The Impact of AI Tools on the Development of Foreign Language Skills and Digital Competence: A Longitudinal Study and Assessment Matrix
https://doi.org/10.31992/0869-3617-2026-35-3-90-113
Abstract
The article presents the results of a two‑year longitudinal study (2023–2025) conducted with a sample of 50 first- and second‑year students of Moscow State Institute of International Relations (17 German‑track students and 33 English‑track students). Drawing on a triangulation of methods (standardized CEFR‑based language proficiency tests, digital competence questionnaires, analysis of student portfolios, semi‑structured interviews with 10 teachers and 20 students, and reflective narratives), the authors examined the impact of the purposeful integration of artificial intelligence (AI) tools on the development of foreign language skills and digital competence. All participants had achieved at least a B2 level according to their Unified State Exam (≥75 points), which reflects the selective nature of the institution. The results show a significant improvement in all four language skills (speaking: Cohen’s d = 0.97, writing: d = 0.70, reading: d = 0.54, listening: d = 0.48, all p < 0.01). Qualitative analysis revealed a three‑stage evolution of students’ value‑laden attitudes towards AI: from techno‑scepticism through pragmatism to an integrative understanding. The authors developed a matrix of digital AI competence comprising four components (cognitive, operational, affective and axiological) and three developmental levels (novice, practitioner, expert). The study found that students with higher digital competence demonstrate significantly greater improvement in language skills (correlation with speaking r = 0.42, p = 0.002). Four patterns of AI use were identified and classified; 71% of students use AI responsibly. The authors argue that value‑laden attitudes towards AI are a critical precondition for the effective use of technology in the training of future international affairs professionals. In practical terms, the competence matrix can serve as a tool for assessment, course design and teacher training in the context of Education 4.0.
About the Authors
A. P. GulovRussian Federation
Artem P. Gulov – Dr.Sci. (Pedagogics), Associate Professor, English Language Department No. 6
Researcher ID: AEM-0663-2022;
76 Vernadsky ave., Moscow, 119454
N. O. Yudin
Russian Federation
Nikita O. Yudin – Cand.Sci. (Politics), Senior Lecturer, German Language Department, Head of Scientometric Analysis Sector
Researcher ID: AAF-3714-2019;
76 Vernadsky ave., Moscow, 119454
M. A. Chigasheva
Russian Federation
Marina A. Chigasheva – Cand.Sci. (Philology), Associate Professor, Head of Language Training
Department, Professor of German Language Department
Researcher ID: AAP-4748-2020;
76 Vernadsky ave., Moscow, 119454
O. V. Printsipalova
Russian Federation
Olga V. Printsipalova – Cand.Sci. (Philology), Associate Professor, Head of German Language Department
Researcher ID: AAX-2578-2021;
76 Vernadsky ave., Moscow, 119454
V. V. Selezneva
Russian Federation
Vera V. Selezneva – Cand.Sci. (Philology), Head of English Language Department No. 6
Researcher ID: PMQ-2460-2026;
76 Vernadsky ave., Moscow, 119454
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