Institute of Education

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

  Ivanov I.

  Institutional transformation of the children’s aftersсhool sector in post-Soviet countries

  This study examines the institutional transformation of extracurricular education systems across the 15 former Soviet republics using Qualitative Comparative Analysis (QCA).​ Drawing on national statistics, policy documents, and expert interviews, the research identifies three distinct transformation scenarios: reproduction, modernisation, and degeneration. The analysis reveals varying degrees of change, from low-level transformation in countries maintaining significant state involvement to high-level transformation where the sector has undergone substantial restructuring. The study employs a composite index to quantify institutional change and explores key parameters such as coverage, accessibility, and legislative frameworks. While highlighting the persistence of Soviet legacy in some nations, the research also demonstrates the dynamic nature of these transformations, with some countries transitioning between scenarios. This comparative approach provides valuable insights into the evolving landscape of extracurricular education in post-Soviet space.

  Compare: A Journal of Comparative and International Education. 2025. P. 1-18.

• Book chapter

  Panova A., Slepyh V.

  Science or industry: Improving the quality of the Russian higher education system

  The shape of the modern Russian science and higher education system is largely determined by the peculiarities of its structure during the Soviet period: its division into research and higher education sectors. However, throughout the 21st century, the higher education system has undergone significant reforms. An important focus of these reforms has been the development of the research mission of higher education institutions (HEIs). At the same time, the government launched reforms that focused on the development of regional markets, where universities were an important driver of this development, and on the interaction of universities with local employers. There have also been reforms aimed at changing the system to determine the number of state-funded places and places for targeted enrolment and how they are distributed between universities. All these reforms have changed the functioning of universities. In this study, we describe the current landscape of the Russian higher education system, and how different types of universities are involved in research, attracting more talented students and engaging with other sectors of the economy.

  In bk.: Vocation, Technology & Education. Vol. 1. Iss. 4. Shenzhen Polytechnic University, 2024.

• Working paper

  Kardanova E., Ivanova A., Tarasova K. et al.

  A Novel Psychometrics-Based Approach to Developing Professional Competency Benchmark for Large Language Models

  The era of large language models (LLM) raises questions not only about how to train models, but also about how to evaluate them. Despite numerous existing benchmarks, insufficient attention is often given to creating assessments that test LLMs in a valid and reliable manner. To address this challenge, we accommodate the Evidence-centered design (ECD) methodology and propose a comprehensive approach to benchmark development based on rigorous psychometric principles. In this paper, we have made the first attempt to illustrate this approach by creating a new benchmark in the field of pedagogy and education, highlighting the limitations of existing benchmark development approach and taking into account the development of LLMs. We conclude that a new approach to benchmarking is required to match the growing complexity of AI applications in the educational context. We construct a novel benchmark guided by the Bloom's taxonomy and rigorously designed by a consortium of education experts trained in test development. Thus the current benchmark provides an academically robust and practical assessment tool tailored for LLMs, rather than human participants. Tested empirically on the GPT model in the Russian language, it evaluates model performance across varied task complexities, revealing critical gaps in current LLM capabilities. Our results indicate that while generative AI tools hold significant promise for education - potentially supporting tasks such as personalized tutoring, real-time feedback, and multilingual learning - their reliability as autonomous teachers' assistants right now remain rather limited, particularly in tasks requiring deeper cognitive engagement.

  Computation and Language (cs.CL); Artificial Intelligence (cs.AI). cs.CL. arXiv, 2024