Institutional AI policies in Ukrainian higher education: A thematic analysis and assessment using the taxonomy of institutional AI policy maturity

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Type of the article: Research Article

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.

Acknowledgment
We thank the Armed Forces of Ukraine for providing security for this work, which was made possible only thanks to the resilience and bravery of the Ukrainian Army.

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  • JEL Classification (Paper profile tab)
    I23, O33, D83, L38, M14, O38
  • References
    45
  • Tables
    4
  • Figures
    2
    • Figure 1. Heatmap of AI-PMT dimensions across institutions
    • Figure 2. Distribution of cumulative AI policy maturity scores by year of adoption (2023–2025)
    • Table 1. Taxonomy of institutional AI policy maturity (AI-PMT)
    • Table 2. Descriptive statistics of cumulative AI policy maturity scores by year of adoption
    • Table 3. Clusters of institutional AI policies by maturity level (AI-PMT)
    • Table 4. Thematic patterns in institutional AI policies
    • Conceptualization
      Yana Suchikova, Serhii Omelchuk
    • Data curation
      Yana Suchikova, Serhii Omelchuk
    • Formal Analysis
      Yana Suchikova, Serhii Omelchuk
    • Investigation
      Yana Suchikova, Serhii Omelchuk
    • Methodology
      Yana Suchikova, Serhii Omelchuk
    • Project administration
      Yana Suchikova, Serhii Omelchuk
    • Resources
      Yana Suchikova, Serhii Omelchuk
    • Software
      Yana Suchikova
    • Supervision
      Yana Suchikova, Serhii Omelchuk
    • Validation
      Yana Suchikova, Serhii Omelchuk
    • Visualization
      Yana Suchikova
    • Writing – original draft
      Yana Suchikova, Serhii Omelchuk
    • Writing – review & editing
      Yana Suchikova, Serhii Omelchuk
    • Funding acquisition
      Yana Suchikova