‘Many Want to Create AI-Based Products and Become More Competitive’
In 2024, the online Russian-taught master’s programme ‘Artificial Intelligence,’ offered by the HSE Faculty of Computer Science, saw a record number of first-year students—over 300. What accounts for such a high interest in AI, how the curriculum is structured, and what new skills will graduates acquire? Elena Kantonistova, the programme’s academic director, shares more.
— Why are there so many people who are eager to study artificial intelligence these days?
— The programme has indeed attracted many students: in September 2024, 304 first-year students, 150 second-year students, and about 20 more students through a partnership with T-Bank’s Central University began their studies.
Artificial intelligence is very popular nowadays due to the emergence of services like Midjourney, Kandinsky, ChatGPT, Gigachat, Whisper, and other widely known AI solutions. Many people want to learn how to create their own products based on AI, making them more competitive and valuable in the current IT labour market. Others are interested in conducting AI research— also a main focus of our programme. We study AI (particularly machine learning and deep learning) at a deep level: we do not just familiarise students with the concepts but also explore the details of algorithms and architectures. Our students receive extensive practical experience, giving them a competitive edge upon graduation.
— How do you manage the increasing number of students?
— The number of students in the programme has nearly doubled this year. In the 2023/24 academic year, about 260 students enrolled in September, while this year, the number exceeds 450.
With the increase in students, communication challenges have arisen, but these are local issues. Overall, we expected a large intake and prepared all the necessary resources and platforms in advance. Furthermore, processes scale more easily due to the online format, so the quality of communication has not diminished. In fact, I think it is improved, as we have accumulated and taken into account feedback from students in previous groups. Each year, our interaction with students becomes more thoughtful and comfortable for the learners.
— How do you assess the interaction between students and instructors with such a large intake?
— It is definitely more challenging for instructors to interact with so many students. Now, in chats, instead of 30–50 or even 100 students, there are over 300, and many ask questions and want to get quick answers. In such situations, a team of assistants and curators is essential—they help the instructor and answer some of the questions. Each of our courses has lots of assistants (up to 10 for each key discipline), and for flagship courses, there is an assistant lecturer who oversees the organisational process within the discipline and ensures communication runs smoothly.
— How does the programme work, given that students have varying levels of preparedness?
— In the previous intake, we already tested a format that divided students into groups based on their knowledge level. This year, we have fully implemented this approach for all mandatory courses—students are placed into groups according to difficulty. Before the courses begin, we offer an entry test, and based on the results, we recommend students join a suitable group. The division is very flexible and tailored to the subject within each course. For example, a student may choose a basic group for mathematics but attend an advanced group for machine learning.
— What new opportunities or courses have been added to the programme in 2024?
— This year, we added a new specialisation for the second year of study, focusing on working with large language models (LLM) in production.
These are six advanced courses designed to train ML developers with highly sought-after industry skills. This year, there was an additional selection process for these courses among students in the programme
However, the students have managed well. For future intakes, we may drop the selection process and give access to all our students.
— How do you plan to develop the programme in the future, considering the current results?
— The programme now offers a wide range of courses in data analysis (machine and deep learning) and machine learning development. After completing the core courses (2–3 courses per semester), students can create a flexible, individualised learning path by selecting the courses that interest them the most from the curriculum. The programme’s curriculum has more or less stabilised. I don’t anticipate making any drastic changes in the coming years, but rather we will focus on improving the quality of the educational process. It is currently good, in my opinion, but there is always room for improvement.
— What are your plans for further development and improvement of the programme in the next year?
— Our main goal is not to disrupt what is already working well! But seriously, we are constantly working on improving course organisation and making communication with students and the academic office more efficient than ever. This will continue to be our focus.
‘Artificial Intelligence’ is a practical-oriented master’s programme that prepares specialists in AI, delivered 100% online. Students learn the full circle of implementing AI in products: collecting and processing data, building deep learning models, and deploying algorithms in industrial environments. Additionally, students become familiar with popular architectures for processing texts, images, and videos.
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