In a globalized, knowledge-driven economy, the quality of higher education is a pivotal contributor to socio-economic advancement, yet its assessment remains complex due to its inherent subjectivity and multifaceted nature. This study presents an innovative methodological approach for evaluating the quality of higher education within the knowledge economy framework, utilizing the context-input-process-output (CIPO) model, exploratory factor analysis, and stochastic frontier analysis. The input indicators include financial resources (government spending per student, direct public funding for a student, share of capital/current expenditures, compensation to the teaching/nonteaching staff), human resources (student–teacher ratio, share of enrollment in higher education, number of teachers), and expected duration of higher education. The output indicators include the general level of graduation from first-degree programs and level of education, at least completed short-cycle higher education. Indicators of economic (GDP per capita) and social (employment rate and Gini index) development of the country were chosen as context parameters. Conducting a comparative analysis across 36 European countries from 2001 to 2017 available data, the authors identified integrated factors for input and output parameters, as well as context parameters characterizing the quality of higher education. Then we categorize national higher education systems into five distinct quality levels: very low, low, satisfactory, high, and very high. This classification enables us to dissect and understand the challenges faced by countries at the lower end of the quality spectrum and propose strategic solutions informed by the best practices of the leading nations. Our findings offer critical insights into optimizing higher education quality to enhance competitive advantages for educational institutions, improve employment prospects and living standards for students, secure a more qualified workforce for employers, and spur economic growth and productivity at the national level. This comprehensive assessment underscores the role of quality education as a cornerstone of the knowledge economy, driving innovation, economic development, and societal progress.

The purpose of this work is to identify the functional links between key indicators of scientific activity and socio-economic development and to check whether the quality of scientific activity and the dynamics of innovative development are the key determinants of socio-economic progress. Following the chosen methodology, the paper forms an array of input data that characterizes the level of scientific and innovative activity, economic and social development. The principal component method is used to identify the most relevant indicators from each group and to introduce three latent variables that denote each group separately. A system of simultaneous structural equations is obtained as a result of establishing functional relationships between manifest and latent variables and building a structural model. In addition, the paper determines two clusters of the studied countries to confirm the obtained results through structural modelling. The study is conducted for 35 European countries based on 33 indicators, which characterize the quality of scientific activity, economic and social development during 2014-2020. The obtained system of structural equations confirms the hypothesis regarding the importance of scientific activity quality in terms of ensuring the socio-economic development of the country.

The rapid diffusion of generative artificial intelligence (GenAI) is reshaping higher education by challenging traditional roles of teaching, trust, and academic integrity. This study aims to explore how university staff cognitively and ethically evaluate GenAI by analysing perceptions of its pedagogical replacement potential, practical feasibility, academic integrity risks, and perceived reliability across national contexts. The analysis is based on an anonymous cross-sectional survey of 637 respondents conducted between May and September 2025, using descriptive statistics, correlations, regression models, and exploratory factor analysis. The findings show that perceived replacement potential is low (M = 2.47), with over 51% of respondents rejecting the idea of AI replacing teachers. Academic integrity concerns are the strongest dimension (M = 3.51), while trust in AI accuracy remains low (M = 1.99), indicating widespread scepticism. Perceived cost and complexity do not significantly influence beliefs about replacement (R² = 0.007; p = 0.117), suggesting a weak relationship between feasibility and perceived impact. Finally, moderate positive correlation (ρ = 0.34) and low reliability (α = 0.50; α = 0.45) provide evidence that perceptions of GenAI are fragmented and multidimensional rather than internally consistent.