International Laboratory of Research and Design in elearning

Publications

• Book

  Terentev E., Froumin I.

  Open praxis "The Manifesto for Teaching and Learning in a Time of Generative AI: A Critical Collective Stance to Better Navigate the Future"

  This manifesto critically examines the unfolding integration of Generative AI (GenAI), chatbots, and algorithms into higher education, using a collective and thoughtful approach to navigate the future of teaching and learning. GenAI, while celebrated for its potential to personalize learning, enhance efficiency, and expand educational accessibility, is far from a neutral tool. Algorithms now shape human interaction, communication, and content creation, raising profound questions about human agency and biases and values embedded in their designs. As GenAI continues to evolve, we face critical challenges in maintaining human oversight, safeguarding equity, and facilitating meaningful, authentic learning experiences. This manifesto emphasizes that GenAI is not ideologically and culturally neutral. Instead, it reflects worldviews that can reinforce existing biases and marginalize diverse voices. Furthermore, as the use of GenAI reshapes education, it risks eroding essential human elements—creativity, critical thinking, and empathy—and could displace meaningful human interactions with algorithmic solutions. This manifesto calls for robust, evidence-based research and conscious decision-making to ensure that GenAI enhances, rather than diminishes, human agency and ethical responsibility in education.

  Vol. 16. Iss. 4. International Council for Open and Distance Education, 2024.

• 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

  Maloshonok N., Zhuchkova S., Bekova S. et al.

  The quality of admissions to Russian doctoral programmes during the COVID-19 pandemic

  Previous studies have shown that the COVID-19 pandemic had a negative impact on higher education systems and student learning globally. However, despite this, many countries experienced an increase in doctoral enrolment in 2020, which has raised concerns about the quality of admission and the motivation of doctoral candidates during the pandemic. This article aims to explore this context by delving into statistics about Russian doctoral programmes, which saw a decline in enrolment from 2010 to 2019. We use data from a web survey of 1,895 students enrolled in doctoral programmes at Russian universities in 2020. The results suggest that the increase in enrolment came in tandem with a decrease in the quality of doctoral admission. More students with non-academic motivation and a lack of academic skills and attainment were admitted. This situation is likely to prompt completion rates to lower over the course of the next three to five years.

  In bk.: Global Perspectives on Graduate and Doctoral Education: International Case Studies. L.: Routledge, 2025. P. 103-112.

• Working paper

  Larina G., Kuzmina Y., Georgijs K.

  The Precision of Symbolic Numerical Representation in Verbal Format Has an Indirect Effect on Math Performance in First Grade

  Numerical information can be represented in three formats: two symbolic (visual (digits) and verbal (number words)) and one nonsymbolic (analog) format. Studies have shown that the precision of symbolic numerical representation is associated with math performance. The precision of symbolic representation is mostly discussed as the precision of representation in a visual format, whereas the precision of representation in verbal format and its relation with math performance is less studied. The current study examines the precision of symbolic numerical representation in visual and verbal formats and the relationship between such precision and math performance when controlling for prior math performance, nonsymbolic numerical representation, phonological processing, reading skills and working memory. We used data from 367 Russian first graders (mean age, 7.6 years; 53% girls). To assess the precision of symbolic numerical representation, magnitude comparison tasks with digits and number words were used. It was found that the precision of symbolic representation in verbal format did not have a direct effect on math performance, but has an indirect effect via visual format of symbolic representation, even when controlling for prior math performance and other cognitive abilities.

  PSYCHOLOGY. WP BRP. Издательский дом НИУ ВШЭ, 2020. No. 120.