Teaching professionally oriented foreign language vocabulary with artificial intelligence tools

An efficiently constructed process for vocabulary acquisition is a key component of English for Specific Purposes (ESP) instruction for future specialists, forming the foundation of professional communicative competence. This challenge is particularly acute in non-linguistic universities, where the time allocated for foreign language study is severely limited. The most important task is to find new strategies for teaching professionally oriented vocabulary, which make it possible to optimize the acquisition of foreign language lexical material in the curriculum of universities with a non-linguistic profile. At the same time, it is impossible to ignore the technological realities that have recently unfolded before us: the rapid development of artificial intelligence (AI), which began several years ago, inevitably led to transformational processes in various fields of human activity, including education. Having found ourselves in a new framework, we have gained access to previously unimaginable cognitive capacities, the use of which has great potential in teaching foreign languages, although it comes with certain risks. The key to resolving this contradiction lies in selecting optimal teaching units for professionally oriented foreign language vocabulary, whose structure and functionality would align with both the cognitive mechanisms of language acquisition and the specifics of professional discourse. The traditional focus on isolated lexical items, which requires students to mechanically "assemble" them into speech constructions and often leads to violations of collocational and grammatical compatibility, appears insufficiently effective under these conditions. Consequently, the focus of this research is on the concept of stable word sequences, which, when acquired as holistic units, can serve as ready-made building blocks for communication. However, integrating such units into the teaching process is associated with the resource intensity of developing methodological support for working with them. This challenge, in our opinion, can be significantly overcome through the integration of AI tools. This article analyzes modern approaches to teaching vocabulary, particularly the shift from memorizing isolated lexical items to acquiring larger, communicatively meaningful blocks, as discussed in the works of various authors. The terminology used in both Russian and international literature to describe word sequences that function as a single unit in natural speech is examined. The analysis concludes that a universal term for such phrases has yet to be established. The literature features a variety of concepts, including "phrasal unity", "phrasal ensemble" (V.A. Bukhbinder), "syntagma" (L.V. Shcherba), "lexical complex" (Yu.G. Sedelkina), "communicative fragment" (G.M. Gasparov), "lexical unity" (K.Yu. Vartanova), "chunk" (M. Lewis), "formulaic sequence" (N. Schmitt), and others. In this study, we adopt the working term "lexical block", defined as a fragment of speech consisting of two or more words that constitutes a typical and fixed combination in the language. Despite its name, a lexical block is a multilevel construct encompassing not only lexical, but also grammatical and discursive features. This paper substantiates the choice of lexical blocks as the structural and functional unit for teaching professionally oriented vocabulary; the overarching methodological principles for such training are analyzed. A five-step algorithm is then proposed for building professional lexical competence in non-linguistic students, consisting of the following: 1) selection of lexical blocks, 2) their semantization and contextualiza-tion 3) primary fixation, 4) development of automated use skills, and 5) activation of acquired lexical blocks in new communicative situations. The application of AI tools in the process of implementing the algorithm is justified for the semantization of lexical blocks (through generating detailed explanations of meaning, comparing usage variants in different contexts, etc.) and their contextualiza-tion (by generating individual sentences and texts, automated selection of given lexical blocks from corpora of authentic texts); for the primary fixation of lexical blocks and forming skills and abilities in their use (by generating exercises and activities containing target lexical blocks). The paper highlights the crucial need for methodological guidance for students engaging with AI tools. Examples of exercises and activities aimed at the initial consolidation of lexical blocks, as well as the formation of skills in their use, are provided. The paper explores an interactive format for the first and second steps of the procedure, which is designed for group work, and outlines the teacher's role at this stage. Preliminary implementation of this algorithm with first- and second-year Bachelor and Specialist students at the Faculty of Radiophysics, Tomsk State University, based on classroom observation, yielded promising results. However, these initial findings indicating the algorithm's potential efficacy require further validation through structured experimental teaching. The authors declare no conflicts of interest.

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