Abstract. Digital transformation is influencing linguistic communication within the aviation industry; however, linguistic data remains limited. This study examines these changes based on 120 publicly available trilingual texts related to the aviation sector, comparing two time periods: 2018–2019 and 2024–2025. Preprocessing revealed cross-linguistic similarities. Text processing was carried out using a rule-based, Unicode-aware pipeline. Lexical richness was assessed using the MATTR-50 metric, while sentence length and digital terminology were analyzed via five conceptual categories and document-level variance analysis. Independent sample assessment involved Mann-Whitney U-tests with the Holm correction applied across the entire text. Variance analysis of terminology was conducted using Fisher’s tests. The results indicate that sentence length decreased in both Russian and English—though the changes remained statistically significant after the Holm correction—whereas no changes were observed in Kazakh. While digital terminology was present in the earlier period, modern terms place greater emphasis on digital concepts such as AI, modeling, and learning.

Keywords: digital transformation, aviation discourse, multilingual corpus, digital aviation terminology, professional standardization.