Sun’iy Intellekt va Raqamli Iqtisodiyot

keladi, bu esa o‘z navbatida til o‘rganishga bo‘lgan ijobiy munosabatni mustahkamlaydi. Bu jihat til o‘rganish motivatsiyasi bilan bevosita bog‘liq. 4. SI dan pedagogik maqsadlarda ongli foydalanish zaruriyati. Empirik natijalar shuni ham ko‘rsatadiki, faqat SI vositasi qo‘llanilishi darhol eng yuqori natijalarni bermaydi — uning pedagogik maqsadga yo‘naltirilgan, strukturaviy va metodologik jihatdan uyg‘unlashtirilgan holda qo‘llanilishi muhim. SI yordamida o‘rgatish tizimi o‘qituvchidan to‘liq mustaqil bo‘lmaydi; balki o‘qituvchi SI vositalaridan samarali foydalanishni nazorat qilib, yo‘naltirib borishi zarur [2,12,18]. Shunga ko‘ra, loyiha asosida o‘qitish yondashuvi doirasida qo‘llanilgan raqamli vositalar va sun’iy intellekt ilovalari talabalar uchun yanada samarali va barqaror o‘quv muhitini yaratadi hamda ushbu usulda ko‘proq ijobiy natijalarga erishiladi. Demak, xorijiy til ta’limida loyiha asosida o‘qitishga tayangan holda rus tilini raqamli vositalar va SI yordamida o‘qitish talabalar va o‘qituvchilarga til ko‘nikmalarini egallash va bilimni saqlab qolishni oshirishda yordam beradi. Chunki bunday muhit o‘qituvchiga boyroq sinf muhiti va xilma-xil materiallarni taqdim etadi. Bundan tashqari, ushbu yondashuv an’anaviy usullarga qaraganda moslashuvchan va motivatsiyalovchi muhit yaratib, o‘rganishni yanada samarali qiladi. Talabalarning o‘rganish uslublari turlicha bo‘lgani sababli, muhitdagi xilma-xillik idrok va eslab qolish samaradorligini oshiradi. Xulosalar. Xulosa qilib aytganda, O‘zbekiston oliy ta’lim tizimida sun’iy intellektning ingliz tili ta’limida qo‘llanilishi nafaqat til o‘rganish samaradorligini oshiradi, balki ta’limning zamonaviy, talaba markazli va raqamli kompetensiyalarni rivojlantiradigan modelini shakllantiradi. SI vositalari yordamida talabalarning individual ehtiyojlari qondiriladi, motivatsiyasi oshadi, muloqot ko‘nikmalari rivojlanadi va o‘qituvchilar darsni yanada interaktiv, ijodiy va maqsadga yo‘naltirilgan tarzda olib borish imkoniga ega bo‘ladi. Shu bilan birga, SI integratsiyasining muvaffaqiyati o‘qituvchilarning raqamli savodxonligi, dars rejalashtirish va metodik qo‘llanmalarning mavjudligi, shuningdek, universitetlarning texnik infratuzilmasiga bog‘liq bo‘ladi. Shuning uchun O‘zbekistonda SI asosidagi ingliz tili ta’limini kengaytirish uchun o‘qituvchilarga treninglar, mavjud ta’lim dasturlariga SI elementlarini kiritish va universitetlarda raqamli infratuzilmani mustahkamlash bo‘yicha tizimli ishlar olib borilishi zarur. Foydalanilgan adabiyotlar: 1. Long, J., & Lin, J. (2022). An empirical study on cultivating college students’ crosscultural communicative competence based on the artificial intelligence English-teaching mode. Frontiers in Psychology, 13, Article 976310. https://doi.org/10.3389/fpsyg.2022.976310 2. Nomass, B.B. (2023). The impact of using technology in teaching English as a second language. English Language and Literature Studies, 3(1), 111. 3. Noom-Ura, S. (2023). English-teaching problems in ThSIland and ThSI teachers’ professional development needs. English Language Teaching, 6(11), 139–147. 4. Opeifa, O., Adelana, O.P., & Atolagbe, O.D. (2022). Teaching oral English through technology: Perceptions of teachers in Nigerian secondary schools. International Journal of Learning and Teaching, 14(1), 41–54. 359

5. Pengju, D. (2022). An English teaching ability evaluation model based on edge computing. Mathematical Problems in Engineering, 2022, 1–8. 6. Ramadhona, N., Putri, A.A., & Wuisan, D. S. S. (2022). Students’ opinions of the use of Quipper School as an online learning platform for teaching English. International Transactions on Education Technology, 1(1), 35–41. 7. Robin, B.R. (2016). The educational uses of digital storytelling. In C. Crawford et al. (Eds.), Proceedings of the Society for Information Technology & Teacher Education International Conference (pp. 709–716). AACE. 8. Softa, V.L. (2022). Attitudes of high school students toward foreign language learning in public and non-public education systems: A demographic approach. In O. Noroozi & I. Sahin (Eds.), Proceedings of IHSES 2022 – International Conference on Humanities, Social and Education Sciences (pp. 218–229). ISTES Organization. 9. Stockwell, A. (2020). Using mobile phones for vocabulary activities: Examining the effect of the platform. Language Learning & Technology, 14(2), 16. 10. Stevani, L., & Putro, N. H. P. S. (2020). Teaching English through social media to promote students’ 21st century skills. In Teacher Education and Professional Development in Industry 4.0 (pp. 409–414). CRC Press. 11. Sullivan, N., & Pratt, E. (2019). A comparative study of two ESL writing environments: A computer-assisted classroom and a traditional oral classroom. System, 24(4), 491–501. 12. Susanty, L., Hartati, Z., Sholihin, R., Syahid, A., & Liriwati, F. Y. (2021). Why English teaching truth on digital trends as an effort for effective learning and evaluation: Opportunities and challenges. Linguistics and Culture Review, 5(S1), 303–316. 13. Thrall, J. H., Li, X., Li, Q., Cruz, C., Do, S., Dreyer, K., & Brink, J. (2018). Artificial intelligence and machine learning in radiology: Opportunities, challenges, pitfalls, and criteria for success. Journal of the American College of Radiology, 15(3), 504–508. 14. Wang, L. (2021). Role of machine learning and artificial intelligence algorithms for teaching reform of linguistics. Journal of Intelligent & Fuzzy Systems, 40(2), 3251–3262. 15. Yee, K., & Hargis, J. (2022). Digital storytelling: Kizoa, Animoto, and Photo Story 3. Turkish Online Journal of Distance Education, 13(1), 12–14. 16. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. 17. Zeng, Y. (2021). Application of flipped classroom model driven by big data and neural network in oral English teaching. Wireless Communications and Mobile Computing, 2021, 1–7. 18. Zhou, X., & Wu, X. (2022). Teaching mode based on educational big data mining and digital twins. Computational Intelligence and Neuroscience, 2022. 19. Suh, S. (2025). Investigating the impact of personalized SI tutors on language learning performance (arXiv preprint). arXiv. https://doi.org/10.48550/arXiv.2505.02443 THE ROLE OF PRESUPPOSITION IN DIPLOMATIC DISCOURSE: AN AI-BASED PRAGMATIC ANALYSIS Oqila Bayjanova Iskandarovna, Associate professor, PhD in Philology, UzSWLU oqilabayjanova89@gmail.com Abstract. This article investigates the role of presupposition in diplomatic discourse from a pragmatic perspective using AI-supported analytical tools. Diplomatic language is characterized by indirectness, strategic ambiguity, and implicit meaning, where presupposition 360

functions as a powerful mechanism for shaping interpretations and managing political intentions. The study examines how presuppositions are embedded in diplomatic speeches and official statements to construct legitimacy, maintain politeness, and influence international audiences. AI-based methods are applied to identify and classify presupposition triggers, including factive verbs, definite descriptions, temporal clauses, and rhetorical constructions. The findings demonstrate that presupposition plays a key role in strengthening ideological positions and framing political narratives while reducing explicit responsibility. The article concludes that AI-assisted pragmatic analysis can significantly enhance the detection of implicit meanings in diplomatic communication and contribute to modern discourse studies. Keywords: presupposition; diplomatic discourse; pragmatics; implicit meaning; presupposition triggers; political communication; AI-based analysis; NLP tools; discourse strategies; pragmatic manipulation. Аннотация. В данной статье рассматривается роль пресуппозиции в дипломатическом дискурсе с прагматической точки зрения с применением AIинструментов анализа. Дипломатическая речь характеризуется косвенностью, стратегической неопределённостью и скрытым смыслом, в которых пресуппозиция выступает важным механизмом формирования интерпретации и управления политическими намерениями. В исследовании анализируется, каким образом пресуппозиции используются в дипломатических выступлениях и официальных заявлениях для создания легитимности, соблюдения речевого этикета и воздействия на международную аудиторию. Методы искусственного интеллекта применяются для выявления и классификации триггеров пресуппозиции, включая фактивные глаголы, определённые описания, временные конструкции и риторические формы. Полученные результаты показывают, что пресуппозиция играет ключевую роль в укреплении идеологических позиций и формировании политического нарратива при снижении прямой ответственности говорящего. Делается вывод о том, что AI-поддерживаемый прагматический анализ повышает эффективность выявления имплицитных смыслов в дипломатической коммуникации и расширяет возможности современного дискурсанализа. Ключевые слова: пресуппозиция; дипломатический дискурс; прагматика; имплицитный смысл; триггеры пресуппозиции; политическая коммуникация; AI-анализ; NLP-инструменты; дискурсивные стратегии; прагматическая манипуляция. Annotatsiya. Ushbu maqolada diplomatik diskursda presupozitsiyaning roli pragmatik nuqtayi nazardan sun’iy intellektga asoslangan tahlil vositalari yordamida o‘rganiladi. Diplomatik nutq bilvositalik, strategik noaniqlik va yashirin mazmun bilan ajralib turadi, bunda presupozitsiya auditoriya talqinini shakllantirish hamda siyosiy niyatni boshqarishda muhim mexanizm sifatida namoyon bo‘ladi. Tadqiqotda diplomatik nutqlar va rasmiy bayonotlarda presupozitsiyalar legitimlik yaratish, nutqiy odobni saqlash hamda xalqaro auditoriyaga ta’sir ko‘rsatish maqsadida qanday qo‘llanilishi tahlil qilinadi. AI yondashuvi orqali presupozitsiya triggerlari, jumladan faktiv fe’llar, aniq ta’riflar, vaqt qo‘shimchalari va ritorik konstruksiyalar aniqlanib, tasniflanadi. Natijalar presupozitsiyaning ideologik pozitsiyalarni mustahkamlash va siyosiy narrativni shakllantirishda, shuningdek, gapiruvchining ochiq mas’uliyatini kamaytirishda muhim rol o‘ynashini ko‘rsatadi. Xulosa sifatida, AI-yordamidagi pragmatik tahlil diplomatik muloqotdagi yashirin ma’nolarni aniqlash samaradorligini oshirishi va zamonaviy diskurs tadqiqotlarini boyitishi ta’kidlanadi. Kalit so‘zlar: presupozitsiya; diplomatik diskurs; pragmatika; yashirin ma’no; presupozitsiya triggerlari; siyosiy kommunikatsiya; AI-tahlil; NLP vositalari; diskurs strategiyalari; pragmatik manipulyatsiya. 361

Introduction. Diplomatic discourse represents one of the most strategic and ideologically loaded forms of institutional communication. Unlike everyday conversation, diplomatic language is typically shaped by political interests, international relations, and the need to maintain balance between cooperation and confrontation. Therefore, diplomats and political leaders rarely express intentions directly. Instead, they rely on indirectness, ambiguity, and implicit pragmatic devices to convey messages without producing open conflict. In this context, presupposition becomes one of the most effective linguistic mechanisms that allows speakers to deliver hidden meanings while presenting them as shared knowledge or unquestionable truth. Presupposition is commonly understood as background information assumed to be accepted by both the speaker and the listener. In diplomatic communication, presuppositions are not merely linguistic elements; they function as instruments of political influence. Through presupposition, diplomats may frame events, assign responsibility, legitimize political decisions, or shape public perception without explicitly stating controversial claims. For example, the statement “We must restore stability in the region” presupposes that instability already exists and that the speaker’s side has the authority to restore it. Such presuppositions guide the listener’s interpretation by presenting certain assumptions as already established facts. The relevance of presupposition in diplomatic discourse has increased significantly in modern political communication, where speeches and official statements are widely distributed through global media and social networks. In such environments, language becomes a tool of persuasion and narrative control. Presupposition allows political actors to build ideological positions subtly, often avoiding direct accusation while still constructing a specific worldview. This pragmatic phenomenon is closely related to power discourse, manipulation, and institutional authority. In recent years, the development of artificial intelligence has introduced new possibilities for linguistic and pragmatic research. AI-based tools and natural language processing (NLP) techniques can process large corpora of diplomatic texts and detect recurring pragmatic patterns that may remain unnoticed in manual analysis. This technological shift has encouraged researchers to integrate computational approaches with classical pragmatics and discourse analysis. AIassisted methods can identify presupposition triggers, classify their types, and measure their frequency across political contexts, thus improving the reliability and scope of diplomatic discourse studies. The present article aims to investigate the role of presupposition in diplomatic discourse through an AI-based pragmatic analysis. The study focuses on the identification and classification of presupposition triggers in diplomatic speeches and official statements. The objectives of the research include analyzing how presuppositions contribute to indirectness, politeness strategies, ideological framing, and the construction of legitimacy. Furthermore, the study explores how AI tools can support pragmatic research by providing systematic corpus-based evidence. The significance of this study lies in its interdisciplinary approach 362

combining pragmatics, discourse studies, and artificial intelligence. By applying AI-based analytical techniques to diplomatic texts, the research contributes to a deeper understanding of implicit meaning and pragmatic manipulation in political communication. Ultimately, this investigation demonstrates that presupposition is not only a linguistic phenomenon but also a strategic instrument that shapes diplomatic narratives and influences international audiences. Presupposition has been widely studied in pragmatics and semantics as a key mechanism of implicit meaning. Classical works by scholars such as Frege and Strawson laid the theoretical foundation for presupposition by emphasizing the relationship between truth conditions and assumed background information. Later, Stalnaker developed the concept of common ground, arguing that presuppositions are propositions treated as shared knowledge between interlocutors. This approach has been particularly influential in discourse analysis, where presupposition is viewed as a tool for constructing coherence and guiding interpretation. Yule defines presupposition as something the speaker assumes to be true prior to making an utterance. This definition highlights the pragmatic nature of presupposition and its dependence on context. Levinson further develops this idea by explaining presupposition as a part of pragmatic inference, where the listener derives unstated meanings based on linguistic triggers and communicative expectations. These theoretical frameworks suggest that presupposition is deeply connected with the communicative goals of the speaker and the interpretive process of the audience. The identification of presupposition triggers has been an important topic in pragmatic studies. Scholars such as Karttunen and Kiparsky classify triggers into categories such as factive verbs, implicative verbs, change-of-state verbs, temporal clauses, definite descriptions, cleft constructions, and comparative structures. These triggers signal the existence of background assumptions. For example, the use of factive verbs such as “realize” or “regret” presupposes the truth of the embedded proposition. Similarly, definite descriptions such as “the crisis” presuppose that the crisis exists and is recognized by the audience. In political and diplomatic discourse, presupposition is often discussed as a strategic communicative tool. Fairclough’s critical discourse analysis emphasizes that political language is never neutral and that implicit meaning functions as a mechanism of power. Van Dijk also argues that political discourse relies on ideological structures that shape group identity and legitimize political actions. Presupposition contributes to this process by presenting ideological assumptions as natural and unquestionable. In this way, presupposition becomes an effective instrument for framing narratives, shifting responsibility, and manipulating public opinion. Diplomatic discourse, in particular, is characterized by indirectness and politeness. Brown and Levinson’s politeness theory provides a useful framework for understanding why diplomats avoid direct statements. According to their theory, face-saving strategies are essential in institutional communication, especially when disagreement or conflict is possible. Presupposition supports politeness by allowing diplomats to imply accusations or demands indirectly. For 363

instance, the statement “We hope the other side will return to peaceful dialogue” presupposes that the other side has abandoned peaceful dialogue, yet it avoids direct confrontation. Recent research also emphasizes the role of presupposition in constructing legitimacy and authority in political speech. Chilton highlights that political actors use language to create conceptual metaphors and frames that justify their actions. Presupposition plays a central role in this framing process because it allows political leaders to treat their interpretations of events as shared truth. This strategy is particularly common in international speeches, where political actors must justify military interventions, sanctions, or diplomatic negotiations. With the rise of artificial intelligence and computational linguistics, new methods have emerged for analyzing presupposition in large corpora. Natural language processing tools can identify linguistic triggers, categorize them, and measure their distribution across different contexts. AI-based discourse analysis has become an important area in digital humanities and political linguistics. Researchers argue that computational approaches allow for more objective and large-scale analysis compared to traditional manual methods. AI can also detect subtle patterns of manipulation and implicit meaning that may be overlooked by human analysts. Nevertheless, scholars also note that AI systems face challenges in pragmatic interpretation. While AI can detect lexical triggers, understanding pragmatic meaning requires contextual awareness and knowledge of political situations. Therefore, AI-assisted analysis is most effective when combined with qualitative interpretation. This hybrid approach strengthens both methodological reliability and theoretical depth. Overall, existing literature confirms that presupposition is a crucial pragmatic phenomenon in diplomatic discourse. It functions as a tool for indirect communication, ideological framing, politeness strategies, and political persuasion. At the same time, the integration of AI-based methods provides new opportunities for systematic detection and classification of presupposition triggers. However, effective analysis requires combining computational tools with pragmatic and discourse-based interpretation. This study contributes to the field by applying an AI-supported framework to explore presupposition strategies in diplomatic texts, highlighting the role of implicit meaning in shaping international political communication. Methodology. This study applies a qualitative-pragmatic research design supported by AI-based linguistic tools in order to investigate presupposition in diplomatic discourse. The methodology is based on the combination of classical pragmatic theory and computational text analysis, allowing the research to identify both linguistic presupposition triggers and their communicative functions in political communication. Since diplomatic discourse is characterized by strategic ambiguity and indirect persuasion, the analysis focuses on how presupposition structures meaning implicitly rather than explicitly. The research material consists of a small corpus of diplomatic texts, including official speeches, press releases, and formal statements produced by international political institutions and state representatives. The texts were selected from 364

publicly available diplomatic sources such as official government websites, international summit speeches, UN-related statements, and ministerial press briefings. The corpus includes texts that reflect major diplomatic communicative goals: conflict management, peace-building rhetoric, legitimization of political decisions, and international cooperation narratives. The data selection process was based on the presence of pragmatically rich constructions that typically contain presupposition triggers. The corpus was filtered to include texts with high frequency of factive verbs, definite descriptions, temporal markers, cleft constructions, comparative structures, and evaluative adjectives. These triggers were chosen because they are widely recognized in presupposition theory as the main linguistic indicators of assumed background information. AI-supported methods were employed to enhance the accuracy and scale of the analysis. First, texts were processed using natural language processing tools for tokenization and syntactic segmentation. Second, AI-assisted keyword extraction was applied to identify high-frequency pragmatic markers. Third, presupposition triggers were detected through pattern recognition, focusing on lexical units such as “regret,” “realize,” “continue,” “return,” “restore,” “again,” “still,” “the fact that,” “even,” and “already.” Additionally, large language models were used to generate alternative paraphrases of diplomatic statements to observe whether presupposed meanings remain stable or change under reformulation. The analysis follows a multi-step procedure. At the first stage, presupposition triggers were identified and categorized according to established classifications in pragmatic studies. At the second stage, each presupposition was interpreted contextually in order to determine its implicit meaning. At the third stage, the pragmatic function of the presupposition was analyzed, focusing on its role in framing, legitimization, responsibility shifting, and politeness. Finally, the results were systematized into functional categories, demonstrating how presupposition operates as a diplomatic strategy. To ensure reliability, the research uses a triangulation approach. The linguistic interpretation is supported by AI-based detection, while the final evaluation is conducted through qualitative pragmatic reasoning. This combined method strengthens the validity of conclusions, since presupposition in diplomatic discourse cannot be fully understood through computational analysis alone without contextual interpretation. Results and Discussion. The analysis of diplomatic discourse demonstrates that presupposition functions as one of the most effective pragmatic tools for shaping interpretation and controlling political narratives. The results show that presupposition in diplomatic texts is not accidental but systematically employed to frame international events, influence public perception, and indirectly assign responsibility. Diplomatic presuppositions are often embedded in seemingly neutral sentences, yet they carry strong ideological implications. One of the most frequent presupposition triggers identified in the corpus is the use of definite 365

descriptions. Diplomatic statements often employ noun phrases such as “the crisis,” “the aggression,” “the violation,” “the peace process,” “the international community,” and “the legitimate government.” Such expressions presuppose the existence of a specific event or reality without explicitly proving it. For instance, the statement “We call on all parties to end the violence and restore stability in the region” presupposes that violence is already occurring and that stability has been disrupted. The presupposition is introduced through the definite description “the violence” and the verb “restore,” which implies that stability existed before but has been lost. A similar example can be observed in the phrase “The ongoing conflict must be resolved through dialogue.” The adjective “ongoing” presupposes that a conflict already exists and continues. The speaker does not explicitly argue whether the conflict is real or who caused it. Instead, the existence of the conflict is presented as an unquestionable fact. This strategy is typical in diplomatic discourse because it allows political actors to avoid direct accusations while still framing the situation in a particular way. Another common group of triggers is factive verbs, such as “regret,” “realize,” “acknowledge,” “recognize,” and “be aware.” These verbs presuppose the truth of the proposition embedded in the clause. For example, in the statement “We regret the violation of international law,” the verb “regret” presupposes that a violation has occurred. The sentence does not present the violation as a debatable claim but as a factual reality. In diplomatic communication, this type of presupposition is strategically used to accuse indirectly. The speaker avoids direct confrontation but still communicates a strong evaluative position. The analysis shows that factive verbs are particularly frequent in statements related to sanctions, peace negotiations, and international conflicts. For example, “We recognize the suffering of civilians” presupposes that civilians are suffering. Another example is “We are aware of the growing threats in the region,” which presupposes that threats exist and are increasing. These constructions frame political realities as already established facts, leaving little space for alternative interpretation. Temporal adverbs such as “still,” “already,” “again,” “no longer,” and “yet” also play a major role in presupposition construction. They are extremely common in diplomatic language because they allow speakers to highlight continuity, blame, or urgency. For example, the statement “The country is still facing instability” presupposes that instability existed previously and continues. The adverb “still” implies that the situation has not improved, indirectly suggesting failure or responsibility. Similarly, “We urge the parties to return to negotiations” presupposes that negotiations were taking place earlier but were interrupted. The verb “return” creates a presupposition of past dialogue, framing the current absence of negotiations as abnormal. Another example is “The government has once again violated its obligations.” The phrase “once again” presupposes that similar violations occurred before. This is a powerful diplomatic strategy because it constructs a narrative of repetition and unreliability without providing explicit 366

evidence. It shapes the audience’s perception by implying that the actor has a history of wrongdoing. This type of presupposition is often used in international statements condemning political opponents. Change-of-state verbs such as “stop,” “continue,” “restore,” “begin,” and “resume” were also found to be significant presupposition triggers. These verbs presuppose a previous state. For instance, “We call on the parties to stop hostile actions” presupposes that hostile actions are currently happening. The verb “stop” embeds the assumption of existing hostility. Similarly, “The country must resume reforms” presupposes that reforms were previously underway but have stopped. Diplomatic discourse frequently employs these verbs because they allow speakers to make implicit accusations while maintaining formal politeness. The analysis also identified the use of cleft constructions and rhetorical emphasis as a tool for strengthening presupposition. Cleft sentences such as “It is the responsibility of the international community to act” presuppose that the international community has responsibility. The construction frames responsibility as an already accepted fact. Another example is “What we need now is a commitment to peace,” which presupposes that commitment is currently missing. Such structures are persuasive because they create focus and urgency while embedding assumptions. Comparative structures were also frequent in diplomatic statements. For example, “This situation is more dangerous than before” presupposes that the situation existed before and was dangerous earlier. The comparative “more dangerous” implies a historical trajectory of worsening conditions. Another example is “We are closer than ever to achieving peace,” which presupposes that peace has been an ongoing goal and that progress has been made. These comparisons function as narrative-building tools, shaping the audience’s understanding of time and political progress. The results further show that presupposition plays an essential role in legitimization strategies. Diplomatic discourse often relies on presuppositions to establish moral authority and justify political actions. For example, “We must defend democratic values” presupposes that democratic values are under threat. The speaker does not explicitly prove the existence of a threat but assumes it as common knowledge. Similarly, “We stand with our partners in protecting international security” presupposes that international security requires protection and that the speaker’s side is acting legitimately. In addition, presupposition functions as a responsibility-shifting mechanism. Diplomatic speakers often use presuppositions to suggest blame indirectly. For instance, “We hope the other side will finally respect international norms” presupposes that the other side has not respected international norms before. The word “finally” strengthens the presupposition, implying long-term disobedience. This is a common diplomatic tactic because it allows criticism without direct accusation. AI-assisted analysis confirmed that such presuppositions are highly repetitive across diplomatic texts. NLP-based detection revealed that the most frequent presupposition triggers include “restore,” “return,” “still,” “again,” 367

“continue,” “regret,” and “recognize.” These triggers represent a systematic pragmatic pattern in diplomatic discourse, where speakers frame their interpretation as shared truth. However, the study also shows that AI tools have limitations in pragmatic interpretation. While AI can successfully detect triggers, it cannot fully determine the political function without contextual interpretation. For example, the phrase “the legitimate government” presupposes legitimacy, but AI cannot always distinguish whether the phrase is used neutrally or as a strategic ideological claim. Similarly, “the international community” may presuppose global agreement, but in reality it may refer only to certain political alliances. Therefore, AI analysis must be combined with qualitative pragmatic interpretation. Overall, the findings confirm that presupposition is a core mechanism of implicit meaning in diplomatic discourse. It enables speakers to construct political narratives, establish legitimacy, maintain politeness, and manipulate interpretation while avoiding direct confrontation. AI-based analysis significantly supports the detection and classification of presuppositions, but human interpretation remains essential for understanding ideological and pragmatic effects. Conclusion. The study confirms that presupposition plays a crucial role in diplomatic discourse as a pragmatic instrument of indirect communication and political framing. Diplomatic speakers systematically rely on presupposition triggers such as definite descriptions, factive verbs, temporal markers, change-ofstate verbs, cleft constructions, and comparative structures in order to embed assumptions into their statements. These presuppositions function as strategic tools for legitimizing political actions, shifting responsibility, strengthening ideological positions, and shaping international narratives while maintaining formal politeness and diplomatic neutrality. The results also demonstrate that AI-assisted pragmatic analysis can significantly improve the identification and classification of presuppositions in large corpora of diplomatic texts. NLP-based tools effectively detect frequent triggers and reveal systematic patterns of implicit meaning. Nevertheless, AI methods remain limited in interpreting the deeper ideological and contextual dimensions of presupposition, as diplomatic meaning often depends on geopolitical realities and communicative intention. Therefore, the most effective approach is a hybrid methodology in which AI provides technical support while human researchers conduct pragmatic interpretation. In conclusion, presupposition should be regarded not only as a linguistic phenomenon but also as a powerful diplomatic strategy that influences perception and political decision-making. The integration of AI into pragmatic research opens new perspectives for discourse analysis and provides a modern interdisciplinary framework for investigating implicit meaning in international communication. References: 1. Brown, P., & Levinson, S.C. (1987). Politeness: Some universals in language usage. Cambridge University Press. 2. Chilton, P. (2004). Analysing political discourse: Theory and practice. Routledge. 368

3. Fairclough, N. (1992). Discourse and social change. Polity Press. 4. Fairclough, N. (1995). Critical discourse analysis: The critical study of language. Longman. 5. Frege, G. (1892). On sense and reference. In P. Geach & M. Black (Eds.), Translations from the philosophical writings of Gottlob Frege (pp. 56–78). Blackwell. 6. Karttunen, L. (1973). Presuppositions of compound sentences. Linguistic Inquiry, 4(2), 169–193. 7. Kiparsky, P., & Kiparsky, C. (1971). Fact. In D. Steinberg & L. Jakobovits (Eds.), Semantics: An interdisciplinary reader in philosophy, linguistics and psychology (pp. 345–369). Cambridge University Press. 8. Levinson, S. C. (1983). Pragmatics. Cambridge University Press. 9. O‘Halloran, K.L. (2011). Multimodal discourse analysis. In K. Hyland & B. Paltridge (Eds.), The continuum companion to discourse analysis (pp. 120–137). Continuum. 10. Stalnaker, R. (1974). Pragmatic presuppositions. In M. Munitz & P. Unger (Eds.), Semantics and philosophy (pp. 197–213). New York University Press. 11. Strawson, P.F. (1950). On referring. Mind, 59(235), 320–344. 12. Van Dijk, T.A. (1997). What is political discourse analysis? In J. Blommaert & C. Bulcaen (Eds.), Political linguistics (pp. 11–52). Benjamins. 13. Van Dijk, T.A. (2006). Politics, ideology, and discourse. In R. Wodak (Ed.), Encyclopedia of language and linguistics (2nd ed., pp. 728–740). Elsevier. 14. Wodak, R. (2001). The discourse-historical approach. In R. Wodak & M. Meyer (Eds.), Methods of critical discourse analysis (pp. 63–94). Sage. USING AI TO IMPROVE STUDENTS’ WRITING Urishev Abdumajid, ELT Senior Teacher at UzSWLU, madjidmadjidov@gmail.com Annotatsiya. Ushbu maqola yozuv ko‘nikmasini o‘qitishda sun’iy intellekt vositalaridan (generativ SI va avtomatlashtirilgan yozma baholash) foydalanish imkoniyatlarini tahlil qiladi. SI fikr-mulohazasi grammatik aniqlik va tahrir strategiyalarini kuchaytiradi, biroq ishonchlilik va akademik halollik bo‘yicha ehtiyot choralarini talab qiladi. Kalit so‘zlar: sun’iy intellekt, yozuv ko‘nikmasini o‘qitish, qayta yozish (revision), fikrmulohaza, akademik halollik. Аннотация. Статья анализирует использование инструментов ИИ (генеративный ИИ и автоматизированная оценка письма) для улучшения письменной речи студентов. ИИ-обратная связь повышает точность и качество редактирования, но требует педагогического контроля из-за надежности, предвзятости и рисков академической нечестности. Ключевые слова: искусственный интеллект, обучение письму, редактирование, обратная связь, академическая честность. Abstract. This article reviews how AI tools (generative AI and automated writing evaluation) can enhance students’ writing through formative feedback, iterative revision, and targeted language support. It also addresses limitations bias, reliability, and integrity and proposes a classroom workflow for responsible, learning-oriented adoption. Key words: artificial intelligence, L2 writing, revision, feedback, academic integrity. Introduction. Good writing in English is important for university study and future work. However, many EFL students find writing difficult: their ideas may be unclear, paragraphs may not be well organized, and grammar and vocabulary problems can reduce clarity. Teachers can help through feedback, but in many 369

contexts feedback is limited because classes are large and marking takes a lot of time. When feedback is delayed or too short, students often revise less, and their writing improves slowly. AI tools can help by giving faster feedback during drafting and revision. In ELT, two types of AI are commonly used. First, automated writing evaluation (AWE) tools (such as grammar and style checkers) provide immediate comments on language accuracy and basic writing features. Research shows that, when used for learning (not only for scoring), AWE can improve L2 writing performance and motivation and can reduce writing anxiety (Huang et al., 2024). Second, generative AI tools (such as ChatGPT) can give interactive explanations and revision suggestions and help students improve wording and organization. At the same time, research warns that students may over-trust the tool, accept incorrect advice, or depend on AI instead of developing their own writing skills (Barrot, 2023). Responsible use is necessary, especially for academic integrity and fairness. For example, some AI-detection tools can wrongly flag human writing, and this problem may be stronger for non-native English writers, which creates equity risks in assessment (Myers, 2023). For this reason, a learning-focused approach is needed: teachers should set clear rules for what AI support is allowed and design writing tasks that still measure students’ own progress. This article applies an integrative review approach, combining recent research on automated writing evaluation (AWE) and generative AI in L2 writing with a classroom-oriented design that positions AI as formative support for drafting and revision. The review is guided by three practical questions: whether AI feedback improves writing outcomes over time, how teachers can ensure that students engage with feedback critically rather than accept it automatically, and what integrity and fairness risks arise when AI detection is used in assessment (Huang et al., 2024; Barrot, 2023; Myers, 2023). Evidence indicates that AI improves writing most effectively when it accelerates the feedback cycle and increases opportunities for iterative revision; in particular, AWE tools can strengthen L2 writing performance and motivation and may reduce writing anxiety when used as learning-oriented feedback rather than as a final score or substitute for instruction (Huang et al., 2024). However, these benefits are conditional: students’ gains depend on how they process feedback (deep revision vs surface correction), whether the teacher provides strategy instruction (e.g., identifying patterns of error, prioritizing meaning over form, and checking whether a “correction” changes the intended message), and whether classroom time and assessment criteria reward revision and learning rather than only polished final products (Huang et al., 2024). Generative AI extends feedback beyond error correction by offering explanations, alternative phrasing, and organization advice, which can be especially useful for students who lack metalinguistic knowledge or struggle to diagnose why a sentence is unclear; yet research warns that students may over-trust AI outputs, accept inaccurate or stylistically inappropriate suggestions, or outsource idea development, voice, and argumentation unless 370

teachers explicitly teach “AI feedback literacy” (e.g., evaluating the reason behind a suggestion, comparing options, asking for rule-based explanations, and verifying recommendations through examples or reference materials) (Barrot, 2023). A robust classroom model therefore treats AI not as an “answer machine” but as a feedback generator whose suggestions must be tested against task requirements, genre conventions, and the writer’s communicative intention; practically, this can be operationalized through a teacher-controlled writing cycle in which students produce an initial draft, use AI only for diagnostic feedback (clarity, cohesion, grammar patterns, and vocabulary precision), and then complete a verification step—such as selecting 5–10 AI suggestions, explaining why each is accepted or rejected, and checking at least a few items using a grammar reference, a teacher-provided checklist, or corpus-style examples—before revising (Barrot, 2023). To strengthen accountability and learning evidence, students can submit a brief revision memo (“What I changed and why”), highlight one paragraph where coherence improved, and identify two recurring language problems with a plan for future drafts; such process documentation makes learning visible and discourages uncritical copy-paste practices because students must articulate the relationship between feedback and revision decisions (Huang et al., 2024; Barrot, 2023). Integrity management should also be redesigned: because some AI detectors can falsely label human writing as AI-generated—and this risk may be higher for nonnative English writers—high-stakes judgments based mainly on detection can be unfair and pedagogically counterproductive, pushing students toward concealment rather than responsible use (Myers, 2023). Instead, the evidence supports governance through transparent rules (what AI use is permitted for each task), assessment designs that require authorship evidence (draft history, in-class writing checkpoints, oral explanation of key claims, or short post-submission conferences), and teacher sampling that prioritizes higher-order issues such as argument quality, coherence, and appropriate source use rather than only surface correctness (Myers, 2023; Barrot, 2023). Within this framework, AI becomes a tool for strengthening the revision habit and improving language control, while the teacher remains the authority on genre, purpose, and evaluation; the net result is a sustainable feedback ecology in which students receive more timely guidance, practice decision-making about language, and develop long-term writing competence—without turning writing into a purely technical exercise of polishing AI-generated text (Huang et al., 2024; Barrot, 2023; Myers, 2023). Overall, the research-based picture is consistent: AI can support students’ writing best when it is used to increase feedback frequency and strengthen revision habits, not when it replaces student thinking or drafting (Huang et al., 2024; Barrot, 2023). AWE tools can improve L2 writing performance and motivational variables, particularly when feedback is embedded in a formative 371

cycle where students revise repeatedly and learn how to respond strategically to comments (Huang et al., 2024). Generative AI can extend support by explaining language choices, suggesting clearer phrasing, and helping students notice weaknesses in coherence and argument structure; however, this benefit depends on explicit instruction in “AI feedback literacy,” because students may over-trust AI suggestions or accept inaccurate guidance without verification (Barrot, 2023). Integrity management should also be learning-centered: because AI detectors can misclassify human writing and may produce higher false positives for non-native English writers, high-stakes reliance on detection can create inequitable outcomes and weaken trust in assessment (Myers, 2023). Therefore, the most defensible approach is not “detection-first,” but “design-first”: clear rules for permitted AI use, process evidence (drafts, revision memos, in-class checkpoints), and teacher oversight that prioritizes higher-order writing development rather than only surface correctness (Myers, 2023; Barrot, 2023). Recommendations: (1) Using a structured AI-assisted writing cycle: student-authored draft → AI diagnostic feedback (restricted to comments/explanations) → verification of selected suggestions → revision → short reflection explaining changes (Huang et al., 2024; Barrot, 2023). (2) Teaching AI feedback literacy explicitly: require students to justify accept/reject decisions, identify recurring error patterns, and verify at least some feedback using examples or reference resources, so AI becomes a trigger for metalinguistic awareness rather than a shortcut (Barrot, 2023). (3) Separating practice from assessment: allow broader AI support in low-stakes drafting and editing tasks, but use AI-limited conditions for high-stakes writing (e.g., in-class writing, oral defense of key claims, or staged submissions) to protect construct validity (Huang et al., 2024). (4) Replacing detector-heavy policing with transparent governance: publish a simple policy describing allowed AI uses per task, require brief disclosure when AI is used, and evaluate learning through drafts, revision memos, and teacher sampling—because detector outputs can be unreliable and unfair for L2 writers (Myers, 2023). (5) Prioritizing higher-order writing goals: use teacher time saved by AI surface feedback to focus on coherence, argument quality, source integration, and genre conventions, ensuring that students develop independent control over meaning and academic voice rather than only producing “clean” sentences (Huang et al., 2024; Barrot, 2023). List of references: 1. Barrot, J.S. (2023). Using ChatGPT for second language writing: Pitfalls and potentials. Assessing Writing, 57, 100745. https://doi.org/10.1016/j.asw.2023.100745 2. Huang, X., Xu, W., Li, F., & Yu, Z. (2024). A meta-analysis of effects of automated writing evaluation on anxiety, motivation, and second language writing skills. The Asia-Pacific 3. Education Researcher, 33(4), 957–976. https://doi.org/10.1007/s40299-02400865-y 372

4. Myers, A. (2023, May 15). AI-detectors biased against non-native English writers. Stanford Institute for Human-Centered Artificial Intelligence (HAI). RETRANSLYATSIYA JARAYONIDA INTERTEKSTUAL ELEMENTLARNING YANGI TALQINI: ESKI TARJIMALARNI YANGILASHDAGI EKSTRALINGVISTIK SABABLAR Normurodova Oygul Bobirjon qizi, SamDCHTI, Ingliz tili nazariyasi va amaliyoti kafedrasi tayanch doktoranti, oygulnormurodova9@gmail Annotatsiya. Ushbu maqolada retranslyatsiya jarayonida intertekstual birliklar (alluziya, iqtibos, madaniy ishora va paratekst) qayta talqin qilinishining sabablari tahlil qilinadi. Eski tarjimalarni yangilashga turtki bo‘luvchi ekstralingvistik omillar—ideologiya, senzura, bozor talabi, o‘quvchi didi va norma o‘zgarishi—nazariy asosda yoritiladi. Kalit so‘zlar: retranslyatsiya, intertekstuallik, paratekst, ekstralingvistik omillar, tarjima normasi. Аннотация. В статье рассматривается, как в процессе ретрансляции заново интерпретируются интертекстуальные элементы (аллюзии, цитаты, культурные отсылки и паратекст). Показано, что обновление старых переводов обусловлено экстралингвистическими факторами: идеологией, цензурой, издательской политикой, рыночным спросом, сменой читательских ожиданий и переводческих норм. Ключевые слова: ретрансляция, интертекстуальность, паратекст, экстралингвистические факторы, переводческая норма. Annotation. The article examines how retranslation reinterprets intertextual elements— quotations, allusions, cultural references, and paratexts. It argues that updating older translations is primarily driven by extralinguistic forces such as ideology, censorship, publishing policies, market dynamics, shifting readership expectations, and evolving translation norms. Key words: retranslation, intertextuality, paratext, extralinguistic factors, translation norms. Kirish. Tarjima tarixida bir asarning bir necha marta tarjima qilinishi tabiiy holat bo‘lsa-da, zamonaviy tarjimashunoslikda bu hodisa retranslyatsiya (retranslation) sifatida alohida tadqiqot obyektiga aylandi. Retranslyatsiya nafaqat “eski tarjimani yangilash”, balki matnni yangi davr, yangi o‘quvchi va yangi madaniy muhitga qayta moslashtirish jarayonidir. Aynan shu nuqtada intertekstual elementlar—ya’ni asar ichidagi iqtiboslar, tarixiy-madaniy ishoralar, badiiy an’analar bilan bog‘liq alluziyalar, shuningdek muqaddima, izoh, sarlavha kabi paratekst birliklari—yangicha talqinga ega bo‘la boshlaydi. Maqsad: retranslyatsiya jarayonida intertekstual elementlarning qayta talqin qilinishiga olib keluvchi ekstralingvistik sabablarni tizimli yoritish va amaliy tahlil uchun konseptual mezonlar taklif etish. Vazifalar: retranslyatsiya va intertekstuallik o‘rtasidagi bog‘liqlikni nazariy asoslash; ekstralingvistik omillarni (ideologiya, senzura, bozor, norma, institutlar) tasniflash; intertekstual birliklarni yangilash strategiyalarini amaliy tavsiyalar darajasida umumlashtirish. Asosiy qism. 1. Retranslyatsiya: “qayta tarjima”dan “qayta talqin”gacha 373

Retranslyatsiyani oddiy “yangi tarjima” deb tushunish yetarli emas: u ko‘pincha matnni qayta o‘qish va qayta talqin qilishni anglatadi. Bir tomondan, avvalgi tarjimaning til me’yorlari eskiradi; ikkinchi tomondan, o‘quvchi kutgan estetik va madaniy signal o‘zgaradi; uchinchi tomondan, nashriyotlar va madaniy institutlar asarni boshqa formatda “qayta taqdim etadi” (yangi muqaddima, yangi izohlar, reklama matni, muqova konsepsiyasi va h.k.). Shu sababli retranslyatsiya ko‘pincha matnning “semantik” qatlamidan ham ko‘ra “kommunikativ” va “madaniy” qatlamiga ko‘proq ta’sir qiladi [1], [2], [3], [4]. Retranslyatsiya nazariyasida ayrim qarashlar “keyingi tarjimalar asliyatga yaqinlashadi” degan taxminni ilgari suradi. Biroq amaliy kuzatuvlar shuni ko‘rsatadiki, “yaqinlashish” doimo lingvistik emas; u ko‘pincha madaniy aniq-lik (cultural specificity) va intertekstual signallarni qayta tiklash orqali ro‘y beradi [3], [5]. Demak, intertekstuallik retranslyatsiyada markaziy indikatorlardan biriga aylanadi: tarjima “qanchalik zamonaviy” yoki “qanchalik izohli” bo‘lishidan qat’i nazar, intertekstual uzilishlar asarning qabul qilinishiga bevosita ta’sir ko‘rsatadi. 2. Intertekstual elementlar: matn ichidagi ishoradan paratekstgacha Intertekstuallik—matnning boshqa matnlar, diskurslar, tarixiy voqealar, diniymadaniy qatlamlar bilan aloqadorligi—badiiy asarda ko‘pincha yashirin yoki ko‘p qavatli bo‘ladi [6], [9]. Intertekstual birliklarni shartli ravishda quyidagi guruhlarga ajratish mumkin: • To‘g‘ridan-to‘g‘ri iqtiboslar (aniq manbadan keltirilgan parcha); • Alluziyalar (ochiq aytilmagan, lekin tanish kontekstga ishora); • Realiya va madaniy kodlar (tarixiy shaxslar, toponimlar, urf-odat, institutsional terminlar); • Janr va uslubiy ishoralar (masalan, diniy nutq, siyosiy diskurs, satira uslubi); • Paratekst: sarlavha, muqaddima, izoh, sharh, tarjimon qaydlari, muqova matni va h.k. [7], [8]. Aynan paratekst retranslyatsiyada ko‘pincha “eng tez o‘zgaradigan” qatlamdir: nashriyot marketingi, ilmiy apparatning kuchayishi, o‘quvchi uchun qulaylik, ideologik “kadr” (framing) o‘zgarishi paratekst orqali boshqariladi [8]. Shu bois intertekstual elementlarning yangi talqini faqat matn ichida emas, balki matn atrofidagi “izohlar tizimi”da ham yuzaga chiqadi. 3. Eski tarjimalarni yangilashdagi ekstralingvistik sabablar Retranslyatsiya jarayonini faqat lingvistik ehtiyojlar bilan izohlash biryoqlama bo‘ladi. Amalda “yangilash”ning asosiy drayverlari ko‘pincha ekstralingvistik (til tashqarisidagi) omillardir. 3.1. Ideologiya va senzura Tarjima—madaniy “qayta yozish” (rewriting) amaliyoti bo‘lib, u har doim muayyan ijtimoiy-ideologik maydonda amalga oshadi [10]. Ma’lum davrda ruxsat etilmagan g‘oya, diniy ishora, jinsiy ma’no yoki siyosiy terminlar avvalgi tarjimada yumshatilgan, qisqartirilgan yoki butunlay tushirib qoldirilgan bo‘lishi mumkin. Keyingi davrda esa aynan shu “yo‘qolgan” intertekstlar tiklanadi. 374

Senzura omili nafaqat matn mazmunini, balki paratekstni ham o‘zgartiradi: yangi muqaddima asarni boshqa “qadriyatlar tizimi”da talqin qiladi [10], [12]. 3.2. Nashriyot siyosati, bozor va o‘quvchi kutishlari Bozor mexanizmi ham retranslyatsiyani kuchli rag‘batlantiradi: klassik asarlarning yangi tarjimalari “yangilangan til”, “zamonaviy uslub”, “to‘liq matn”, “izohli nashr” kabi marketing signallari bilan taqdim etiladi. Bu jarayonda paratekst strategik rol o‘ynaydi: muqaddima, izohlar, hatto muqova dizayni o‘quvchining intertekstual kodlarni qanday o‘qishini belgilaydi [8], [11]. Natijada, oldingi tarjimada “oddiy” ko‘ringan ishora yangi tarjimada izoh bilan kuchaytiriladi yoki aksincha, izohlar kamaytirilib “o‘qish tezligi” ustuvor qilinadi. 3.3. Til normasi va estetik didning o‘zgarishi Til tizimi dinamik: leksika eskiradi, sintaksis soddalashadi yoki murakkablashadi, uslubiy me’yorlar o‘zgaradi. Shu sababli avvalgi tarjimadagi intertekstual birliklar bugungi o‘quvchi uchun “begona”, “sun’iy” yoki “o‘ta kitobiy” bo‘lib qolishi mumkin. Retranslyatsiya bu farqni bartaraf etishga urinadi: • alluziyani saqlab qolish uchun izohlash; • o‘quvchi tanimaydigan referentni madaniy vositachilik orqali yaqinlashtirish; • yoki asarning poetikasini saqlagan holda stilistik qayta kodlash [11], [14]. 3.4. Tarjima normasi va adabiy tizimdagi o‘rin Tarjimaning “qanday bo‘lishi kerakligi” haqidagi tasavvur ham davrga bog‘liq. Normativ qarashlar o‘zgargan sari tarjimada qaysi intertekstlarni ochiq qilish, qaysilarini yashirin qoldirish, qaysilarini izohlash kabi qarorlar ham qayta ko‘riladi [14]. Bunda adabiy tizim (politsistema) dinamikasi muhim: qabul qiluvchi adabiyotda tarjima qaysi o‘rinda turishi, qaysi janrlar va uslublar ustunligi retranslyatsiya strategiyasini belgilaydi [15]. 3.5. Madaniy maydon va tarjimon “ko‘rinishi” Tarjimon shaxsiyati va uning madaniy maydondagi pozitsiyasi ham (mukofotlar, akademik status, nashriyot bilan hamkorlik, tanqidchilik muhitidagi obro‘) retranslyatsiya natijasiga ta’sir qiladi. Ba’zan yangi tarjima aynan tarjimon “ko‘rinishini” oshirishga, uning izohlari va tanqidiy yondashuvini oldinga chiqarishga xizmat qiladi [11], [16]. Bu esa intertekstual elementlarning talqinini ham o‘zgartiradi: oldingi tarjimada “yashirin” bo‘lgan qatlamlar yangi tarjimada “tushuntirilgan” va “yo‘naltirilgan” bo‘ladi. 4. Intertekstual birliklarni retranslyatsiyada yangilash strategiyalari Maqola mavzusidan kelib chiqib, amaliy tahlil uchun quyidagi strategik modelni taklif etish mumkin: Identifikatsiya: intertekstual markerlarni aniqlash (iqtibos, alluziya, realiya, janr signal, paratekst). Diagnostika: oldingi tarjimada markerga qo‘llangan usulni topish: saqlash, moslashtirish, tushirib qoldirish, izohlash, neytrallashtirish. Ekstralingvistik xaritalash: davr omillarini belgilash (ideologiya/senzura, bozor, norma, nashriyot siyosati, o‘quvchi profili). 375

Qaror: yangi tarjimada qaysi yo‘l optimal: Eksplicatsiya (izoh yoki paratekst orqali ochish) [8]; Domestikatsiya (o‘quvchiga yaqinlashtirish) yoki foreignizatsiya (begonalikni saqlash) [11]; Paratekstual kompensatsiya: matnda saqlab bo‘lmagan ishorani muqaddima/izohda tiklash [8]; Normaga mos qayta kodlash: stilistik va poetik uyg‘unlikni yangilash [14]. Bu model retranslyatsiya jarayonini “til ichidagi” masala sifatida emas, balki madaniy-kommunikativ qayta qurish sifatida ko‘rishga yordam beradi. Xulosa va amaliy tavsiyalar. Retranslyatsiya intertekstual elementlarni “qayta tarjima” qilmaydi, balki ularni yangi madaniy vaziyatda qayta talqin qiladi. Bu jarayonda ekstralingvistik omillar—ideologik cheklovlar, senzura izlari, nashriyot va bozor logikasi, o‘quvchi kutishlari, til normasi hamda adabiy tizimdagi siljishlar—hal qiluvchi rol o‘ynaydi [10], [11], [14], [15]. Amaliy tavsiyalar: Retranslyatsiya loyihasini boshlashdan oldin “eski tarjima”dagi intertekstual uzilishlar xaritasi tuzilsin (tushirib qoldirish, noto‘g‘ri alluziya, noaniq realiya, paratekstning yo‘qligi). Intertekstual birliklar bo‘yicha minimal standart: manbani aniqlash + funksiyani belgilash + o‘quvchi uchun qabul qilinishni prognozlash. Izohlar (footnote/endnote) “ortiqcha” emas, balki intertekstni saqlashning ilmiy vositasi ekani hisobga olinsin; biroq izohlar soni o‘quvchi segmentiga mos me’yorlangan bo‘lsin [8]. Nashriyot parateksi (muqaddima, anotatsiya, muqova matni) intertekstual talqinni buzmasligi uchun tarjimon va muharrir hamkorligida ishlab chiqilsin. Yakuniy baholashda tarjima faqat “so‘z mosligi” bilan emas, intertekstual qatlamning madaniy ta’siri va reseptiv aniqligi bilan ham o‘lchansin [14] Foydalanilgan adabiyotlar ro‘yxati: 1. Antuan Berman. (1990). La retraduction comme espace de la traduction. Palimpsestes, (4), 1–7. 2. Yves Gambier. (1994). La retraduction, retour et détour. Meta, 39(3), 413–417. 3. Kaisa Koskinen, & Outi Paloposki. (2010). Multiple retranslations: Revisiting the retranslation hypothesis. Across Languages and Cultures, 11(1), 29–49. 4. Sharon Deane-Cox. (2014). Retranslation: Translation, literature and reinterpretation. Bloomsbury Academic. 5. Julia Kristeva. (1969). Séméiotikè: Recherches pour une sémanalyse. Éditions du Seuil. 6. Gérard Genette. (1987). Seuils. Éditions du Seuil. 7. Graham Allen. (2000). Intertextuality. Routledge. 8. Kathryn Batchelor. (2018). Translation and paratexts. Routledge. 9. André Lefevere. (1992). Translation, rewriting, and the manipulation of literary fame. Routledge. 10. Lawrence Venuti. (1995). The translator’s invisibility: A history of translation. Routledge. 11. Mona Baker. (2006). Translation and conflict: A narrative account. Routledge. 376

12. Carmen Billiani (Ed.). (2007). Modes of censorship and translation: National contexts and diverse media. St. Jerome Publishing. 13. Gideon Toury. (1995). Descriptive translation studies and beyond. John Benjamins. 14. Juliane House. (2015). Translation quality assessment: Past and present. Routledge. 15. Itamar Even-Zohar. (1990). Polysystem studies. Poetics Today, 11(1), 1–268. 16. Pierre Bourdieu. (1993). The field of cultural production: Essays on art and literature. Polity Press. 17. PJ Freeth. (2023). Between consciously crafted and the vastness of context: Reconsidering paratextuality. Translation Studies. 18. İC Doğan. (2023). The retranslation of intertextuality: A case-based inquiry. [Journal/Proceedings]. ARTIFICIAL INTELLIGENCE AND INNOVATIVE APPROACHES IN PERSONAL TRAINING: LINGUISTIC AND SOCIO-HUMANITARIAN ISSUES IN TECHNICAL HIGHER EDUCATION Karimov Marufjon, PhD “Foreign languages” dept., NamSTU marufjon.karimov.87@mail.ru Abstract. The article examines the linguistic and socio-humanitarian aspects of organizing education and personnel training based on artificial intelligence and innovative pedagogical approaches in technical universities. In the context of digital transformation, AI technologies significantly influence English language teaching, professional communication, and the formation of interdisciplinary competencies. The study analyzes methodological changes in language instruction, ethical and cultural challenges of AI integration, and the role of sociohumanitarian disciplines in preparing competitive specialists. The research concludes that effective AI-based education requires a balanced integration of technological innovation and humanistic values. Keywords: artificial intelligence, innovative pedagogy, English for Specific Purposes, technical university, socio-humanitarian education, digital literacy, professional competence. Аннотация. В статье рассматриваются лингвистические и социальногуманитарные аспекты организации образования и подготовки кадров на основе искусственного интеллекта и инновационных педагогических подходов в технических вузах. В контексте цифровой трансформации технологии искусственного интеллекта оказывают значительное влияние на обучение английскому языку, профессиональное общение и формирование междисциплинарных компетенций. В исследовании анализируются методологические изменения в преподавании языков, этические и культурные проблемы интеграции ИИ, а также роль социально-гуманитарных дисциплин в подготовке конкурентоспособных специалистов. В исследовании делается вывод, что эффективное образование на основе искусственного интеллекта требует сбалансированной интеграции технологических инноваций и гуманистических ценностей. Ключевые слова: искусственный интеллект, инновационная педагогика, английский язык специальных целей, технический университет, социально-гуманитарное образование, цифровая грамотность, профессиональная компетентность. Annotatsiya. Maqolada texnika oliy ta’lim muassasalarida sun’iy intellekt va innovatsion pedagogik yondashuvlar asosida ta’lim va kadrlar tayyorlashni tashkil etishning lingvistik va ijtimoiy-gumanitar jihatlari ko‘rib chiqiladi. Raqamli transformatsiya sharoitida sun’iy intellekt texnologiyalari ingliz tilini o‘qitish, kasbiy muloqot va fanlararo kompetensiyalarni shakllantirishga sezilarli ta’sir ko‘rsatadi. Tadqiqotda til o‘qitishning uslubiy o‘zgarishlari, sun’iy intellekt integratsiyasining axloqiy va madaniy muammolari, raqobatbardosh mutaxassislarni tayyorlashda ijtimoiy-gumanitar fanlarning o‘rni tahlil qilingan. Tadqiqot 377

natijalariga ko‘ra, sun’iy intellektga asoslangan samarali ta’lim texnologik innovatsiyalar va insonparvarlik qadriyatlarining muvozanatli integratsiyasini talab etadi. Kalit so‘zlar: sun’iy intellekt, innovatsion pedagogika, maxsus maqsadlar uchun ingliz tili, texnika universiteti, ijtimoiy-gumanitar ta’lim, raqamli savodxonlik, kasbiy kompetensiya. Introduction. The rapid development of artificial intelligence technologies has led to fundamental transformations in higher education systems worldwide. Technical universities, traditionally focused on engineering and applied sciences, are increasingly integrating AI-based platforms into teaching and learning processes. In this context, foreign language instruction particularly English acquires strategic significance. English serves not only as a communication tool but also as a medium for accessing scientific knowledge, participating in international research collaboration, and engaging with digital technologies. The purpose of this study is to analyze the linguistic and socio-humanitarian issues that arise in organizing AI-based personnel training in technical higher education institutions. The research is based on: 1. Analysis of contemporary pedagogical and linguistic literature; 2. Comparative analysis of traditional and AI-based instructional models; 3. Observation of English language teaching practices in technical universities; 4. Theoretical generalization of innovative educational approaches. The methodological framework combines competence-based, interdisciplinary, and humanistic approaches to education. The integration of artificial intelligence into the educational process of technical universities has led to substantial transformations in both linguistic training and the broader organization of personnel preparation. The findings indicate that AI-driven tools significantly enhance the development of academic and professional language competence. Intelligent tutoring systems, natural language processing technologies, and generative language platforms facilitate the acquisition of specialized terminology, improve academic writing skills, and support the formation of discourse competence in technical and scientific communication. Students demonstrate greater exposure to authentic professional contexts through AI-based simulations, automated feedback systems, and interactive learning environments. At the same time, the research reveals that the increasing reliance on automated language support tools may influence students’ cognitive independence. While AI provides rapid correction and content generation, it can reduce the necessity for deep linguistic analysis and critical reflection if not pedagogically regulated. Therefore, the effectiveness of AI integration depends largely on the instructor’s methodological guidance and the balance between automation and active cognitive engagement. The results further show that innovative educational organization based on AI contributes to personalized learning trajectories and adaptive assessment 378