Aleshkovski I.A, Krukhmaleva O.V., Narbut N.P., Savina N.E. Russian students on the potential and limitations of artificial intelligence in education. RUDN Journal of Sociology. 2024. Vol. 24. No. 2. Pp. 335–353.

Текст статьи на сайте журнала URL: https://journals.rudn.ru/sociology/article/view/39929/23650 (дата обращения 16.09.2024)

Abstract

The rapid entry of artificial intelligence into all spheres of society's life activity requires the fixation of ongoing changes and systematic sociological study. Education and science in this process are key resources that, on the one hand, develop artificial intelligence (AI) technologies, improve them, and, on the other hand, fully experience the pressure of contradictions caused by new technologies. In this regard, it is important for higher education and society as a whole to understand how students react to new opportunities and technologies, how involved they are in the introduction of AI into their educational activities and how they evaluate their experience of using new technologies. This paper examines the assessments of Russian university students' their personal experience of using generative AI models (neural networks) in educational activities, highlights the most popular AI functions, and evaluates satisfaction from this interaction. The analysis is based on the data from a survey of Russian university students conducted in 2023-2024 (N=52919). The study revealed that, despite the massive fascination with digital technologies and the use of neural networks, students of Russian universities have a very ambiguous assessment of their use in the course of their studies. By the senior years, a critical perception of the opportunities provided by AI is growing, and a more balanced attitude towards their capabilities is being formed. The data of the research allow us to conclude that the use of generative AI models in the educational process entails the adoption of a set of necessary decisions, both on the direct regulation of application and on ethical issues, revision of the forms of independent work of students’ independent work, including final certifications and test tasks, and also dictates the need to search for constructive approaches to using the capabilities of AI to improve the quality of education and enhancement of the work of higher education as a whole. Moreover, AI assigns higher education a task of forming and developing in students competencies aimed at critically assessing the results of interaction between a human being and a neural network, understanding the limitations and capabilities of the generated information, and acceptable formats for its use in scientific and educational work.