Sun’iy Intellekt va Raqamli Iqtisodiyot

2. Hedge, T. (2000). Teaching and Learning in the Language Classroom. Oxford: OUP. 3. Lightbown, P., Spada, N. (2013). How Languages are Learned. Oxford: OUP. 4. Chapelle, C. (2017). Technology and Second Language Acquisition. Cambridge: CUP. 5. Warschauer, M. (2011). Learning in the Cloud. New York: Teachers College Press. 6. UNESCO (2021). Artificial Intelligence and Education: Guidance for Policy-makers. 7. OECD (2021). Digital Education Outlook. SUN’IY INTELLEKTNING GENERATIV MODELLARIDAN FOYDALANGAN HOLDA KADRLAR TAYYORLASHDA “MADANIYLINGVISTIK SENSIVLIK” INDEKSINING IJTIMOIY-GUMANITAR BAHOLASH MEXANIZMLARI Safarova Fotima Ne’matulla qizi,JizDPU, Filologiya fakulteti Roman-German tillari kafedrasi o‘qituvchisi e-mail: fotimanematullaevna@gmail.com Annotatsiya. Ushbu maqolada sun’iy intellektning generativ modellaridan (GPT-4o, mahalliy tilli modellar) foydalangan holda gumanitar yo‘nalishdagi kadrlar (o‘qituvchilar, psixologlar) tayyorlash jarayonida yaratiladigan o‘quv materiallarining madaniy va lingvistik moslashuvchanligini baholash uchun yangi “Madaniy-lingvistik sensivlik indeksi” (MLSindeksi) ishlab chiqilgan va sinovdan o‘tkazilgan. Indeks O‘zbekiston oliy ta’lim muassasalarida amaliyotga oid empirik tadqiqot asosida taklif etilgan bo‘lib, uning ijtimoiy-gumanitar ahamiyati baholangan. Tadqiqot natijalari shuni ko‘rsatdiki, AI generativ materiallarining MLSindeksi an’anaviy materiallarga nisbatan 22–28% past bo‘lgan (p < 0,001). Indeksni qo‘llash orqali madaniy biasni kamaytirish mexanizmlari taklif etilgan. Kalit so‘zlar: sun’iy intellekt, generativ modellar, madaniy-lingvistik sensivlik indeksi, kadrlar tayyorlash, ijtimoiy-gumanitar baholash, O‘zbekiston oliy ta’limi. Kalit so‘zlar: sun’iy intellekt, generativ modellar, madaniy-lingvistik sensivlik indeksi, kadrlar tayyorlash, ijtimoiy-gumanitar baholash, O‘zbekiston oliy ta’limi. Аннотация. В данной статье разработан и апробирован новый «Индекс культурно-лингвистической чувствительности» (MLS-индекс) для оценки культурной и лингвистической адаптивности учебных материалов, созданных с использованием генеративных моделей искусственного интеллекта (GPT-4o, модели на местных языках) в процессе подготовки кадров гуманитарного направления (преподаватели, психологи). Индекс предложен на основе эмпирического исследования, проведённого в высших учебных заведениях Узбекистана, и оценено его социально-гуманитарное значение. Результаты исследования показывают, что MLS-индекс материалов, сгенерированных ИИ, на 22–28% ниже по сравнению с традиционными материалами (p < 0,001). Предложены механизмы снижения культурного предубеждения путём применения индекса. Ключевые слова: искусственный интеллект, генеративные модели, индекс культурно-лингвистической чувствительности, подготовка кадров, социальногуманитарная оценка, высшее образование Узбекистана. Abstract. This article develops and tests a new “Cultural-Linguistic Sensitivity Index” (MLS-index) to evaluate the cultural and linguistic adaptability of educational materials generated using artificial intelligence generative models (GPT-4o, local language models) in the process of training humanitarian personnel (teachers, psychologists). The index is proposed on the basis of an empirical study conducted in higher education institutions of Uzbekistan, and its socio-humanitarian significance has been assessed. The research results show that the MLS759

index of AI-generated materials is 22–28% lower than that of traditional materials (p < 0.001). Mechanisms for reducing cultural bias through the application of the index have been proposed. Keywords: artificial intelligence, generative models, cultural-linguistic sensitivity index, staff training, socio-humanitarian assessment, higher education of Uzbekistan. Kirish. Zamonaviy raqamli transformatsiya sharoitida sun’iy intellektning generativ modellaridan foydalanish ta’lim jarayonini tubdan o‘zgartirmoqda. O‘zbekiston Respublikasida oliy ta’lim muassasalarida GPT seriyali modellar va mahalliy tilli AI vositalari kadrlar tayyorlashda faol qo‘llanilmoqda. Biroq, bu materiallarning madaniy va lingvistik jihatdan moslashuvchanligi yetarli darajada baholanmayapti. Xalqaro tadqiqotlar shuni ko‘rsatadiki, generativ AI modellarida ingliz va protestant-evropa madaniy qadriyatlariga moyillik (WEIRD bias) kuchli namoyon bo‘lib, bu boshqa madaniyatlar uchun xavf tug‘diradi. O‘zbekiston kontekstida esa milliy qadriyatlar, o‘zbek tili nuanslari va ijtimoiy normalar (oilaviy munosabatlar, hurmat, jinsiy tenglik) hisobga olinmasligi gumanitar kadrlarning kasbiy tayyorgarligiga salbiy ta’sir ko‘rsatishi mumkin. Hozirgi paytda mavjud adabiyotlarda AI yaratgan o‘quv materiallarining madaniy-lingvistik sensivligini miqdoriy baholaydigan maxsus indeks yo‘q. Ushbu tadqiqotning maqsadi - yangi “Madaniy-lingvistik sensivlik indeksi” (MLSindeksi)ni ishlab chiqish, uni O‘zbekiston oliy ta’lim muassasalari misolida sinovdan o‘tkazish va ijtimoiy-gumanitar baholash mexanizmlarini taklif etishdir. Metodlar. Tadqiqot 2025-yil sentyabr–dekabr oylarida Toshkent davlat universiteti, Toshkent davlat pedagogika universiteti, Farg‘ona davlat universiteti va boshqa 6 ta oliy ta’lim muassasasida o‘tkazildi. Namuna: 120 nafar ekspert (o‘qituvchilar va psixologlar, yoshi 28–55, 62% ayollar). Indeksni ishlab chiqish bosqichlari: 1. Delphi usuli orqali 3 bosqichli ekspert so‘rovnomasi (madaniy moslik, lingvistik aniqlik, ijtimoiy normalarga sezgirlik komponentlari aniqlandi). 2. Har bir komponent 1–5 ballik Likert shkala bo‘yicha baholanadi. 3. Indeks formulasi quyidagicha: \[ I_{MLS} = \left( 0.4 \times R_M + 0.3 \times A_L + 0.3 \times S_I \right) \times 20 \] bu yerda: - \( R_M \) - madaniy moslik balli (O‘zbekiston milliy qadriyatlari, tarixiy kontekst); - \( A_L \) - lingvistik aniqlik balli (o‘zbek tilining professional terminlari, stilistik moslik); - \( S_I \) - ijtimoiy normalarga sezgirlik balli (gender, oila, hurmat normalari). Natijada indeks 0–100 ball oralig‘ida bo‘ladi (100 - to‘liq sensivlik). Sinov bosqichi: - 50 ta AI generativ material (GPT-4o va mahalliy model orqali yaratilgan psixologiya va pedagogika darsliklari, testlar); - 50 ta an’anaviy material (davlat o‘quv dasturlari). 760

Har bir material 3 nafar mustaqil ekspert tomonidan baholandi. Statistik tahlil: SPSS 27 dasturi, t-test, ANOVA, korrelyatsiya tahlili (α = 0,05). Natijalar MLS-indeksining o‘rtacha qiymatlari quyidagicha: - AI generativ materiallar: \( M = 67,4 \), \( SD = 10,8 \); - An’anaviy materiallar: \( M = 91,7 \), \( SD = 5,2 \). Farq statistik jihatdan ishonchli (t = 14,72, p < 0,001). Komponentlar bo‘yicha eng past ko‘rsatkich - madaniy moslikda (AI uchun 58,3 ball). O‘zbek tilidagi mahalliy modellarda indeks 72,1 ballni tashkil etdi (ingliz tilidagi GPT-4o ga nisbatan yuqoriroq, lekin hali yetarli emas). Ekspertlarning 78% AI materiallarida “oilaviy hurmat” va “gender stereotiplari” bo‘yicha biasni qayd etgan. Korrelyatsiya tahlili shuni ko‘rsatdiki, MLS-indeksi yuqori bo‘lgan materiallardan foydalangan talabalar (n=320)ning kasbiy moslashuvchanligi 0,68 darajada yuqori (Pearson r = 0,68, p < 0,01). Muhokama. Natijalar xalqaro tadqiqotlar bilan mos keladi: generativ AI da madaniy bias mavjudligi va uning ta’limdagi xavflari tasdiqlangan. O‘zbekiston kontekstida bu bias gumanitar kadrlarning milliy identifikatsiyasini zaiflashtirishi mumkin. Taklif etilgan MLS-indeksi birinchi marta miqdoriy baholash vositasi sifatida taklif qilinmoqda va uni oliy ta’lim standartlariga kiritish mumkin. Kamchiliklar: indeks faqat ekspert bahosiga asoslangan (subyektivlik). Kelgusida talabalar va bitiruvchilarning real natijalarini o‘rganish zarur. O‘zbekiston Oliy ta’lim vazirligi va UNESCO tavsiyalari bilan hamkorlikda indeksni milliy platformaga integratsiya qilish mumkin. Xulosa. Taklif etilgan “Madaniy-lingvistik sensivlik indeksi” sun’iy intellekt asosidagi kadrlar tayyorlashning ijtimoiy-gumanitar xavfsizligini ta’minlashning samarali mexanizmi hisoblanadi. Indeksni qo‘llash orqali AI materiallarining sifatini 25–30% oshirish mumkin. Natijalar O‘zbekiston oliy ta’lim tizimida raqamli transformatsiyani insonparvar va madaniyatga moslashtirishga xizmat qiladi. Adabiyotlar: 1. Tao, Y. et al. (2024). Cultural bias and cultural alignment of large language models. PMC. 2. UNESCO. (2023). Ta’lim va tadqiqotda generativ sun’iy intellektdan foydalanish qo‘llanmasi (o‘zbek tilida). 3. Shakib Kotamjani, S. (2023). Lecturers’ perceptions of using Artificial Intelligence in Tertiary Education in Uzbekistan. ACM. 4. Nigmatova, N. (2026). Improving the theoretical and methodological foundations for integrating generative artificial intelligence technologies into English language teaching in higher education. 5. O‘zbekiston Respublikasi Oliy ta’lim, fan va innovatsiyalar vazirligi. (2024). Raqamli ta’lim strategiyasi. 761

LOGISTIKA TERMINLARINING LEKSIKOGRAFIK XUSUSIYATLARI VA SUN’IY INTELLEKT Xo‘jaqulova Shohista G‘ofur qizi, TIIU magistranti Annotatsiya. Mazkur maqolada ingliz va o‘zbek tillaridagi logistika terminlarining leksikografik xususiyatlari qiyosiy tahlil qilingan. Tadqiqotda logistika terminlarining tuzilishi, ma’nosi va qo‘llanish doirasi o‘rganilgan. Ingliz tilidan o‘zbek tiliga kirib kelgan logistika atamalarining o‘zlashtirilish jarayoni, ularning semantik rivoji hamda xalqaro aloqalar ta’siri yoritilgan. Shuningdek, har ikki tildagi lug‘atlardagi ifodalanish uslubi va ishlatilishdagi o‘xshash hamda farqli jihatlar tahlil etilgan. Tadqiqot natijalari ikki tilli leksikografiyani rivojlantirish va o‘zbek logistika terminologiyasini boyitishga xizmat qiladi. Kalit so‘zlar: leksikografiya, logistika, terminologiya, qiyosiy tahlil, ingliz tili, o‘zbek tili, o‘zlashma, semantika. Аннотация. В данной статье проводится сравнительный анализ лексикографических особенностей логистических терминов в английском и узбекском языках. В исследовании изучены структура, значение и сфера применения логистических терминов. Освещены процесс заимствования логистических терминов из английского языка в узбекский, их семантическое развитие и влияние международных отношений. Также проанализированы сходства и различия в стиле выражения и использования в словарях обоих языков. Результаты исследования послужат развитию двуязычной лексикографии и обогащению узбекской логистической терминологии. Ключевые слова: лексикография, логистика, терминология, сравнительный анализ, английский язык, узбекский язык, заимствования, семантика. Annotation. This article presents a comparative analysis of the lexicographical properties of logistics terms in English and Uzbek languages. The study examines the structural, semantic, and functional features of logistics terminology in both languages. It explores how English logistics terms are borrowed and adapted into Uzbek, emphasizing the influence of globalization and international communication. The research also discusses the similarities and differences in dictionary representation and usage of logistics terms. The results contribute to the development of bilingual lexicography and the enrichment of Uzbek logistics terminology. Keywords: lexicography, logistics, terminology, comparative analysis, English, Uzbek, borrowing, Kirish. Zamonaviy global iqtisodiyotda logistika savdo, transport va ta'minot zanjiri boshqaruvini rivojlantirishda muhim rol o‘ynaydi. Logistika tez rivojlanayotgan sohaga aylanganligi sababli, uning terminologiyasi ham rivojlandi. Fan va biznesning yetakchi tili bo‘lgan ingliz tili logistika bilan bog‘liq boy terminologik tizimga ega, o‘zlashtirb va moslashtirib kelmoqda. (Crystal, D. (2003). Logistika atamalarining leksikografik tahlili professional lug‘at qanday shakllantirilganligi, standartlashtirilganligi va ikkala tilda ham qo‘llanilishini tushunishga yordam beradi. Shuning uchun, logistika terminologiyasining semantik ekvivalentligi va madaniy moslashuvini aniqlash uchun qiyosiy yondashuv zarur. Tadqiqot davomida logistika terminlarining semantik aniqligi, variantliligi, sinonimiyasi va tarjima muammolari leksikografik nuqtai nazardan ko‘rib chiqiladi. Ayrim terminlarning bir nechta muqobil shakllarda qo‘llanishi terminologik noaniqlikka olib kelishi aniqlanadi. Shuningdek, umumiy lug‘atlar va maxsus terminologik lug‘atlar o‘rtasidagi 762

farqlar, terminlarning izohlanish darajasi va kontekstual qo‘llanilishi tahlil qilinadi.Logistika terminlarining asosiy leksikografik xususiyatlaridan biri ularning semantik aniqligi va monosemantik xarakteridir. Ideal holatda termin bitta aniq tushunchani ifodalashi lozim. Biroq amaliyotda ayrim logistika terminlari ko‘p ma’nolilik xususiyatiga ega bo‘lib, bu holat terminologik noaniqlikka sabab bo‘ladi. Masalan, ayrim terminlar umumtil birliklari bilan shaklan bir xil bo‘lib, professional kontekstdagina maxsus ma’noga ega bo‘ladi. Bu holat lug‘atlarda termin va umumiy so‘z ma’nolarini ajratib ko‘rsatishni talab qiladi. Aks holda, foydalanuvchi uchun tushunmovchilik yuzaga kelishi mumkin. Usullar. Ushbu tadqiqot qiyosiy, semantik va leksikografik tahlil usullaridan foydalanadi. Qiyosiy usul ingliz va o‘zbek logistika atamalari o‘rtasidagi leksik o‘xshashliklar va farqlarni aniqlashga yordam beradi. Semantik yondashuv ma'no maydonlari va atama ekvivalentligiga qaratilgan, leksikografik tahlil esa logistika atamalarining ikki tilli va ixtisoslashgan lug‘atlarda qanday taqdim etilishini o‘rganadi. (Atkins, B. T. S., & Rundell, M. (2008). Logistika terminlarining asosiy leksikografik xususiyatlaridan biri ularning semantik aniqligi va monosemantik xarakteridir. Ideal holatda termin bitta aniq tushunchani ifodalashi lozim. Biroq amaliyotda ayrim logistika terminlari ko‘p ma’nolilik xususiyatiga ega bo‘lib, bu holat terminologik noaniqlikka sabab bo‘ladi. Masalan, ayrim terminlar umumtil birliklari bilan shaklan bir xil bo‘lib, professional kontekstdagina maxsus ma’noga ega bo‘ladi. Bu holat lug‘atlarda termin va umumiy so‘z ma’nolarini ajratib ko‘rsatishni talab qiladi. Aks holda, foydalanuvchi uchun tushunmovchilik yuzaga kelishi mumkin. Sinonimiya va variantlilik muammosi Logistika terminologiyasida sinonimiya va variantlilik keng tarqalgan hodisa hisoblanadi. Bir tushuncha uchun bir nechta termin shakllarining qo‘llanishi leksikografik muammolarni yuzaga keltiradi. Masalan, ayrim terminlar tarjima yo‘li bilan bir nechta muqobil shaklda berilishi mumkin. Bu esa yagona me’yorning yo‘qligidan dalolat beradi. Leksikografik nuqtai nazardan sinonim terminlar orasida ustuvor variantni tanlash, qolganlarini esa izoh yoki variant sifatida ko‘rsatish muhim hisoblanadi. Aks holda, terminologik tizimda tartibsizlik yuzaga keladi va ilmiy hamda amaliy muloqotda aniqlik buziladi. Tahlil va muhokama. Tahlil shuni ko‘rsatadiki, o‘zbek tilidagi ko‘plab logistika atamalari to‘g‘ridan-to‘g‘ri ingliz tilidan olingan, masalan: • logistika – logistika, • omborxona – omborxona, • transport – tashish, • ta'minot zanjiri – ta'minot zanjiri. Jahon tilshunosligida logistika sohasiga oid tarixiy lug‘atlar, asosan: 1. Harbiy logistika manbalari 763

2. Dengizchilik va port tizimlari lug‘atlari 3. Savdo va iqtisodiy tarixiy lug‘atlar 4. Ingliz tilidagi OED va Merriam tizimlari 5. Tarixiy ensiklopediyalar Logistika sohasiga oid eng qadimgi harbiy lug‘atlardan biri. Lug‘atda quyidagi sohalar qamrab olingan: • harbiy ta’minot (supply) • qo‘shinlarni joylashtirish (deployment) • ombor tizimi (storage) • transport birikmalari (convoy, fleet, logistics corps) Biroq, hamma ham o‘zlashtirilgan emas atamalar bir xil ma'nolarni saqlab qoladi. Ba'zilari o‘zbek tiliga moslashtirilganda semantik torayishga yoki kengayishga uchraydi. (Karimov, A. (2020). Ingliz logistika terminologiyasi qo‘shma asosga ega bo‘lishga moyil (masalan, yuk tashish, yuklarni qayta ishlash), o‘zbek ekvivalentlari esa ko‘pincha ma'noni yetkazish uchun sintaktik konstruksiyalardan foydalanadi (yuk tashlash xizmati, ombor boshqaruvi). Leksikografik taqqoslash shuni ko‘rsatadiki, Oksford Biznes va Menejment Dictionary va Logistics and Supply Chain Management Dictionary kabi inglizcha ixtisoslashgan lug‘atlar batafsil terminologik ta'riflarni beradi, o‘zbekcha tushuntirish va ikki tilli lug‘atlar esa hali ham o‘zlarining terminologik asoslarini ishlab chiqmoqda. (Waters, D. (2003). Xulosa. Logistika terminlarini qiyosiy o‘rganish shuni ko‘rsatadiki, ingliz tili yanada aniqroq va standartlashtirilgan terminologik tizimga ega, ozbek logistika lug‘ati esa faol rivojlanish va moslashish jarayonida. Leksikografik tahlil semantik aniqlik va terminologik izchillikni ta’minlaydigan ixtisoslashgan o‘zbekingliz logistika lug‘atlarini yaratish zarurligini ta'kidlaydi. Xulosa qilib aytganda, o‘zbek va ingliz tilining logistika terminlarining xususiyatlari va ularning leksikografik qiyosiy tahlili va barqarorlikni ta’minlash maqsadida sinonimiya va variantlikni kamaytirish hamda ikki tilli maxsus logistika lug‘atlarni yaratish uchun muhim nazariy va amaliy asos bo‘lib xizmat qiladi. Foydalanilgan adabiyotlar ro‘yxati 1. Crystal, D. (2008). A Dictionary of Linguistics and Phonetics. Oxford: Blackwell Publishing. 2. Cabré, M. T. (1999). Terminology: Theory, Methods and Applications. Amsterdam: John Benjamins Publishing Company. 3. Sager, J. C. (1990). A Practical Course in Terminology Processing. Amsterdam: John Benjamins. 4. ISO 704:2009. Terminology Work — Principles and Methods. Geneva: International Organization for Standardization. 5. ISO 1087-1:2019. Terminology Work and Terminology Science — Vocabulary. Geneva: International Organization for Standardization. 6. O‘zbek tilining izohli lug‘ati. (2020). Toshkent: O‘zbekiston Milliy Ensiklopediyasi davlat ilmiy nashriyoti. 7. Rahmatullayev, Sh. (2006). Hozirgi o‘zbek adabiy tili. Toshkent: Universitet. 764

8. Yo‘ldoshev, B. (2018). Terminologiya va tarjima masalalari. Toshkent: Fan va texnologiya. 9. Karimov, A. (2021). Logistika terminlarining shakllanishi va tarjima muammolari. Til va adabiyot ta’limi, 4, 45–52. O‘ZBEK-QIRG‘IZ ELEKTRON LUG‘AT ISHLAB CHIQISHDA MULTIMODAL TEXNOLOGIYALARNING ROLI Abidova Shaxnoza Baxodirovna, TATU, v.b. dotsent, email: shaxnoza23@mail.ru Qurbonbekova Sabira Baxit qizi, TATU 1-kurs magistranti, sabiraqurbonbekova2224@gmail.com Musayeva Dilnoza Baxrom qizi, Belarus davlat informatika va radioelektronika universiteti magistranti Annotatsiya. Maqolada o‘zbek-qirg‘iz elektron lug‘atlarida OCR texnologiyasining rasmli, matnli va audio tarjimadagi roli tahlil qilinadi. Multimodal yondashuv so‘zning ma’nosi, talaffuzi va vizual jihatlarini tez va aniq o‘rganish imkonini beradi. Kalit so‘zlar: multimodal texnologiyalar, affiksatsiya, kompozitsiya, nutqni matnga aylantirish, agglunativ, OCR Аннотация. В статье рассматривается роль технологии OCR в узбекскокыргызских электронных словарях для текстового, аудио и визуального перевода. Мультимодальный подход позволяет пользователям быстро и точно изучать значение слова, его произношение и визуальные особенности. Ключевые слова: мультимодальные технологии, аффиксация, композиция, преобразование речи в текст, агглюнатив, OCR Abstract. The article analyzes the role of OCR technology in Uzbek-Kyrgyz electronic dictionaries for text, audio, and image translation. The multimodal approach enables users to quickly and accurately learn word meaning, pronunciation, and visual aspects. Kalit so‘zlar: multimodal texnologiyalar, affiksatsiya, kompozitsiya, Speech-toText,agglunativ, OCR Globallashuv va raqamli transformatsiya davrida qardosh turkiy tillar o‘rtasidagi muloqatni jadallashtirish muhim ahamiyat kasb etmoqda. O‘zbek va qirg‘iz tillari turkiy tillar oilasiga mansub bo‘lib, ko‘plab leksik va grammatik xususiyatlar bilan o‘zaro yaqinlikka ega. Lekin ayrim so‘zlarning talaffuzi, yozilishi va ma’nosi turlicha bo‘lishi tufayli o‘zaro tushunishni qiyinlashtiradi. Raqamli davrda an’anaviy qog‘oz lug‘atlar o‘rnini elektron va interaktiv lug‘atlar egallamoqda. Shu sababli o‘zbek-qirg‘iz til juftligi uchun samarali elektron lug‘at yaratish zaruriyati ortib bormoqda. Zamonaviy axborot texnalogiyalari ya’ni multimodal yondashuvlar, til o‘rganish va tarjima jarayonini yangi bosqichga olib chiqmoqda. Multimodal texnologiyalar bir nechta turdagi ma’lumotlarni birlashtirgan holda ishlovchi texnologiyalarni anglatadi. Bular quyidagilarni o‘z ichiga oladi: ● Matn – asosiy lug‘at ma’lumotlari. ● Audio – talaffuzni tinglash imkoniyati. ● Rasm – so‘zga vizual kontekst qo‘shish. ● Imo-ishora – harakat yoki jarayonlarni ko‘rsatish. 765

Bugungi kunda matn, audio, rasm orqali tarjima qiluvchi elektron lug‘atlar ko‘payib bormoqda. Ularning ba’zilarida o‘zbek va qirg‘iz tillari matn ko‘rinishda tarjima qilish imkoniyati mavjud lekin audio va rasm tarjima qilish imkoniyati mavjud emas quyidagi 1-jadvalda solishtirma tahlil keltirilgan. 1-jadval O‘zbek qirg‘iz lug‘atlarida multimodal tarjima imkoniyatlari tahlili № Dastur nomi Matnni tarjima Audio tarjima (o‘zbek–qirg‘iz) Audio tarjima (qirg‘iz–o‘zbek) Rasm tarjima 1 Google translate + + - + 2 Yandex translate + - - + 3 Glosbe + - - - 4 Tilmoch - - - - 5 Lingvo - - - - 6 DeepL + - - - Bu tahlildan ko‘rinib turibdiki hozirgi vaqtda o‘zbek va qirg‘iz tili o‘rtasidagi tarjimani amalga oshirishda to‘liq audio va rasm tarjima qilishni qo‘llab quvvatlamaydi. Bundan kelib chiqadiki hozirgi kunda bunday lug‘atni yaratish masalasi mavjud va biz bu masalani o‘z oldimizga maqsad qilib belgilab olindi. O‘zbek va qirg‘iz tillari turkiy tillar oilasiga mansub bo‘lib agglunativ tuzilishga ega, ya’ni so‘zlar qo‘shimchalar orqali yasaladi va har bir qo‘shimcha alohida grammatik ma’no bildiradi. O‘zbek va qirg‘iz tillarida so‘z yasalishining asosiy usullari: 1. Affiksatsiya usuli (Morfologik usul). 2. Kompozitsiya usuli(kompozitsiya usul). Affiksatsiya usulida o‘zak va negizga so‘z yasovchi qo‘shimchalar qo‘shish orqali yangi so‘z yasaladi. Bunda so‘z yasovchi qo‘shimchalar o‘zak: Bil – bilim, yoz – yozuvchi , жаз – жазуучу ,бил – билим. Sifat so‘z turkumiga mansub bir nechta so‘zlarning yasalishiga old qo‘shimchalar qo‘shiladi. Misol uchun badavlat, serdavlat kabi. Shunday so‘zlar borki ular ko‘p ma’noli so‘zlardir ularni talaffuz qilish va yozish bir xil lekin ishlatishda har xil ma’noni beradi. Misol: ipni uzdi – жипти үздү, munosabatni uzdi– мамилени үздү bu yerda “uz ”so‘zi ko‘p ma’noli so‘zdir. Lug‘at yaratishda ko‘p ma’noli so‘zlar va omonimlikni hisobga olish lozim. Sababi bir so‘z har xil ma’noda keladi va bu til o‘rganuvchilar uchun tushunarsiz bo‘lishi mumkin. Shu sababli ham elektron lug‘at ichida ko‘p ma’noli va omonim so‘zlarning ma’nosi, ma’noga mos holda misol berib ketish maqsadga muvofiqdir. Audio tarjima qilish jarayonida audioni matn ko‘rinishga o‘tkazish undan so‘ng esa so‘zni lug‘at ichida qidiruv amalga oshiriladi. 766

1-sxema. Audioni tarjima qilish sxemasi 1-rasmda ko‘rsatilgan ketma-ketlik audio ma’lumotni matnga aylantirish va tarjima qilish jarayonini ifodalaydi. Ovoz bu bosqichda foydalanuvchining nutqini qabul qilishdan iborat. Nutq anolog signal shaklida mikrafon orqali olinadi va raqamli ko‘rinishga o‘tkaziladi. Raqamlashtirish jarayonida anolog signal diskret qiymatlarga aylantiriladi [1]. Speech-to-Text algoritmi nutq signalini matn ko‘rinishini yaratadi. Ushbu bosqich: ● Signalni oldindan ishlov berish. Shovqinni kamaytirish, segmentlarga ajratish va windowing(oynalash). ● Akustik xususiyatlarni ajratish. Audioni raqamli ko‘rinishini olish. ● Akustik va til modeli yordamida dekodlash. Neyron tarmoqlar (LSTM) yoki an’anaviy usulda (Hidden markov model) yordamida eng ehtimolli so‘zlar ketma-ketligi aniqlanadi [4][5]. Matn bosqichida olingan matn elektron lug‘at bilan ishlash uchun tayyorlanadi. Bu yerda morfologik tahlil va stemming jarayonlari amalga oshiriladi. Matn lug‘at bazasida qidiriladi. Lug‘at ma’lumotlar bazasi har bir o‘zbek so‘zi uchun qirg‘izcha ekvivalentini yoki aksincha qirg‘izcha so‘z uchun o‘zbekcha ekvivalentini va qo‘shimcha ma’lumotlarni saqlaydi. Bu bosqichda: ● So‘zlar oddiy shaklda qidiriladi. ● Omonimlar va ko‘p ma’noli so‘zlar uchun kontekstual izohlar taqdim etiladi. Tarjima foydalanuvchiga matn shaklida taqdim etiladi. Audio chiqarish uchun Text-to-Speech texnalogiyasi qo‘llaniladi. Multimodal lug‘atning muhim tarkibiy qismlaridan biri rasmdagi matnlarni aniqlash va tarjima qilishdir. Bu jarayon Optical Belgilarni Anilqlash(OCR) optik belgilarni tanish texnalogiyasiga asoslanadi. Yuqoridagi 1-sxemada OCR tizimining ishlash bosqichlari ko‘rsatilgan [7]. OCR texnologiyasi rasmdagi matnni avtomatik ravishda aniqlash va uni tahrir qilinadigan matn ko‘rinishiga o‘tkazishga xizmat qiladi. Multimodal elektron lug‘at tizimida ushbu modul foydalanuvchi tomonidan yuklangan rasm yoki kamera orqali olingan tasvirdagi so‘zlarni aniqlash va tarjima qilish imkonini beradi. OCR tizimining ishlash bosqichlarini ko‘rib chiqamiz. Dastlabki bosqichda tasvirni oldindan qayta ishlash (pre-processing). Bu bosqichda rasm sifatini yaxshilash uchun bir nechta operatsiyalar amalga oshiriladi: rasm kulrang ko‘rinishga o‘tkaziladi, shovqinlar kamaytiriladi, kontrast oshiriladi va binarizatsiya jarayoni bajariladi. Binarizatsiya natijasida tasvir qora va oq rangli ko‘rinishga o‘tadi, matn va fon aniq ajraladi. Shuningdek, rasmning qiyshayganligi aniqlanib, to‘g‘rilanadi [7][8]. 767

Kirishdagi rasm Dastlabki ishlov berish Belgilarni lokalizatsiya qilish Belgilarni segmentatsiya qilish Yakuniy tahlil 2-sxema. Rasm uchun Optik Belgilarni Aniqlash (OCR) tizimining to‘rt bosqichi Segmentatsiya bosqichida Tasvirdagi matn tarkibiy qismlarga bo‘linadi: matn bloklari, satrlar, so‘zlar va oxirida alohida belgilar ajratiladi. Segmentatsiya ayniqsa agglutinativ tillar uchun muhimdir, chunki o‘zbek va qirg‘iz tillarida qo‘shimchalar orqali hosil bo‘lgan uzun so‘zlar uchraydi. Belgilarni to‘g‘ri ajratish OCR aniqligini sezilarli darajada oshiradi [9]. Belgilarni tanib olish bosqichida ajratilgan har bir belgi raqamli ko‘rinishga o‘tkazilinadi. Belgining konturi, chiziqlari, burchaklari va shakliy xususiyatlari aniqlanadi, so‘ngra ushbu xususiyatlar asosida klassifikatsiya amalga oshiriladi. Zamonaviy OCR tizimlarida konvolyutsion neyron tarmoqlar (CNN) yoki uzoq muddatli xotiraga ega tarmoqlar (LSTM) qo‘llaniladi [7][10]. Yakuniy tahlil bosqichida tanib olingan matn til modeli yordamida tekshiriladi. Imloviy xatolar tuzatiladi, noto‘g‘ri aniqlangan belgilar kontekst asosida qayta baholanadi. Aniqlangan so‘z lug‘at bazasida qidirilib, mos tarjima chiqariladi [11]. Multimodal texnologiyalar o‘zbek-qirg‘iz elektron lug‘atining samaradorligini oshirishda muhim ahamiyatga ega. Matn, audio va rasm komponentlarini birlashtirish orqali foydalanuvchiga nafaqat so‘zning ma’nosini tushunish, balki uning talaffuzi, kontekstdagi ishlatilishi va vizual jihatlarini ham tez va aniq o‘rganish imkoniyati beriladi. Bu esa qardosh turkiy tillar o‘rtasidagi muloqatni sezilarli darajada soddalashtiradi va tezlashtiradi. Matn, audio va rasm komponentlarini birlashtirish orqali foydalanuvchiga nafaqat so‘zning ma’nosini tushunish, balki uning talaffuzi, kontekstdagi ishlatilishi va vizual jihatlarini ham tez va aniq o‘rganish imkoniyati beriladi. Bu esa qardosh turkiy tillar o‘rtasidagi muloqatni sezilarli darajada soddalashtiradi va tezlashtiradi. 768

Foydalanilgan adabiyotlar 1. Hamrayeva Munira Rustamovna(2023), O‘zbek tilida so‘z yasalishining morfologik va sintaktik xususiyatlari, Pedagoglar.uz jurnali 32-34 bet. 2. Абдувалиев Ибраим ,Кыргыз тилинин морфологиясы Бишкек 2008. 3. O‘Shaughnessy, D. (2000). Speech Communications: Human and Machine. IEEE Press. 4. Huang, X., Acero, A., Hon, H.-W. (2001). Spoken Language Processing. Prentice Hall. 5. Jurafsky, D., Martin, J. (2009). Speech and Language Processing. Pearson Education. 6. Nazirova E.Sh., Abidova Sh.B. Mathematical model and algorithm for calculating complex words int Uzbek Language. Solid State Technology. Valume: 64, Issue 2. 2021 y. pp. 4347-4359. 7. Yu, K., Kim, M., & Choi, J. R. (2023). Memory-tree based design of optical character recognition in FPGA. Electronics, 12(3), 754. 8. Smith, R. (2007). An Overview of the Tesseract OCR Engine. Proceedings of the Ninth International Conference on Document Analysis and Recognition, 629–633. 9. Casey, R. & Lecolinet, E. (1996). A survey of methods and strategies in character segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(7), 690– 706. 10. LeCun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature, 521, 436–444. 11. Breuel, T. (2008). High performance OCR for printed English and other languages. Pattern Recognition, 41(10), 2645–2656. 12. Wang, T., Chen, X., & Huang, W. (2020). Multimodal OCR and translation systems: A review. ACM Computing Surveys, 53(6), 1–35. 13. Akhmedov, F., & Rakhimov, S. (2022). Development of multilingual electronic dictionaries: Theory and practice. Tashkent. 14. Li, H., Xu, Z., & Wang, Q. (2021). Advances in multimodal translation technologies. Journal of Artificial Intelligence Research, 70, 123–150. 15. Chen, J., & Liu, M. (2019). Integrating text, audio, and visual modalities in language learning applications. Computers & Education, 142, 103639. ARTIFICIAL INTELLIGENCE IN EDUCATION: HELP OR HARM? Akhmedova Charos Chingizbek qizi, bachelor 1st-year student, E-mail: behruzbekfarhodov0@gmail.com Uzakova Nilufar Anvarovna, bachelor 1st-year student E-mail: uzakovanlfr@gmail.com Xolmo‘minov Ibrohim Xo‘jayor o‘gli, bachelor 1st-year student E-mail: Ibrohimholmuminov0@gmail.com Supervisor: Munirakhon Mukhitdinova Ravshanovna, PhD, Associate Professor University of World Economy and Diplomacy, E-mail: mmuxitdinova@uwed.uz Abstract. This article examines the dual role of artificial intelligence (AI) in modern education through both a literature review and primary survey data collected from 15 students aged 15–25. While AI tools such as ChatGPT offer personalized learning, time efficiency, and writing support, they simultaneously raise concerns about academic dishonesty, critical thinking atrophy, and overdependence on technology. Survey results indicate that 100% of respondents use AI tools, yet opinions remain divided: 47% view AI as both helpful and harmful, while 53% acknowledge concerns about student laziness and loss of independent thinking. The paper proposes a balanced framework for ethical AI integration in educational settings. Keywords: artificial intelligence, education technology, personalized learning, academic integrity, critical thinking, ChatGPT, survey 769

Аннотация. В данной статье рассматривается двойственная роль искусственного интеллекта (ИИ) в современном образовании на основе обзора литературы и первичных данных опроса, собранных у 15 студентов в возрасте от 15 до 25 лет. Хотя такие инструменты ИИ, как ChatGPT, предлагают персонализированное обучение, эффективность времени и поддержку в написании работ, они одновременно вызывают опасения по поводу академической нечестности, атрофии критического мышления и чрезмерной зависимости от технологий. Результаты опроса показывают, что 100% респондентов используют инструменты ИИ, но мнения остаются разделенными: 47% считают ИИ полезным и вредным одновременно, в то время как 53% признают опасения по поводу лени студентов и утраты независимого мышления. В статье предлагается сбалансированная модель для этичной интеграции ИИ в образовательные условия. Ключевые слова: искусственный интеллект, образовательные технологии, персонализированное обучение, академическая честность, критическое мышление, ChatGPT, опрос. Annotatsiya. Ushbu maqola zamonaviy ta'limda sun'iy intellektning (AI) ikki tomonlama rolini adabiyotlarni ko‘rib chiqish va 15-25 yoshdagi 15 o‘quvchidan to‘plangan birlamchi so‘rov ma'lumotlari orqali ko‘rib chiqadi. ChatGPT kabi sun'iy intellekt vositalari shaxsiylashtirilgan o‘rganish, vaqt samaradorligi va yozishni qo‘llab-quvvatlashni taklif qilsa-da, ular bir vaqtning o‘zida akademik insofsizlik, tanqidiy fikrlashning atrofiyasi va texnologiyaga haddan tashqari qaramlik haqida tashvish uyg‘otadi. So‘rov natijalari shuni ko‘rsatadiki, respondentlarning 100 foizi sun’iy intellekt vositalaridan foydalanadi, biroq fikrlar ikkiga bo‘linmayapti: 47 foizi sun’iy intellektni ham foydali, ham foydali deb biladi, 53 foizi esa o‘quvchilarning dangasaligi va mustaqil fikrlash qobiliyatini yo‘qotishi haqidagi xavotirlarni tan oladi. Hujjat ta'lim muassasalarida AIning axloqiy integratsiyasi uchun muvozanatli asosni taklif qiladi. Kalit so‘zlar: sun'iy intellekt, ta'lim texnologiyasi, shaxsiylashtirilgan ta'lim, akademik yaxlitlik, tanqidiy fikrlash, ChatGPT, so‘rov. Introduction. Artificial intelligence has rapidly transformed from a futuristic concept into an everyday reality in classrooms worldwide. Since the launch of ChatGPT in November 2022, the integration of AI tools into educational contexts has expanded at an unprecedented pace, prompting intense debate among educators, students, and policymakers [10]. The central question has become whether AI serves as a genuinely helpful pedagogical tool or a harmful shortcut that undermines the development of genuine knowledge and skills. The relevance of this topic is underscored by recent empirical data. Studies conducted across diverse educational systems show that approximately 54% of students and 53% of teachers now use AI for school-related tasks [3]. This widespread adoption has occurred so rapidly that institutional policies, ethical guidelines, and teacher training programs have struggled to keep pace. Uzbekistan, as part of its national digital transformation agenda outlined in the 'Digital Uzbekistan - 2030' strategy [Presidential Decree PF-6079], is not exempt from these global developments; local students and educators are increasingly encountering and utilizing AI tools in their academic lives. The purpose of this article is twofold: first, to synthesize existing scholarly literature on the benefits and risks of AI in education; and second, to present and analyze original survey data gathered from 15 university and school students in Uzbekistan. By combining international research with local primary data, this 770

paper aims to contribute a contextually grounded perspective to the broader global discourse. Methodology. To supplement the literature review, the authors designed and distributed a structured questionnaire among students aged 15-25 studying at university and secondary school levels in Uzbekistan. The survey was administered electronically between March 1-2, 2026, yielding 15 valid responses. Participants were asked about their frequency of AI use, perceived benefits and risks, attitudes toward AI in education, and overall recommendations. The questionnaire included both closed-ended Likert-scale items and openended questions to capture nuanced opinions. Demographic data included age group and academic level. Responses were analyzed using descriptive statistics to identify dominant patterns and trends. The survey was exploratory and qualitative in orientation, intended to provide illustrative evidence rather than statistically generalizable findings. The helpful side of ai in education. Artificial intelligence offers several significant advantages for teaching and learning. One of the most widely discussed applications is personalized learning. AI-powered systems can adapt to each student's individual pace, providing targeted practice in weak areas while advancing rapidly through already-mastered material [7]. This degree of individualization was previously unachievable in traditional classroom settings with large student-to-teacher ratios. Teachers also benefit substantially from AI assistance. Research indicates that educators spend less than half of their working time engaged in direct instruction, with the remainder consumed by administrative duties such as grading, lesson planning, and parental communication [5]. AI tools can automate many of these tasks - drafting rubrics, generating quiz items, summarizing student performance - thereby freeing educators to focus on the relational and mentoring aspects of their role [8]. Cross-national research from several European countries further illustrates AI's pedagogical potential. In Ireland, teachers employed AI-powered tools to motivate disengaged learners; in Luxembourg and Finland, educators used AI for personalized feedback and adaptive support [4]. Student populations globally report using AI to simplify complex topics, engage in self-directed practice, and prepare for examinations [6]. For students with learning differences and disabilities, AI carries particular promise. Learners with attention deficit disorders may receive structured organizational support, while those with social communication challenges can rehearse interactions through AI-driven simulations [5]. Such applications have the potential to make education significantly more inclusive. Our survey corroborates these findings. Among 15 respondents, 60% identified 'explaining difficult topics' as a key benefit of AI use, 53% highlighted time savings, 47% cited homework assistance, and 40% noted improvements in their writing quality. One respondent offered a nuanced view representative of 771

many: «AI in education is a great tool for personalizing learning and automating routine tasks. It makes education more accessible and efficient. However, it should assist, not replace teachers - human interaction and mentorship remain essential» The harmful side of ai in education. Despite these evident benefits, serious concerns regarding AI in education demand careful attention. Academic integrity stands as the most immediate and measurable challenge. Educators across educational levels increasingly report difficulty distinguishing authentic student work from AI-generated content [6]. A large-scale analysis of over 200 million student papers found that approximately 11% contained at least 20% AI-generated text [8]. Our survey reflects similar anxieties: 53% of respondents cited increased cheating as a primary concern. Perhaps more structurally significant is AI's potential erosion of critical thinking. Surveys in the United States reveal that 55% of high school students and 61% of parents believe that greater AI use will negatively affect students' capacity for independent reasoning [3]. When AI provides immediate and polished answers, students may never develop the cognitive persistence required to work through intellectually demanding problems. As a UNESCO analysis cautions, when AI tools simply provide answers rather than scaffolding reasoning processes, students may ultimately learn less - not more [10]. This concern was the most frequently cited in our survey, with 67% of respondents worried that AI makes students lazy and 60% concerned about loss of critical thinking skills. One respondent from the 15–18 age group observed that AI 'can be a helper but it cannot become like your professor,' implicitly acknowledging the irreplaceable role of human mentorship. Another respondent expressed concern more forcefully: «Not good for pupils and students. I strongly disagree with the statement of using AI». Privacy and data security represent a third category of concern. AI systems gather extensive data about student performance, engagement patterns, and even emotional responses [9]. Institutional and regulatory frameworks governing who owns this data, how it is secured, and whether it may be repurposed in harmful ways remain largely underdeveloped globally and particularly in Central Asian educational contexts. Finally, the digital divide threatens to exacerbate existing educational inequalities. Students lacking reliable internet connectivity or access to updated hardware cannot access AI tools, potentially widening achievement gaps between privileged and disadvantaged learners [7]. An instrument designed to democratize education may paradoxically reinforce stratification if access remains unequal. Survey results: key findings. Table 1 below summarizes the principal findings from the student survey conducted for this study. The data reveal a predominantly ambivalent posture toward AI in education: while nearly all respondents use AI tools and recognize tangible benefits, a substantial majority simultaneously express concerns about its impact on learning habits and intellectual independence. 772

Table 1 Summary of Survey Findings (n=15) Survey Statement / Finding Frequency / % Use AI tools for education 100% ChatGPT is the most used tool 80% Use AI every day or several times/week 73% AI helps explain difficult topics 60% AI saves time 53% AI helps with homework 47% AI improves writing 40% Concern: students become lazy 67% Concern: loss of critical thinking 60% Concern: cheating increases 53% Concern: overdependence on technology 40% AI is 'both equally' good and harmful 47% AI is 'mostly good' 20% AI is 'mostly harmful' 13% Would recommend AI use in education 53% Respondents (n=15) 15/15 12/15 11/15 9/15 8/15 7/15 6/15 10/15 9/15 8/15 6/15 7/15 3/15 2/15 8/15 Source: Authors' primary survey data, March 2026. The most striking finding is the universality of AI adoption: every respondent reported using at least one AI tool, with ChatGPT dominant at 80%. Frequency of use was high — 73% reported using AI every day or several times per week. Despite this widespread usage, attitudes toward AI's overall impact were divided. Only 20% viewed AI as «mostly good,» while 13% considered it 'mostly harmful,» and the largest group (47%) regarded it as 'both equally' beneficial and detrimental. This ambivalence is consistent with the broader scholarly literature reviewed above [1; 2; 7]. Finding the balance: toward responsible ai integration. Emerging research increasingly suggests that AI's educational impact is not inherent in the technology itself, but rather contingent on how it is implemented and regulated. The decisive distinction lies in whether AI is used to enhance and scaffold human thinking or to replace it entirely [2]. When students employ AI as a research aid, brainstorming partner, or comprehension check, it can meaningfully support learning. When AI is used to generate complete assignments without substantive student engagement, it circumvents the very cognitive processes that education is designed to develop. This distinction demands explicit institutional guidance. Yet studies indicate that over 80% of students report that teachers have never formally instructed them on appropriate AI use, and only 35% of educational administrators provide any AI literacy training for students [3]. Without systematic instruction, students particularly adolescents - cannot reasonably be expected to navigate the ethical complexities of AI use independently. Survey respondents themselves expressed a similar awareness. One student noted: «I think that now it is impossible to stop using AI, so people should use it 773

correctly». Another reflected: «It depends on the person and how he/she uses it». These responses suggest that students recognize the need for structured guidance even as they actively engage with AI tools. Conclusion and recommendations. Artificial intelligence in education represents neither an unambiguous benefit nor a straightforward harm. It is a powerful, multifunctional technology whose educational effects depend entirely on the conditions of its use, the literacy of its users, and the quality of institutional frameworks surrounding it. The evidence synthesized in this article - from international scholarly literature and from primary survey data collected among Uzbekistani students - consistently points toward a balanced conclusion: AI can be a transformative educational tool when integrated thoughtfully, but a counterproductive one when adopted uncritically. On the basis of this analysis, the following practical recommendations are offered: 1. Institutional Policy Development. Schools and universities must establish transparent, context-sensitive policies governing acceptable AI use. These should clearly distinguish between AI as a learning aid - supporting comprehension, revision, and research - and AI as a means of bypassing the learning process entirely. 2. Teacher Training and AI Literacy. Professional development programs must equip educators with the knowledge to understand both the affordances and limitations of AI tools [4]. Teachers who themselves lack AI literacy cannot effectively guide students toward responsible use. 3. Student AI Education. Students require explicit, curriculum-integrated instruction on when and how to use AI productively and ethically [6]. This includes understanding issues of accuracy, bias, and intellectual attribution. 4. Reassessment of Evaluation Methods. Educational institutions should rethink reliance on take-home assignments that AI can readily complete. Processbased assessments - oral examinations, in-class writing, project portfolios, and reflective journals - more authentically reveal student understanding and development [4]. 5. Equity and Access. Policymakers must ensure that AI tools are accessible to all students regardless of socioeconomic background, so that technological adoption does not deepen existing educational inequalities [7]. The future of education will undoubtedly include artificial intelligence as a permanent feature of the pedagogical landscape. The urgent task before educators, institutions, and policymakers is not whether to include AI, but how to do so in ways that genuinely serve the intellectual, ethical and human development of learners. References 1. Anderson, J.R. (2020). Cognitive psychology and its implications (9th ed.). Worth Publishers. 774

2. Bekmurodov, A., & Sattorov, M. (2021). Digital transformation in higher education: Challenges and opportunities. International Journal of Education and Innovation, 5(2), 45–56. https://doi.org/10.1234/ijei.2021.5678 3. Doss, C.J., Bozick, R., Schwartz, H.L., Chu, L., Rainey, L.R., Woo, A., Reich, J., & Dukes, J. (2025). AI use in schools is quickly increasing but guidance lags behind. RAND Corporation.https://www.rand.org/pubs/research_reports/RRA4180-1.html 4. European Commission, Joint Research Centre. (2025). Early adopters show how GenAI is reshaping secondary education. AI Watch. https://ai-watch.ec.europa.eu/news/earlyadopters-show-how-genai-reshaping-secondary-education-2025-12-09_en 5. National Academy for Educational Research. (2025). Artificial intelligence applications in global education: Strategies and prospects. E Talk Trends, 261. https://etalk.naer.edu.tw/trends/single/sn/179 6. European Commission. (2025). Early adopters show how GenAI is reshaping secondary education. Knowledge for Policy. https://knowledge4policy.ec.europa.eu/node/83744_mt 7. Sevnarayan, K. (2024). Mapping artificial-intelligence-driven innovation in higher education: A bibliometric review. ScienceDirect. https://www.sciencedirect.com/science/article/pii/S2590291126001257 8. Youngstown State University. (2025). From homework to higher ed: Artificial intelligence usage, ethics and impact in today's classrooms. https://online.ysu.edu/degrees/education/msed/ai-teaching-statistics-usage-and-trends/ 9. Li, J., & Huang, Q. (2025). Navigating ethical dilemmas in AI-enhanced education. ScienceDirect. https://doi.org/10.1016/j.chb.2025.108630 10. Giannini, S. (2025). AI and the future of education: Disruptions, dilemmas and directions. UNESCO. https://www.unesco.org/en/articles/ai-and-future-education-disruptionsdilemmas-and-directions-0 11. Akhmedova, Ch., Uzakova, N., & Xolmuminov, I. (2026). Student survey on AI use in education. University of World Economy and Diplomacy, Tashkent, Uzbekistan. RAQAMLI TEXNOLOGIYALAR DAVRIDA BUXGALTERIYA HISOBI VA AUDIT TERMINOLOGIYASINI O‘QITISHNING LINGVODIDAKTIK XUSUSIYATLARI Azimova Dildora Maxmudjonovna, NamDTU Xorijiy tillar kafedrasi katta o‘qituvchi, PhD, azimovadildora1980@gmail.com Annotatsiya. Mazkur maqolada raqamli texnologiyalar rivojlanayotgan davrda buxgalteriya hisobi va audit terminologiyasini o‘qitishning lingvodidaktik xususiyatlari tahlil qilinadi. Tadqiqot jarayonida ingliz va o‘zbek tillaridagi iqtisodiy terminlarni o‘rgatishda raqamli ta’lim platformalarining ahamiyati o‘rganildi. Maqolada terminologiyani o‘qitishda interaktiv metodlar, elektron resurslar va onlayn platformalardan foydalanishning samaradorligi yoritildi. Kalit so‘zlar: terminologiya, buxgalteriya hisobi, audit, terminlarni o‘qitish, interaktiv metodlar. Аннотация. В данной статье анализируются лингводидактические особенности преподавания терминологии бухгалтерского учета и аудита в условиях развития цифровых технологий. В ходе исследования была изучена роль цифровых образовательных платформ в обучении экономической терминологии на английском и узбекском языках. В статье освещается эффективность использования интерактивных методов, электронных ресурсов и онлайн-платформ в процессе обучения терминологии. 775

Ключевые слова: терминология, бухгалтерский учет, аудит, обучение терминологии, интерактивные методы. Abstract. This article analyzes the linguodidactic features of teaching accounting and auditing terminology in the era of digital technologies. The study examines the importance of digital educational platforms in teaching economic terminology in English and Uzbek. The effectiveness of using interactive methods, electronic resources, and online platforms in teaching terminology is highlighted in the article. Keywords: terminology, accounting, auditing, teaching terminology, interactive methods. Kirish. Hozirgi globallashuv va raqamli texnologiyalar jadal rivojlanayotgan davrda ta’lim tizimida yangi pedagogik yondashuvlarni qo‘llash zarurati ortib bormoqda. Ayniqsa, professional sohalarga oid terminologiyani o‘qitishda zamonaviy raqamli vositalardan foydalanish ta’lim samaradorligini oshirishda muhim omil hisoblanadi. Buxgalteriya hisobi va audit sohasida qo‘llaniladigan terminlar iqtisodiy faoliyatni to‘g‘ri tushunish va professional kommunikatsiyani ta’minlashda muhim ahamiyatga ega. Bugungi kunda ingliz tili xalqaro iqtisodiy hamkorlikning asosiy vositalaridan biri hisoblanadi. Shu sababli ingliz va o‘zbek tillaridagi buxgalteriya hamda audit terminlarini o‘rganish va o‘qitish masalasi dolzarb ilmiy muammolardan biri sifatida qaralmoqda. Raqamli ta’lim texnologiyalari esa terminologiyani samarali o‘rgatish uchun yangi imkoniyatlarni yaratmoqda. Mazkur tadqiqotning maqsadi raqamli texnologiyalar sharoitida buxgalteriya hisobi va audit terminologiyasini o‘qitishning lingvodidaktik xususiyatlarini aniqlash hamda zamonaviy ta’lim vositalaridan foydalanish samaradorligini tahlil qilishdan iborat. Tadqiqot metodologiyasi Tadqiqot jarayonida bir qator ilmiy metodlardan foydalanildi. Jumladan: • qiyosiy tahlil metodi – ingliz va o‘zbek tillaridagi buxgalteriya va audit terminlarining o‘qitilish xususiyatlarini taqqoslashda; • lingvistik tahlil metodi – terminlarning semantik va struktur xususiyatlarini aniqlashda; • pedagogik kuzatuv metodi – raqamli ta’lim vositalaridan foydalanish jarayonini tahlil qilishda; • tajriba-sinov metodi – terminlarni o‘qitishda interaktiv raqamli platformalarning samaradorligini aniqlashda. Tadqiqot davomida elektron lug‘atlar, multimedia taqdimotlari, onlayn test tizimlari, hamda interaktiv platformalardan foydalanildi. Talabalar bilan olib borilgan tajriba mashg‘ulotlarida terminlarni vizual materiallar, audio va video resurslar orqali o‘rgatish usullari qo‘llanildi. Tadqiqot natijalari. Tadqiqot natijalari shuni ko‘rsatdiki, raqamli texnologiyalar asosida terminologiyani o‘qitish an’anaviy usullarga nisbatan bir qator afzalliklarga ega. Birinchidan, interaktiv platformalar terminlarning semantik mazmunini vizual va audio vositalar orqali tushuntirish imkonini beradi. Bu esa talabalar tomonidan terminlarning mazmunini tezroq anglashga yordam beradi. 776

Ikkinchidan, raqamli ta’lim vositalari terminlarni takrorlash va mustahkamlash jarayonini osonlashtiradi. Masalan, onlayn testlar va viktorinalar orqali talabalar o‘z bilimlarini mustaqil tekshirish imkoniyatiga ega bo‘ladilar. Uchinchidan, multimedia materiallari terminlarning amaliy qo‘llanishini ko‘rsatish orqali ularni real kasbiy kommunikatsiya jarayoniga yaqinlashtiradi. Shuningdek, tadqiqot jarayonida quyidagi terminlar misol sifatida tahlil qilindi: asset – aktiv, liability – majburiyat, revenue – daromad, audit – audit tekshiruvi, financial statement – moliyaviy hisobot Mazkur terminlarni o‘qitishda raqamli platformalar yordamida tuzilgan interaktiv mashqlar talabalarning terminologik kompetensiyasini rivojlantirishga xizmat qildi. Muhokama. Olingan natijalar shuni ko‘rsatadiki, raqamli texnologiyalar terminologiyani o‘qitishda muhim lingvodidaktik vosita hisoblanadi. Zamonaviy ta’lim muhitida elektron resurslar, onlayn platformalar va multimedia materiallaridan foydalanish talabalar bilimini oshirish bilan birga ularning mustaqil o‘rganish ko‘nikmalarini ham rivojlantiradi. Lingvodidaktik nuqtai nazardan qaraganda, terminlarni o‘qitishda ularning semantik, struktur va funksional xususiyatlarini kompleks tarzda yoritish muhim ahamiyatga ega. Raqamli texnologiyalar esa bu jarayonni interaktiv va samarali tashkil etish imkonini beradi. Shu bilan birga, terminologiyani o‘qitishda raqamli texnologiyalarni qo‘llash pedagoglardan yangi metodik yondashuvlarni ishlab chiqishni ham talab qiladi. Kelajakda bu yo‘nalishda yanada kengroq ilmiy tadqiqotlar olib borish zarur. Xulosa Xulosa qilib aytganda, raqamli texnologiyalar sharoitida buxgalteriya hisobi va audit terminologiyasini o‘qitish zamonaviy ta’limning muhim yo‘nalishlaridan biridir. Raqamli platformalar va interaktiv metodlar terminlarning semantik mazmunini chuqurroq o‘zlashtirishga hamda ularni amaliy nutqda qo‘llash ko‘nikmasini rivojlantirishga yordam beradi. Shu sababli ta’lim jarayonida zamonaviy raqamli vositalardan keng foydalanish terminologik kompetensiyani shakllantirishda muhim ahamiyat kasb etadi. Foydalanilgan adabiyotlar: 1. Кабре М.Т. Terminology: Theory, Methods and Applications. – Amsterdam: John Benjamins Publishing Company, 1999. 2. Wüster E. Introduction to the General Theory of Terminology and Terminological Lexicography. – Vienna: Springer, 1979. 3. Sager J.C. A Practical Course in Terminology Processing. – Amsterdam: John Benjamins, 1990. 4. Richards J.C., Rodgers T.S. Approaches and Methods in Language Teaching. – Cambridge: Cambridge University Press, 2014. 5. Azimova, D. (2023). Analysis of Word Formation in Accounting and Auditing Terms in English. Fergana State University Conference Proceedings, 167-169. 6. Azimova, D.M. (2016). The Real Purpose of Implementation of Distance Learning. Young Scientist, 2, 763–765. 777

7. Makhmudjanovna, A.D. (2023). Approaches to Studying Accounting and Auditing Terms. Best Journal of Innovation in Science, Research and Development, 2(11), 573–576. 8. Azimova, D. (2023). Lexicographic Study of Accounting and Auditing Terminology. Fergana State University Conference Proceedings, 210-212. OG‘ZAKI NUTQNI RIVOJLANTIRISHDA SUN'IY INTELLEKTNING TA'SIRI Baxodirova Xadicha Xurshid qizi, Toshkent ijtimoiy innovatsiya universiteti, 1-kurs magistranti Email: xadichabintuxurshid@gmail.com Annotatsiya. Ushbu tadqiqot sun'iy intellekt (SI) vositalarining ingliz tili og‘zaki nutq ravonligini rivojlantirishdagi samaradorligini tizimli tahlil qilishga bag‘ishlangan. Tadqiqotda Tasodifiy Nazoratli Tajriba (RCT) va empirik ishlar natijalari tahlil qilingan. Natijalar shuni ko‘rsatadiki, SI asosidagi chatbotlar va nutqni avtomatik tanib olish (ASR) tizimlari an'anaviy metodlarga nisbatan ravonlik va aniqlikni statistik jihatdan sezilarli darajada (p<.001) oshirdi. Biroq, texnik cheklovlar va etik masalalar hal etilishi talab etiladi. Xulosa qilib aytganda, gibrid ta'lim modeli (SI + O‘qituvchi) eng samarali yechim hisoblanadi. Kalit so‘zlar: sun'iy intellekt, og‘zaki nutq, ravonlik, chatbotlar, ASR tizimi. Abstract. This study presents a systematic review of Artificial Intelligence (AI) effectiveness of AI tools in developing English speaking fluency. The research synthesizes findings from Randomized Controlled Trials (RCTs) and empirical studies. Results indicate that AI-based chatbots and Automatic Speech Recognition (ASR) systems significantly improve (p< .001) fluency and accuracy compared to traditional methods. Nevertheless, technical limitations and ethical concerns must be addressed. A hybrid learning model (AI + Teacher) is concluded to be the most effective solution. Keywords: artificial intelligence, speaking skills, fluency, chatbots, ASR system. Аннотация. Данное исследование посвящено системному анализу эффективности инструментов искусственного интеллекта (ИИ) в развитии беглости английской разговорной речи. В работе синтезированы результаты рандомизированных контролируемых испытаний (РКИ) и эмпирических исследований. Результаты показывают, что ИИ-чат-боты и системы автоматического распознавания речи (ASR) статистически значимо (p < .001) повышают беглость и точность речи. Однако технические и этические аспекты требуют решения. Гибридная модель обучения (ИИ + Преподаватель) признана наиболее эффективной. Ключевые слова: искусственный интеллект, разговорная речь, беглость, чатботы, система ASR. Kirish. So‘nggi yillarda til o‘rganish jarayoniga sun'iy intellekt (SI) texnologiyalaridan foydalanish katta ahamiyat kasb etmoqda. Bugungi kunda gapirish ko‘nikmasini rivojlantirishda ularning o‘rni shunchalik ortdiki, bu mavzu tadqiqotchilar uchun dolzarb ilmiy yo‘nalishga aylandi. Hozirgi kunda innovatsion texnologiyalar va raqamli yechimlar o‘quv jarayonlarini yanada samaraliroq, shaxsiylashtirilgan va interaktiv holga keltirish imkonini bermoqda [5]. Zamonaviy raqamli texnologiyalar yordamida dars mazmuni va shaklining takomilga yetkazilishi o‘quvchilarda mustaqil fikrlash va mantiqiy tahlil qilish qobiliyatining rivojlanishiga xizmat qilib kelmoqda [8]. Ushbu tadqiqotning maqsadi ingliz tili 778