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.

‘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.

‘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.

‘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.