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July 24, 2026
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‘Science Is Universal-It Knows No Borders
Fuad Aleskerov, Tenured Professor and Director of the International Centre of Decision Choice and Analysis at HSE University, together with his colleagues, has developed methods of network analysis in bibliometrics that have made it possible to identify patterns in the appearance and citation of publications in academic journals, as well as their influence on each other. When one or a number of studies are frequently cited by a wide range of journals, this is an indicator that the research is of high quality. By contrast, extensive cross-citation within a limited group of journals increases the likelihood of identifying a network of predatory publications.

 

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People and Machines or People Against Machines? How Readiness to Artificial Intelligence is Changing Higher Education: A Bibliometric Analysis

P. 759–766.
Tunkevichus O., Bagrationi K.

AI is rapidly shaping modern higher education in the last 3 years. This paper aims to identify key areas and prospects of readiness to use AI in higher education. The following research question was formulated: What new research directions are observed in the existing discourse on readiness for the spread of artificial intelligence technologies in higher education? To answer the research question, a bibliometric analysis of the joint citations of 2,138 publications indexed in the SCOPUS database for the period 2016-2025 was carried out. Further, within the framework of the study, qualitative coding of annotations was carried out to narrow the sample to 568 of the most significant publications. The analysis identifies five main research areas: (1) the development of strategic readiness for AI within universities, (2) the formation of organizational readiness for AI adoption, (3) the development of consumer readiness among faculty and students, (4) the establishment of psychological parameters of readiness, and (5) the creation of decision-making mechanisms for AI implementation. The integrated implementation of AI in higher education requires information, the development of digital literacy and legal regulation, as well as consideration of social impacts and motivating factors (including hedonism and anthropomorphism) to increase trust in technology. The findings reveal the multi-level nature of AI readiness in higher education, encompassing strategic, organizational, and individual-psychological dimensions. Successful integration of AI depends not only on technological infrastructure and regulatory frameworks but also on users' trust in algorithms, their emotional comfort, and a sense of personal competence. Effective integration is possible only with the coordinated participation of all stakeholders, focused on creating an ethically responsible and technologically advanced educational environment. The presented bibliometric analysis systematizes existing research and highlights new directions in the field of AI application in education. This bibliometric review contributes to an in-depth understanding of the role of artificial intelligence as an important factor in the transformation of higher education and forms the basis for further research directions and practical solutions.

Language: English
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Keywords: strategic thinkingeducational technologiesbibliometric analysis higher education artificial intelligence

In book

The Proceedings of the 20th European Conference on Innovation and Entrepreneurship
Vol. 20. Issue 1. , Academic Conferences International Limited, 2025.
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