DIAGNOSING THE PROFESSIONAL-COMMUNICATIVE COMPETENCE OF FOREIGN PHILOLOGY STUDENTS THROUGH DIGITAL INSTRUMENTS

Abstract

This article focuses on diagnosing the professional-communicative competence (PCC) in the field of “6B02302-Foreign Philology” in the context of digital transformation in learning foreign languages.

The research objective was to operationalize the assessment of sub-competence (pragmatic-discursive, strategic-professional) and to evaluate the formative potential of digital instruments in the process of philological training.

The methodological framework included a three-point diagnostic scale, implemented through performance-based tasks, questionnaires and observation of students’ activity within digital platforms such as Grammarly, Language Tool, Moodle, Google Docs and so on. The results indicated that 54% of students demonstrated a medium level of PCC, 28% achieved a high level and 18% remained at a low level. The strategic-professional sub-competence showed the highest performance (M=2.7), confirming the effectiveness of digital instruments in formation students’ planning, self-regulation and professional communication adaptability. The findings validate the methodological value of digital diagnostics as an effective means of monitoring and enhancing the communicative preparation of future philologist.

 The findings substantiate the conclusion that learning foreign languages through digital instruments serve as a powerful means of competence formation, yet their pedagogical potential is fully realized only under conditions of systematic methodological integration.

The scientific novelty of the research lies in the development of a diagnostic model for PCC assessment through digital instruments in learning foreign languages, while the practical value consists in the possibility of applying its results for curriculum modernization, the implementation of blended learning strategies and the introduction of adaptive digital environments in philological education. The study outlines perspectives for further research, including longitudinal tracking of competence development and the use of artificial intelligence-driven platforms for personalized learning.