Digital tools for the English for specific purposes approach to developing professional language competence among IT specialists: comparative analysis and selection criteria

Abstract

The article presents a comparative analysis of digital tools for implementing the “English for specific purposes” approach to developing professional language competence among IT specialists in the context of digital transformation of language education. Professional language competence is characterized as a complex integrative formation combining a lexical-terminological component associated with mastery of specialized terminology in software development, cybersecurity, and network technologies; a discursive component presupposing the ability to create and interpret texts of professional genres, including technical documentation, commits, and requirements specifications; a pragmatic component covering communication skills in agile teams, at conferences, and during pair programming; and a sociocultural component reflecting understanding of intercultural aspects of communication in multinational teams. The analysis covers a heterogeneous set of tools classified according to the functional criterion of their primary orientation toward the development of specific components of competence, including specialized corpus tools, platforms for technical writing with feedback, AI assistants for language practice, universal platforms with ESP modules, videoconferencing tools with linguistic functions, dictionary applications for IT terminology, and platforms with authentic professional materials. The results of expert evaluation indicate significant differentiation in their didactic potential, with the highest indicators of alignment with ESP goals and integral assessments obtained by platforms with authentic materials and corpus tools, whereas universal platforms demonstrate limited effectiveness due to the superficiality of content and the absence of real-situation modeling. Generative AI assistants are characterized by high variability of assessments, reflecting divided opinions regarding the reliability of generated content. The developed selection criteria framework, comprising nine parameters ranked by significance with a concordance coefficient of 0.73, identifies as primary the authenticity of professional content, orientation toward target needs, personalization of learning pathways, quality of feedback, and modeling of professional situations, with a clear priority given to substantive characteristics over technical and economic ones. Component analysis confirms the specialization of tools, where the maximum amplitude of differences in effectiveness across components reaches 7.54 points, substantiating the necessity of integrated application of complementary categories of tools to ensure balanced development of the entire competence. The revealed differences in assessments depending on teaching experience, particularly pronounced with regard to AI assistants, underscore the importance of accounting for instructors’ experience. The obtained results facilitate the transition to evidence-based design of the digital component of the educational process in ESP, optimization of methodological systems, and improvement of the quality of professional language training for IT personnel in the global market.