RUDN Journal of Language Studies, Semiotics and SemanticsRUDN Journal of Language Studies, Semiotics and SemanticsВестник Российского университета дружбы народов. Серия: Теория языка. Семиотика. Семантика2313-22992411-1236Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University)5266210.22363/2313-2299-2026-17-2-585-604LTTOJAARTIFICIAL INTELLIGENCE IN APPLIED LINGUISTICS: CURRENT CHALLENGES AND PROSPECTSАКТУАЛЬНЫЕ ВЫЗОВЫ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ПРИКЛАДНОЙ ЛИНГВИСТИКЕResearch ArticleMachine vs Human: Large Language Model Errors as a Means of Creating Stylistically Appropriate TextЧеловек или машина: ошибки больших языковых моделей как средство создания стилистически уместного текстаhttps://orcid.org/0000-0002-5253-273357217873153AAB-4687-20196520-1445ZabolotskikhAnna V.ЗаболотскихАнна Владимировна

Senior lecturer, Department of Foreign Languages, Faculty of Humanities and Social Sciences, Federal State Autonomous Educational Institution of Higher Education

старший преподаватель кафедры иностранных языков факультета гуманитарных и социальных наук

zabolotskikh-av@rudn.ruhttps://orcid.org/0000-0002-1467-345556579818200G-4265-20192013-1826SokolovaElena E.СоколоваЕлена Евгеньевна

PhD in Philology, Associate Professor, Department of Foreign Languages

кандидат филологических наук, доцент Департамента иностранных языков

sokolova.ee@mipt.ruhttps://orcid.org/0000-0002-2727-680357195529929ABW-5226-20223183-1668TavberidzeDarya V.ТавберидзеДарья Владимировна

PhD in Philosophy, Associate Professor, Department of Foreign Languages, Faculty of Humanities and Social Sciences

кандидат философских наук, доцент кафедры иностранных языков факультета гуманитарных и социальных наук

tavberidze-dv@rudn.ruRUDN UniversityРоссийский университет дружбы народовMoscow Institute of Physics and TechnologyМосковский физико-технический институт30092026172ARTIFICIAL INTELLIGENCE IN APPLIED LINGUISTICS: CURRENT CHALLENGES AND PROSPECTSАКТУАЛЬНЫЕ ВЫЗОВЫ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ПРИКЛАДНОЙ ЛИНГВИСТИКЕ58560406102026Copyright ©; 2026, Zabolotskikh A.V., Sokolova E.E., Tavberidze D.V.Copyright ©; 2026, Заболотских А.В., Соколова Е.Е., Тавберидзе Д.В.2026Zabolotskikh A.V., Sokolova E.E., Tavberidze D.V.Заболотских А.В., Соколова Е.Е., Тавберидзе Д.В.https://creativecommons.org/licenses/by-nc/4.0https://journals.rudn.ru/semiotics-semantics/article/view/52662

The relationship between humans and machines in education is analyzing the characteristic features of the LLM artificial language as a tool for teaching the creation of scientific written text through a critical analysis of errors made in texts created by man and machine. This departure from traditional ideas about learning to write in a foreign language reflects progressive processes in teaching, since identifying parallels between human learning and model training based on mistakes refers to a high-tech approach focused on the student rather than the teacher. LLM, as a tool capable of correcting and creating generated text, acts as an incentive for the development of critical thinking when analyzing inaccuracies or evaluating the text “appropriatness”. LLM’s ability to generate different results can be used as a error-based methodology for teaching academic writing. In this regard, academic writing training is a critical analysis and editing of text by visualizing errors, creating a sense of competition between a machine and a human and stimulating mental activity. This seems to be a clear example of how inconsistency and ambiguity can be used for development and creation.

Анализ характерных ошибок, допущенных в текстах, созданных человеком и машиной, рассматривается на примере искусственного языка LLM как инструмента, используемого для обучения созданию научного письменного текста. Данный отход от традиционных представлений об обучении письму на иностранном языке отражает прогрессивные процессы в преподавании, т.к. выявление параллелей между обучением человека и тренировкой модели, основанное на ошибках, относится к высокотехнологичному подходу, ориентированному на учащегося, а не преподавателя. LLM как инструмент, способный корректировать и генерировать текст, выступает стимулом развития критического мышления при анализе неточностей или оценке «уместности» текста. Способность LLM создавать различные типы текста может быть использована для обучения академическому письму в качестве методологии, ориентированной на преодоление ошибок. В этой связи обучение академическому письму представляет собой критический анализ и редактирование текста с помощью визуализации ошибок, создавая ощущение конкуренции между машиной и человеком и стимулируя мыслительную активность. Это является наглядным примером того, как непоследовательность и двусмысленность могут быть использованы в целях развития навыка академического письма.

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