and at HSE, their research, and the role of mentors in their academic success.
HSE Study Reveals Imbalance in the Generative AI Market
Researchers at HSE University analysed how effectively the global generative artificial intelligence market converts investment into real revenue, concluding that AI is currently developing faster than it is paying off. The results have been published in the journal Foresight and STI Governance.
‘Entering Robotics Now Means Growing with the Area’
Unmanned vehicles, courier robots, and smart speakers are rapidly becoming a part of our lives. In 2026, the HSE Faculty of Computer Science opens its new Bachelor’s Programme ‘Design of Intelligent Robotic Systems’ (DIRS). It will train specialists at the intersection of IT, artificial intelligence, and robotics. Academic Supervisor of DIRS Vadim Morgachev explains how studies are organised and why graduates of the programme ‘will definitely be accepted into the future.’
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.