INSTITUTIONAL AI POLICIES IN UKRAINIAN HIGHER EDUCATION: A THEMATIC ANALYSIS AND ASSESSMENT USING THE TAXONOMY OF INSTITUTIONAL AI POLICY MATURITY

Abstract

The study aims to analyze institutional policies governing the use of generative artificial intelligence (GenAI) in Ukrainian universities and assess their regulatory maturity. Drawing on the authors’ Taxonomy of Institutional AI Policy Maturity (AI-PMT), which comprises twelve analytical dimensions, the study examines a sample of 23 publicly available institutional policy documents adopted between 2023 and 2025. The analysis combines qualitative and quantitative approaches. A directed content analysis was used to assign ordinal scores (0-2) across twelve dimensions, enabling the construction of a cumulative maturity index (0-24) for each institution. The results reveal an uneven distribution of regulatory development, with more elaborated provisions related to teaching and learning, and comparatively less developed components addressing research practices, data governance, and infrastructural support. To synthesize these patterns, an analytical typology of institutions was developed based on cumulative maturity scores, identifying three broad groups that differ in the degree of regulatory completeness and procedural specification. In parallel, thematic analysis of policy content identified recurring patterns, including the normalization of AI use in education, the emphasis on transparency and disclosure, the prevalence of precautionary approaches to data and confidentiality, and several contested provisions. Comparison with international policy frameworks suggests that Ukrainian universities broadly align with global normative trends in principles, but exhibit limited operationalization of governance mechanisms and research-related provisions. The findings highlight structural imbalances in institutional AI governance and underscore the need to further develop research-oriented regulation, institutional support mechanisms, and coordinated policy approaches.