Institute of Education

Publications

• Book

  Structural and Technological Transformation of Education in the Post-Pandemic Period

  Springer, 2025.

• Article

  Korchak A., Patarakin Y., Costley J.

  Exploring the use of generative artificial intelligence by university students: a systematic literature review

  Problem statement. Artificial intelligence (AI) has become a transformative force across various sectors, including education. The release of ChatGPT marked a pivotal shift in the educational landscape, accompanied by rapid proliferation of other generative AI (Gen-AI). Gen-AI tools have quickly become one of the most prevalent forms of AI in higher education. This research focus highlights a need for a comprehensive examination of Gen-AI’s use. Addressing this gap is essential to developing a holistic understanding of Gen-AI’s role in higher education, particularly from the student perspective. Given the rapid evolution of Gen-AI technology along with its rapidly growing and often uncontrolled adoption among students, a systematic literature review is necessary to synthesise current knowledge. Methodology. This study conducted a tertiary review utilising a systematic approach outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, focusing on three key steps: search strategy and study selection, data analysis, and synthesis of findings. Data for this study was sourced from two databases: Google Scholar and Lens. These databases were chosen for their extensive coverage and accessibility, ensuring a comprehensive collection of relevant literature on AI use in higher education. The data was approached qualitatively: apriori and aposteriori codes were applied to the papers retrieved from Google Scholar. For a deeper analysis of the selected papers, we conducted a thematic analysis to identify recurring themes and patterns. Results. From the initial screening of 620 papers, 42 were selected for the final sample based on the predefined inclusion and exclusion criteria. The main uses of Gen-AI as identified in the analysed papers are summarised in the table. Conclusion. The variance in how AI is used among students –depending on their competence levels – highlights an essential consideration for educators: AI can potentially widen the gap between more and less competent learners. This observation calls for a pedagogical balance where AI supports learning without diminishing the educational rigour necessary for critical thinking and problem-solving skills.

  RUDN Journal of Informatization in Education. 2025. Vol. 22. No. 1. P. 37-57.

• Book chapter

  Uvarov A., Dvoretskaya I.

  Innovative ICT-Supported Teaching and the School’s Digital Renewal Stages

  Evolving digital technologies are infiltrating schools nowadays. This process is taking place by stages that are viewed as the schools’ digital renewal process (SDRP), the complex and multidimensional one. It includes changes in the educational environment (physical and virtual), the educational process, and the way how school operates. The use of innovative ICT-supported teaching (IITST) can be considered as one of the key indicators of a school’ digital renewal process for measurement of its progress. The aim of the study is to evaluate the status of innovative IITST within the framework of SDRP. During the monitoring of the digital transformation of Russian general education in 2021 data on IITST ware collected. More than 500 schools from all regions of Russia participated in the study. The data collected show that schools in different regions of the country are unevenly progressing along the path of digital renewal. The report presents results obtained during the monitoring and discusses the reasons for the uneven movement of schools along the path of digital renewal in Russia.

  In bk.: Structural and Technological Transformation of Education in the Post-Pandemic Period. Springer, 2025. Ch. 1. P. 3-9.

• Working paper

  Antipkina I., Ivanov A., Guzhelya D.

  Explicit continuum scale format reduces the ceiling effect in self-report questionnaires comparing to Likert response format

  This study presents a methodology for developing a new questionnaire format called explicit continuum scenario scales, in the example of a client focus questionnaire. Elements of the Rasch Guttman scenario scale methodology were used in its development. In three consequent studies, different aspects of the scale functioning were investigated. In Study 1, on the sample of 100 respondents, it was shown that the explicit continuum scale produces reliable results and helps avoid the ceiling effect shown in the Likert response format version of the client focus questionnaire. In Study 2, the scale was administered in a competition environment, in a sample of 735 people. Despite the positive shift of scores, the instrument shows excellent psychometric characteristics and still resists the ceiling effect. In Study 3, new items were included, and the scale was presented in an interactive format. In the sample of 65,000 university students, it demonstrated the robustness of its psychometrics characteristics including dimensionality. The results of the three studies show that the explicit continuum format has the advantage of the stable dimensionality similar to the expanded format and is promising for measurement in social sciences.

  Basic research program. WP BRP. National Research University Higher School of Economics, 2024