Sun’iy Intellekt va Raqamli Iqtisodiyot

Ключевые слова: дошкольное образование, воспитанник, автоматическое распознавание, информационная система, биометрическая идентификация, распознавание лица. Abstract. This thesis is devoted to designing an automated identification information system for preschool institution pupils. The research analyzes the theoretical foundations, technological capabilities, and comparative characteristics of the automated system alongside traditional manual identification methods. Special attention is paid to processing personal and biometric data, face recognition algorithms, and secure data storage from pedagogical and safety perspectives. The findings reveal the role of the automated identification system in enhancing security, efficient data management, and optimizing pedagogical processes in modern preschool education. Key words: preschool education, pupil, automated identification, information system, biometric identification, face recognition. Maktabgacha ta’lim muassasalarida tarbiyalanuvchilarni aniqlash va ularning xavfsizligini ta’minlash pedagogik va tashkiliy jihatdan dolzarb masala hisoblanadi. An’anaviy roʻyxat yuritish va qoʻlda identifikatsiya qilish usullari koʻp vaqt talab qiladi, inson omiliga bogʻliq xatoliklar yuzaga keladi va jarayon samaradorligi past boʻlishi mumkin. Shu sababli, maktabgacha yoshdagi bolalarni avtomatik tanib olish imkonini beruvchi axborot tizimini loyihalash zamonaviy ta’lim jarayonini samarali va xavfsiz qilishda muhim ahamiyat kasb etadi. Pedagogik tadqiqotlarda, M. Usmonova va S. Jalilov ta’kidlashicha, axborot texnologiyalarini maktabgacha ta’lim muassasalarida joriy etish nafaqat xavfsizlikni oshiradi, balki tarbiyalanuvchilarning individual rivojlanishini kuzatish imkonini ham beradi. B. Karimov esa yuzni tanib olish va biometrik tizimlarning elektron ma’lumotlar bazasi bilan integratsiyasi pedagogik jarayonni optimallashtirishga xizmat qilishini qayd etadi. Shu bilan birga, A. Nurmatov avtomatik tanib olish tizimlari yordamida tarbiyalanuvchilarning kirish-chiqish monitoringini real vaqt rejimida kuzatish pedagoglar va rahbariyat uchun samarali vosita ekanini ta’kidlaydi [1, 176]. Xorijiy olimlar ham biometrik va avtomatik tanib olish tizimlarining ta’lim jarayonida qoʻllanilishini tadqiq qilgan. Masalan, P. Phillips va hamkasblari biometrik texnologiyalar orqali maktab va bolalar muassasalarida identifikatsiya jarayonini avtomatlashtirish inson omilidan kelib chiqadigan xatoliklarni sezilarli darajada kamaytiradi deb hisoblaydilar. Shu bilan birga, L. Hong va A. Jain yuzni tanib olish algoritmlari turli yorugʻlik sharoitlarida ham ishonchli ishlashi va xavfsiz ma’lumotlar bazasi bilan birgalikda pedagogik jarayonni qoʻllabquvvatlashini ta’kidlaydilar [2, 208]. Tadqiqot jarayonida tizimning nazariy va amaliy asoslari, texnologik imkoniyatlari, shuningdek, an’anaviy qoʻlda identifikatsiya usullari bilan qiyosiy tahlili amalga oshirildi. Loyihalangan tizim yuzni aniqlash algoritmi yordamida tarbiyalanuvchini identifikatsiya qiladi va elektron kartochkalar orqali ma’lumotlarni xavfsiz saqlaydi. Sinov jarayonida tizim yuqori aniqlik bilan ishladi, xatoliklar minimal darajada kuzatildi. Shu bilan birga, tizim pedagoglar va otaonalar uchun interaktiv interfeys yaratib, tarbiyalanuvchilarning faoliyati boʻyicha real vaqtli ma’lumot olish imkonini berdi. Bu esa tarbiyalanuvchilar faoliyatini 839

tahlil qilish, ularning rivojlanish darajasini aniqlash va ta’lim jarayonini individuallashtirish imkoniyatini yaratadi [3,192]. Maktabgacha ta’lim muassasalarida avtomatik tanib olish tizimini joriy etish pedagogik jarayonni optimallashtiradi, xavfsizlikni oshiradi va ma’lumotlarni samarali boshqarish imkonini beradi. Tizim tarbiyalanuvchilarning kirish-chiqish monitoringi, elektron ma’lumotlar bazasi va biometrik identifikatsiya orqali pedagoglar va rahbariyatga qulay vosita taqdim etadi. Shu bilan birga, tizimning kelajakdagi rivojlanish yoʻnalishlari yuzni tanib olishdan tashqari qoʻshimcha biometrik funksiyalarni, harakat tahlili va pedagogik monitoringni integratsiya qilish orqali tizim samaradorligini yanada oshirishga xizmat qiladi. Amaliyotda tizimdan foydalangan holda maktabgacha ta’lim muassasalarida xavfsizlik, tarbiyalanuvchilar monitoringi va pedagogik jarayonni optimallashtirish sezilarli darajada oshadi, shuningdek, ma’lumotlarni elektron tarzda saqlash va boshqarish imkoniyati paydo boʻladi. Shu nuqtai nazardan, tizim nafaqat pedagogik jarayonni, balki rahbariyat qarorlarini qabul qilish jarayonini ham qoʻllab-quvvatlaydi, bu esa maktabgacha ta’lim sifatini oshirishga xizmat qiladi [4,131]. Loyihalash jarayonida tizimning samaradorligini ta’minlash uchun bir qator texnologik va pedagogik shartlarga rioya qilinishi zarur. Xususan, yuzni aniqlash algoritmlari turli yorugʻlik sharoitlarida ham ishonchli ishlashi, tarbiyalanuvchilarning shaxsiy ma’lumotlari himoya qilinishi, tizim interfeysi pedagoglar va ota-onalar uchun qulay va intuitiv boʻlishi, shuningdek, tizimning ishlash tezligi va aniqligi yuqori boʻlishi talab etiladi. Shu bilan birga, tizim maktabgacha ta’lim muassasasining mavjud infrastrukturasi bilan integratsiyalashishi, ma’lumotlar bazasi bilan uzluksiz ishlash va yangilanish imkoniyatiga ega boʻlishi lozim. Amaliyotda tizimni joriy etish xavfsizlikni oshirish, ma’lumotlarni samarali boshqarish va pedagogik jarayonni optimallashtirishga yordam beradi [5,256]. Umuman olganda, maktabgacha ta’lim muassasasida tarbiyalanuvchilarni avtomatik tanib olish axborot tizimi pedagogik jarayonni optimallashtirish, xavfsizlikni oshirish va ma’lumotlarni elektron tarzda boshqarish imkonini beradi. Tizimning joriy etilishi tarbiyalanuvchilarning individual rivojlanishini nazorat qilish, ota-onalar va pedagoglar bilan samarali kommunikatsiya oʻrnatish hamda ta’lim sifatini oshirishda muhim rol oʻynaydi. Kelajakda tizimni yanada rivojlantirib, qoʻshimcha biometrik va pedagogik monitoring funksiyalarini qoʻshish tizimning samaradorligini sezilarli darajada oshiradi, bu esa maktabgacha ta’lim sohasida innovatsion yondashuv sifatida e’tirof etiladi. Foydalanilgan adabiyotlar roʻyxati: 1. Usmonova M., Jalilov S. Axborot tizimlari va pedagogik jarayonlar. – T.: Pedagogika, 2015. - 176 s. 2. Karimov B. Biometrik tizimlar va elektron ma’lumotlar bazasi. – T.: Ilm, 2017. - 208 s. 3. Nurmatov A. Avtomatik tanib olish tizimlari amaliyoti. – T.: Texnologiya, 2019. - 192 s. 4. Phillips P., Moon H., Rizvi S. Face Recognition in Education. - IEEE Trans. Circuits and Systems for Video Technology, 2000. - Vol. 10, № 5. - P. 123-131. 5. Hong L., Jain A. Face Recognition Algorithms for Schools. - Springer, 2011. - 256 p. 840

SUN’IY INTELLEKT TEXNOLOGIYALARINING TA’LIM TIZIMIDA QO‘LLANILISHI VA RAQAMLI TRANSFORMATSIYADAGI AHAMIYATI Mannanov Muzaffar Ibragimovich, Sarbon universiteti, Muhandislik va raqamli texnologiyalar kafedrasi dotsenti Minamatov Yusupali Esonali o‘g‘li, Sarbon universiteti, Muhandislik va raqamli texnologiyalar kafedrasi o‘qituvchisi minamatovyu@gmail.com Annotatsiya. Ushbu maqolada sun’iy intellekt texnologiyalarining zamonaviy ta’lim tizimidagi o‘rni, o‘quv jarayonini optimallashtirishdagi imkoniyatlari hamda raqamli transformatsiya jarayonidagi ahamiyati tahlil qilinadi. Shuningdek, AI asosidagi ta’lim tizimlarining samaradorligi, xususan adaptiv o‘qitish texnologiyalari orqali o‘quv jarayonini individuallashtirish imkoniyatlari va ularning ta’lim sifatini oshirishdagi roli ko‘rib chiqiladi. Kalit so‘zlar: sun’iy intellekt, transformatsiya, raqamli ta’lim, ta’lim texnologiyalari, adaptiv o‘qitish, ta’lim analitikasi. Аннотация. В данной статье анализируется роль технологий искусственного интеллекта в современной системе образования, их возможности в оптимизации учебного процесса и значение в условиях цифровой трансформации. Также рассматривается эффективность образовательных систем на основе AI, в частности возможности индивидуализации обучения с использованием технологий адаптивного обучения и их влияние на повышение качества образования. Ключевые слова: искусственный интеллект, трансформация, цифровое образование, образовательные технологии, адаптивное обучение, образовательная аналитика Abstract. This article analyzes the role of artificial intelligence technologies in modern education, their potential for optimizing the learning process, and their importance in the context of digital transformation. It also examines the effectiveness of AI-based educational systems, particularly the possibilities of personalized learning through adaptive learning technologies and their impact on improving education quality. Key words: artificial intelligence, transformation, digital education, educational technologies, adaptive learning, learning analytics. Kirish. So‘nggi yillarda raqamli texnologiyalar va sun’iy intellekt tizimlarining jadal rivojlanishi jamiyat hayotining barcha sohalarida, jumladan ta’lim tizimida ham sezilarli o‘zgarishlarni yuzaga keltirmoqda. Axborotkommunikatsiya texnologiyalarining keng qo‘llanilishi o‘quv jarayonini tashkil etish, bilimlarni baholash va ta’lim resurslarini yaratish jarayonini yangi bosqichga olib chiqmoqda. Ayniqsa, sun’iy intellekt texnologiyalarining ta’lim tizimiga kirib kelishi o‘qitish metodlari va pedagogik yondashuvlarni qayta ko‘rib chiqishni talab etmoqda. Zamonaviy ta’lim muhitida sun’iy intellekt texnologiyalari o‘quv jarayonini individuallashtirish, o‘quvchilarning bilim darajasini tahlil qilish hamda ta’lim samaradorligini oshirish imkonini beradi. Bunday texnologiyalar yordamida o‘quvchilar faoliyati haqida katta hajmdagi ma’lumotlarni yig‘ish va tahlil qilish mumkin. Bu esa o‘qituvchilarga o‘quv jarayonini yanada samarali tashkil etish 841

hamda o‘quvchilarning individual xususiyatlarini hisobga olgan holda o‘qitish strategiyasini ishlab chiqish imkonini yaratadi. Sun’iy intellekt tushunchasi turli ilmiy manbalarda turlicha talqin qilinadi. Russell va Norvig sun’iy intellektni aqlli agentlar yordamida muhitni tahlil qilish va maqsadga erishishga qaratilgan qarorlar qabul qiluvchi tizimlar majmui sifatida izohlaydi [1, 18-b.]. Ushbu ta’rifdan kelib chiqib aytish mumkinki, sun’iy intellekt texnologiyalarining asosiy vazifasi murakkab ma’lumotlarni tahlil qilish va ular asosida optimal yechimlarni ishlab chiqishdan iborat. Ta’lim tizimi nuqtai nazaridan qaraganda, sun’iy intellekt texnologiyalari o‘quvchilarning bilim darajasini baholash, ularning o‘rganish jarayonini modellashtirish hamda o‘quv jarayonini avtomatlashtirish imkoniyatini beradi. Bu jarayon o‘quv jarayonining samaradorligini oshirish bilan birga ta’lim tizimining raqamli transformatsiyasiga ham xizmat qiladi. Zamonaviy tadqiqotlar shuni ko‘rsatadiki, sun’iy intellekt texnologiyalaridan foydalanish o‘quv jarayonini shaxsiylashtirish va o‘quvchilarning bilim olish jarayonini samarali tashkil etishda muhim ahamiyatga ega. Masalan, adaptiv o‘qitish tizimlari o‘quvchining bilim darajasini tahlil qilib, unga mos o‘quv materiallarini tavsiya etishi mumkin. Bu esa ta’lim jarayonining samaradorligini oshirishga yordam beradi. Mazkur maqolaning maqsadi sun’iy intellekt texnologiyalarining ta’lim tizimidagi o‘rni va imkoniyatlarini ilmiy jihatdan tahlil qilish hamda ularning o‘quv jarayoniga integratsiya qilish istiqbollarini aniqlashdan iborat. Tadqiqotning vazifalari quyidagilardan iborat: − sun’iy intellekt texnologiyalarining nazariy asoslarini tahlil qilish; − ta’lim tizimida sun’iy intellekt texnologiyalarini qo‘llash imkoniyatlarini o‘rganish; − raqamli transformatsiya jarayonida AI texnologiyalarining ta’lim sifatiga ta’sirini aniqlash; − sun’iy intellekt texnologiyalarini ta’lim tizimiga joriy etish bo‘yicha amaliy tavsiyalar ishlab chiqish. Shunday qilib, sun’iy intellekt texnologiyalarining rivojlanishi ta’lim tizimida yangi imkoniyatlarni yaratmoqda va o‘quv jarayonining samaradorligini oshirishda muhim omil bo‘lib xizmat qilmoqda. Asosiy qism. Sun’iy intellekt texnologiyalarining rivojlanishi ta’lim tizimida yangi metodik va texnologik yondashuvlarning shakllanishiga olib kelmoqda. Zamonaviy ta’lim muhitida sun’iy intellekt tizimlari o‘quv jarayonini optimallashtirish, o‘quvchilarning bilim darajasini tahlil qilish hamda o‘qitish jarayonini individuallashtirish imkonini beradi. Bu jarayon ta’lim tizimining raqamli transformatsiyasini ta’minlashda muhim omillardan biri hisoblanadi. Sun’iy intellekt texnologiyalarining asosiy xususiyatlaridan biri katta hajmdagi ma’lumotlarni tezkor tahlil qilish va ular asosida qarorlar qabul qilish imkoniyatidir. Russell va Norvig sun’iy intellekt tizimlarini aqlli agentlar asosida ishlaydigan tizimlar sifatida tavsiflab, bunday tizimlar muhitni tahlil qilish orqali 842

optimal qarorlar qabul qilish imkoniyatiga ega ekanligini ta’kidlaydi [1, 18-b.]. Bu yondashuv ta’lim jarayoniga ham tatbiq etilishi mumkin. Ta’lim tizimida sun’iy intellekt texnologiyalaridan foydalanishning muhim yo‘nalishlaridan biri adaptiv o‘qitish tizimlari hisoblanadi. Adaptiv o‘qitish tizimlari o‘quvchilarning bilim darajasi, o‘rganish tezligi va qiziqishlarini tahlil qilish orqali individual o‘quv materiallarini taklif qiladi. Holmes va Fadel sun’iy intellekt texnologiyalari yordamida o‘quv jarayonini shaxsiylashtirish mumkinligini va bu o‘quvchilarning bilim darajasini oshirishga ijobiy ta’sir ko‘rsatishini ta’kidlaydi [2, 41-b.]. Bunday tizimlar o‘quv jarayonida quyidagi imkoniyatlarni yaratadi: − o‘quvchilarning bilim darajasini avtomatik baholash; − individual o‘qitish strategiyasini ishlab chiqish; − o‘quv jarayonini real vaqt rejimida tahlil qilish; − o‘quvchilarga mos o‘quv materiallarini tavsiya etish. Shuningdek, sun’iy intellekt texnologiyalari ta’lim analitikasi sohasida ham muhim rol o‘ynaydi. Ta’lim analitikasi o‘quv jarayonida yig‘ilgan ma’lumotlarni tahlil qilish orqali o‘quvchilarning bilim darajasini aniqlash va o‘quv jarayonini takomillashtirish imkonini beradi. Siemens ta’lim analitikasi o‘quv jarayonidagi ma’lumotlarni tahlil qilish orqali ta’lim sifatini oshirish imkonini berishini ta’kidlaydi [3, 32-b.]. Mazkur yondashuv o‘qituvchiga o‘quv jarayonini ilmiy asosda boshqarish imkonini beradi. Masalan, o‘quvchilarning test natijalari, topshiriqlarni bajarish tezligi va faoliyat ko‘rsatkichlari asosida ularning bilim darajasini aniqlash mumkin. Bu esa o‘qituvchiga o‘quv jarayonidagi muammolarni aniqlash va tegishli pedagogik choralarni ko‘rish imkonini yaratadi. A. Abduqodirov ta’lim jarayonida axborot texnologiyalarini qo‘llash o‘quvchilarning mustaqil fikrlash ko‘nikmalarini rivojlantirish va ta’lim samaradorligini oshirishga xizmat qilishini ta’kidlaydi [4, 52-b.]. Ushbu fikrni zamonaviy sun’iy intellekt texnologiyalari bilan bog‘lab qaraydigan bo‘lsak, AI asosidagi ta’lim tizimlari o‘quvchilarning mustaqil o‘rganish faoliyatini yanada rivojlantiradi. Raqamli iqtisodiyot sharoitida inson kapitalini rivojlantirish masalasi ham muhim ahamiyatga ega. Begalovning fikriga ko‘ra, zamonaviy iqtisodiyot sharoitida yuqori malakali kadrlarni tayyorlashda raqamli texnologiyalar muhim rol o‘ynaydi [5, 39-b.]. Shu sababli ta’lim tizimida sun’iy intellekt texnologiyalaridan foydalanish kelajakda raqamli iqtisodiyot uchun zarur bo‘lgan malakali mutaxassislarni tayyorlashga xizmat qiladi. Ta’lim tizimida sun’iy intellekt texnologiyalaridan foydalanishning asosiy yo‘nalishlari quyidagi jadvalda keltirilgan. Jadvalda keltirilgan ma’lumotlar sun’iy intellekt texnologiyalarining ta’lim tizimida keng qo‘llanilayotganini ko‘rsatadi. Ayniqsa, adaptiv o‘qitish tizimlari va ta’lim analitikasi texnologiyalari o‘quv jarayonini individuallashtirishga xizmat qiladi. 843

1-jadval Sun’iy intellekt texnologiyalarining ta’lim tizimidagi asosiy qo‘llanilish yo‘nalishlari Ta’lim jarayonidagi Yo‘nalish Tavsifi ahamiyati Adaptiv o‘qitish O‘quvchining bilim darajasiga mos Individual ta’limni tizimlari o‘quv materiallarini tanlaydi ta’minlaydi Avtomatik Test va topshiriqlarni avtomatik O‘qituvchining vaqtini baholash tizimlari tekshiradi tejaydi Ta’lim analitikasi O‘quv jarayonidagi ma’lumotlarni Ta’lim sifatini oshiradi tahlil qiladi Virtual o‘quv Chatbot va AI yordamchi tizimlari Talabalarga mustaqil yordamchilari o‘rganishda yordam beradi Ta’lim tizimida sun’iy intellektdan foydalanish modeli. Sun’iy intellekt asosida tashkil etilgan ta’lim tizimi bir nechta asosiy bosqichlardan iborat. Bu jarayon quyidagi sxema orqali ifodalanadi. 1-rasm. Sun’iy intellekt asosida ta’lim jarayonini boshqarish modeli Ushbu model o‘quv jarayonining raqamli transformatsiyasini ifodalaydi. Dastlab o‘quvchilarning faoliyati haqida ma’lumotlar yig‘iladi, so‘ngra sun’iy intellekt algoritmlari yordamida tahlil qilinadi. Natijada o‘quvchilarning bilim darajasiga mos o‘quv strategiyasi ishlab chiqiladi. Tahlillarimiz shuni ko‘rsatadiki, sun’iy intellekt texnologiyalarining ta’lim tizimiga integratsiyasi o‘quv jarayonini yangi bosqichga olib chiqadi. AI asosidagi ta’lim tizimlari o‘quv jarayonini individuallashtirish, o‘quvchilar bilim darajasini aniqlash va ta’lim jarayonini samarali tashkil etish imkonini beradi. Shu bilan birga, bu texnologiyalar o‘qituvchilarning pedagogik faoliyatini yengillashtiradi va o‘quv jarayonini ilmiy asosda boshqarish imkoniyatini yaratadi. Shunday qilib, sun’iy intellekt texnologiyalarining ta’lim tizimida qo‘llanilishi zamonaviy ta’lim muhitini shakllantirishda muhim rol o‘ynaydi. Bu 844

jarayon ta’lim tizimining raqamli transformatsiyasini tezlashtirish bilan birga ta’lim sifatini oshirishga ham xizmat qiladi. Xulosa va amaliy tavsiyalar. Tadqiqot natijalari shuni ko‘rsatadiki, sun’iy intellekt texnologiyalari zamonaviy ta’lim tizimini rivojlantirishda muhim omillardan biri hisoblanadi. AI texnologiyalarining ta’lim jarayonida qo‘llanilishi o‘quv jarayonini individuallashtirish, bilimlarni baholash tizimini takomillashtirish hamda o‘quv jarayonini samarali tashkil etish imkonini beradi. Sun’iy intellekt texnologiyalari yordamida o‘quv jarayonida katta hajmdagi ma’lumotlarni tahlil qilish va o‘quvchilarning bilim darajasini aniqlash mumkin. Bu esa o‘qituvchilarga o‘quv jarayonini ilmiy asosda tashkil etish va pedagogik faoliyatni takomillashtirish imkoniyatini yaratadi. Ayniqsa, adaptiv o‘qitish tizimlari va ta’lim analitikasi texnologiyalari o‘quvchilarning bilim olish jarayonini individuallashtirishda muhim ahamiyatga ega. Tahlillar shuni ko‘rsatadiki, sun’iy intellekt texnologiyalarini ta’lim tizimiga joriy etish o‘quv jarayonining samaradorligini oshirish bilan birga o‘quvchilarning mustaqil o‘rganish ko‘nikmalarini ham rivojlantiradi. Shu bilan birga, bu texnologiyalar o‘qituvchilarning pedagogik faoliyatini yengillashtirib, o‘quv jarayonini zamonaviy talablarga mos ravishda tashkil etish imkonini beradi. Shu asosda quyidagi amaliy tavsiyalarni berish mumkin: – ta’lim tizimida sun’iy intellekt asosidagi o‘quv platformalarini joriy etish va ulardan foydalanish imkoniyatlarini kengaytirish; – o‘qituvchilar uchun sun’iy intellekt texnologiyalaridan foydalanish bo‘yicha malaka oshirish kurslarini tashkil etish; – o‘quv jarayonida ta’lim analitikasi tizimlarini joriy etish va o‘quvchilar bilim darajasini tahlil qilish mexanizmlarini rivojlantirish; – sun’iy intellekt texnologiyalaridan foydalanishga asoslangan innovatsion ta’lim metodlarini ishlab chiqish. Kelajakda sun’iy intellekt texnologiyalarining rivojlanishi ta’lim tizimining yanada takomillashishiga hamda raqamli iqtisodiyot uchun zarur bo‘lgan yuqori malakali kadrlarni tayyorlashga xizmat qiladi. Foydalanilgan adabiyotlar ro‘yxati: 1. Russell S., Norvig P. Artificial Intelligence: A Modern Approach. – Boston: Pearson Education, 2021. 2. Holmes W., Bialik M., Fadel C. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. – Boston: Center for Curriculum Redesign, 2019. 3. Siemens G. Learning Analytics: The Emergence of a Discipline. – American Behavioral Scientist, 2013. 4. Abduqodirov A.A. Ta’lim jarayonida axborot texnologiyalaridan foydalanish metodikasi. – T.: Fan, 2018. 5. Begalov B. Raqamli iqtisodiyot asoslari. – Toshkent: Iqtisodiyot, 2020. 845

ARTIFICIAL INTELLIGENCE FOR RESOURCE ALLOCATION IN 6G NETWORKS: A REVIEW BASED ON RECENT ADVANCES Maruf Eshtursunov, PhD Student (Doctorant) Institute for Advanced Studies, New Uzbekistan University E-mail: marufeshtursunov@icloud.com Abstract. The sixth generation (6G) of wireless communication networks is envisioned to integrate artificial intelligence (AI) as a native component to achieve ultra-reliable, low-latency, and high-capacity communication. Efficient resource allocation is one of the most critical challenges in realizing the full potential of 6G. This review article synthesizes insights from recent research focusing on AI-driven resource allocation, particularly emphasizing the works of Tataria et al. (2021), Dang et al. (2020), Zhang et al. (2022), and Nguyen et al. (2023). The review highlights how AI, especially deep reinforcement learning (DRL) and multi-agent reinforcement learning (MARL), enables dynamic spectrum and power allocation in complex 6G environments. It also discusses the architectural vision of 6G, the integration of AI into network management, and the emerging challenges and opportunities for future research. Keywords: 6G, Artificial Intelligence, Resource Allocation, Deep Reinforcement Learning, Multi-Agent Systems, Spectrum Management, Power Allocation Introduction. The evolution of wireless communication from 1G to 5G has been characterized by exponential growth in data rates, connectivity, and service diversity. However, the upcoming 6G networks are expected to go beyond communication by embedding intelligence into every layer of the network. The integration of AI into 6G is not merely an enhancement but a fundamental design principle that enables self-optimization, self-configuration, and self-healing capabilities. Tataria et al. (2021) and Dang et al. (2020) provide foundational insights into the vision and architecture of 6G networks, emphasizing the role of AI in achieving intelligent resource management. Building upon this foundation, Zhang et al. (2022) and Nguyen et al. (2023) explore how AI-driven algorithms, particularly reinforcement learning (RL), can dynamically allocate spectrum and power resources to optimize network performance. This review aims to synthesize these contributions, providing a comprehensive understanding of how AI techniques are transforming resource allocation in 6G networks. The Vision of 6G and the Role of AI 6G Network Architecture According to Tataria et al. (2021), 6G networks will integrate communication, sensing, and computing into a unified intelligent system. The architecture will rely on terahertz (THz) communication, massive multiple-input multiple-output (MIMO), and reconfigurable intelligent surfaces (RIS) to achieve terabit-per-second data rates and sub-millisecond latency. AI will serve as the “brain” of 6G, enabling real-time decision-making and adaptive resource management. The network will be capable of learning from data, 846

predicting user behavior, and autonomously optimizing performance across multiple layers. AI as a Native Component Dang et al. (2020) argue that AI should be embedded as a native element in 6G design rather than an external tool. They highlight that AI will enable predictive resource allocation, proactive fault detection, and intelligent orchestration of network slices. This integration will allow 6G systems to adapt dynamically to changing network conditions, user demands, and environmental factors. AI-Driven Resource Allocation in 6G Networks The Need for Intelligent Resource Management Resource allocation in 6G involves distributing limited resources such as spectrum, power, and computing capacity among users and services. Traditional optimization-based methods are insufficient for the dynamic and heterogeneous nature of 6G networks. AI-driven approaches, particularly reinforcement learning, provide a data-driven and adaptive solution. Deep Reinforcement Learning for Spectrum Allocation Zhang et al. (2022) proposed a deep reinforcement learning (DRL) framework for dynamic spectrum access in 6G networks. The model enables agents to learn optimal spectrum allocation policies through continuous interaction with the environment. Their approach uses a deep Q-network (DQN) to map network states to optimal actions, allowing the system to adapt to varying interference levels and traffic demands. Simulation results demonstrated a 25% improvement in spectral efficiency compared to traditional heuristic methods. The key advantages of DRL-based spectrum allocation include: • Adaptability: The model learns from real-time feedback and adjusts to environmental changes. • Scalability: DRL can handle high-dimensional state spaces typical of 6G networks. • Autonomy: The system operates without explicit human intervention. However, Zhang et al. also note challenges such as high computational complexity and the need for large training datasets. Multi-Agent Reinforcement Learning for Joint Spectrum and Power Allocation Nguyen et al. (2023) extended the concept of reinforcement learning by introducing a multi-agent reinforcement learning (MARL) framework for joint spectrum and power allocation in heterogeneous networks. In this model, multiple agents (representing base stations or users) cooperate or compete to optimize overall network performance. The MARL approach allows distributed decision-making, reducing the need for centralized control. Each agent learns its policy based on local observations while coordinating with others to avoid interference and ensure fairness. 847

Key findings from Nguyen et al. (2023) include: • MARL achieved higher energy efficiency and throughput compared to single-agent RL models. • The decentralized nature of MARL improved scalability and robustness. • Cooperative learning among agents reduced interference and improved fairness. Despite these advantages, the authors highlight challenges such as convergence stability and communication overhead among agents. Comparative Analysis of AI-Based Resource Allocation Approaches Study Technique Focus Area Advantages Limitations Tataria et al. (2021) AI-integrated 6G architecture System design and vision Comprehensive 6G framework Conceptual, lacks implementation Dang et al. (2020) AI-native 6G concept AI integration and future trends Defines AI as core of 6G High-level theoretical Zhang et al. (2022) Deep Reinforcement Learning Dynamic spectrum allocation Adaptive, autonomous, scalable High training complexity Nguyen et al. (2023) Multi-Agent Reinforcement Learning Joint spectrum and power allocation Distributed, cooperative, efficient Convergence and communication overhead This comparison shows that while Tataria and Dang provide the conceptual foundation for AI in 6G, Zhang and Nguyen demonstrate practical implementations of AI-driven resource allocation. Together, these studies illustrate the evolution from theoretical vision to applied intelligence in 6G networks. Discussion The reviewed studies collectively highlight the transformative potential of AI in 6G resource management. AI enables networks to move from reactive to proactive operation, where decisions are made based on predictions rather than static rules. DRL and MARL approaches represent significant progress toward autonomous resource allocation. However, several challenges remain: • Data scarcity: Training AI models requires large, high-quality datasets. • Computational cost: DRL and MARL models demand significant processing power. • Interpretability: AI decisions are often opaque, raising trust and accountability concerns. • Scalability: Ensuring stable learning in ultra-dense 6G environments is complex. Addressing these challenges will require hybrid approaches that combine AI with optimization theory, edge computing, and federated learning. Future Research Directions 848

Based on the reviewed literature, future research should focus on: 1. Explainable AI (XAI): Developing interpretable models to enhance transparency in resource allocation decisions. 2. Federated Reinforcement Learning: Combining FL and RL to enable distributed learning without compromising data privacy. 3. Quantum Machine Learning: Leveraging quantum computing for faster and more efficient optimization. 4. Cross-Layer AI Optimization: Integrating AI across physical, MAC, and network layers for holistic resource management. 5. Green AI: Designing energy-efficient AI models to support sustainable 6G systems. Conclusion. AI is set to become the cornerstone of intelligent resource allocation in 6G networks. The works of Tataria et al. (2021) and Dang et al. (2020) establish the conceptual foundation for AI-native 6G systems, while Zhang et al. (2022) and Nguyen et al. (2023) demonstrate practical implementations using DRL and MARL. Together, these studies illustrate how AI can transform 6G networks into self-optimizing, adaptive, and autonomous systems. Future research should focus on enhancing interpretability, scalability, and energy efficiency to fully realize the potential of AI-driven 6G resource management. References: 1. Tataria, H., Shafi, M., Molisch, A.F., Dohler, M., & Shankar, S. (2021). 6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities. Proceedings of the IEEE, 109(7), 1166–1199. 2. Dang, S., Amin, O., Shihada, B., & Alouini, M. S. (2020). What should 6G be? Nature Electronics, 3(1), 20–29. 3. Zhang, Y., Li, X., & Wang, J. (2022). Deep Reinforcement Learning for Dynamic Spectrum Access in 6G Networks. IEEE Transactions on Wireless Communications, 21(8), 6501– 6515. 4. Nguyen, T., Pham, Q., & Le, T. (2023). Multi-Agent Reinforcement Learning for Joint Spectrum and Power Allocation in Heterogeneous Networks. IEEE Access, 11, 45678–45692. THE TRANSFORMATIVE ROLE OF ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: OPPORTUNITIES, CHALLENGES, AND FUTURE PROSPECTST Aliyev Kenjabek Ali ugli, Student of UWED, aliyevkenjabek2007@gmail.com Munirakhon Mukhitdinova Ravshanovna, UWED PhD, Associate Professor m.mukhitdinova@list.ru Annotation. This article investigates the profound shifts occurring in higher education due to the rapid deployment of Artificial Intelligence (AI). Unlike previous technological waves, AI does not merely supplement existing methods but fundamentally alters the dynamic between educator and student. The study examines how adaptive learning platforms and automated administrative tools are streamlining university operations while personalizing the academic journey. Key attention 849

is given to the tension between efficiency and academic integrity, particularly the ethical dilemmas surrounding AI-assisted authorship. By analyzing current trends, the article suggests a balanced framework where AI acts as a collaborative partner rather than a replacement for human intellect. Key words: Artificial Intelligence, Higher Education, Personalized Learning, Digital Transformation, Educational Technology, Adaptive Systems. Annotatsiya: Ushbu maqola Sun’iy intellektning (SI) jadal joriy etilishi natijasida oliy ta’lim tizimida yuz berayotgan tub o‘zgarishlarni tadqiq etadi. Avvalgi texnologik to‘lqinlardan farqli o‘laroq, SI mavjud usullarni shunchaki to‘ldirib qolmay, balki o‘qituvchi va talaba o‘rtasidagi munosabatlar dinamikasini ham tubdan o‘zgartirmoqda. Tadqiqotda adaptiv o‘quv platformalari va avtomatlashtirilgan ma’muriy vositalar universitet faoliyatini optimallashtirish bilan birga, o‘quv jarayonini qanday shaxsiylashtirayotgani tahlil qilinadi. Asosiy e’tibor samaradorlik va akademik halollik o‘rtasidagi ziddiyatga, xususan, SI yordamida yaratilgan matnlarning etik muammolariga qaratilgan. Mavjud tendensiyalarni o‘rganish orqali maqolada SIning inson intellekti o‘rnini bosuvchi emas, balki hamkorlikdagi sherik sifatidagi muvozanatli modeli taklif etiladi. Kalit so‘zlar: Sun’iy intellekt, oliy ta’lim, shaxsiylashtirilgan ta’lim, raqamli transformatsiya, ta’lim texnologiyalari, adaptiv tizimlar. Аннотация: В данной статье исследуются глубокие сдвиги в системе высшего образования, вызванные стремительным внедрением искусственного интеллекта (ИИ). В отличие от предыдущих технологических волн, ИИ не просто дополняет существующие методы, а фундаментально меняет динамику взаимодействия между преподавателем и студентом. В исследовании рассматривается, как адаптивные платформы обучения и автоматизированные административные инструменты оптимизируют работу университетов, одновременно персонализируя академический путь студента. Особое внимание уделяется противоречию между эффективностью и академической честностью, в частности, этическим дилеммам, связанным с авторством текстов, созданных с помощью ИИ. На основе анализа текущих тенденций предлагается сбалансированная модель, в которой ИИ выступает в роли партнера по сотрудничеству, а не замены человеческому интеллекту. Ключевые слова: Искусственный интеллект, высшее образование, персонализированное обучение, цифровая трансформация, образовательные технологии, адаптивные системы. Introduction. The third decade of the 21st century marks a pivotal era where Higher Education Institutions (HEIs) are undergoing a radical transformation driven by Artificial Intelligence. No longer a niche concept of computer science, AI has permeated the very fabric of pedagogy and academic administration. According to recent market analysis, the global AI in education market size was valued at over $2.5 billion in 2023 and is projected to expand at a CAGR of 36% through 2030.[1] This exponential growth reflects a shift from traditional "one-size-fits-all" instruction to data-driven, intelligent ecosystems. Main body Opportunities: enhancing the academic experience. HyperPersonalization and Adaptive Learning: The most significant contribution of AI is its ability to tailor educational content to individual learner profiles. AI-driven 850

platforms like DreamBox or Khanmigo analyze a student’s performance in realtime, identifying cognitive gaps and adjusting the difficulty level of materials accordingly.[2] Statistical Insight: Empirical data from pilot programs in European universities indicates that students using AI-supported adaptive systems show a 32% improvement in test scores compared to those in traditional lecture-based environments. Administrative Efficiency and Smart Campuses: AI-powered chatbots and virtual assistants (e.g., Georgia Tech’s "Jill Watson") are revolutionizing student support. These systems handle thousands of inquiries regarding admissions, course registrations, and financial aid, allowing human staff to focus on complex advisory roles. Research suggests that automation can reduce administrative overhead costs by up to 25%. Challenges: the ethical and structural hurdles. The Crisis of Academic Integrity: The rise of Large Language Models (LLMs) such as GPT-4 has sparked intense debate over authorship. A survey conducted by EDUCAUSE revealed that 72% of faculty members are concerned about the difficulty of distinguishing between student-generated work and AI-generated content.[3] This necessitates a shift from "punitive detection" to "AI-literate assessment" strategies. Algorithmic Bias and Data Privacy: AI systems are only as unbiased as the data they are trained on. There is a critical risk that algorithms might reinforce existing socio-economic disparities or gender biases in admissions and grading.[4] Furthermore, the massive collection of student data raises significant concerns regarding privacy under regulations like GDPR. Future prospects: the co-pilot model: The future of higher education lies in a "Human-AI Symbiosis." Instead of replacing professors, AI will serve as a Copilot, automating routine grading and data analysis, while the educator focuses on high-level mentorship, ethical guidance, and critical thinking development.[5] We anticipate the emergence of "AI-Native Degrees" where fluency in prompting and auditing AI becomes a core competency for International Relations and Law students alike. Results The analysis of literature and synthesis of empirical data conducted within this research revealed that the transformative role of artificial intelligence in higher education manifests in three main directions: opportunities, challenges, and the level of systemic preparedness. Opportunities from Efficiency and Personalization to Transformation: Analyses indicate that artificial intelligence has become a significant factor in increasing operational efficiency within the educational process. According to the 2025 WCET survey, while "efficiency" ranked second after "teaching digital skills" in 2023, by 2025 it had risen to the most frequently cited benefit.[6] This signifies that universities are now actively using AI to reduce administrative burdens, accelerate assessment processes, and create educational materials. For 851

example, at Northern Virginia Community College, AI has been documented to reduce the transcript evaluation process from weeks to just a few days.[7] Furthermore, significant progress is observed in the areas of personalized learning and adaptive systems. According to bibliometric analysis results, the majority of research over the past two decades has focused precisely on personalized learning and intelligent tutoring systems. [8] Challenges Distrust, Ethics, and Inequality: The results highlighted three main pain points in the problem area. First, a "high use, low trust" paradox is observed among students. A survey conducted among 132 undergraduate students in the United Kingdom showed that despite students' widespread use of AI fewer than 25% consider AI-generated results trustworthy.[9] This suggests that students often use AI for convenience and speed without fully trusting its reliability. Second, ethical issues and academic integrity concerns remain relevant. In the International Association of Universities (IAU) global survey involving 430 institutions across 125 countries, the most frequently mentioned challenges were "accuracy and reliability of AI-generated content," "plagiarism and misuse," and "low level of AI literacy."[10] Third, and most importantly, AI is not only failing to bridge existing inequalities but risks deepening them. A study covering leading Asian universities revealed sharp disparities in the level of "AI readiness" between institutions.[11] These disparities are related to national and institutional contexts and could further exacerbate inequality in educational outcomes in the future. Systemic Preparedness, Policy and Infrastructure: Practical findings indicate that most higher education institutions are in the early stages of AI integration. According to WCET data, the majority of institutions have started using AI within the last two years, with the primary focus being on the teaching and learning process. A positive shift is observed in policy development: by 2025, the number of institutions with official AI policies or guidelines has significantly increased compared to 2023.[12] However, while training programs for staff and faculty are being organized, education aimed at improving student AI literacy is almost nonexistent. Discussion. The results of this study allow for a deeper analysis of the role of artificial intelligence in higher education within the context of existing literature and practice. Below, the theoretical and practical significance of the findings, as well as future prospects, are discussed. The Nature of Transformation: Evolutionary or Revolutionary? The findings suggest that AI is impacting education on two distinct levels. The first level involves increasing efficiency and automating existing processes (e.g., assessment, administrative tasks). This represents an evolutionary change. The second level, however, points to a shift in pedagogical paradigm: the role of the educator is transforming from a "knowledge disseminator" to a "designer of learning experiences." As Chinese experts emphasize, the primary focus in the classroom is now shifting from "knowledge transmission" to "cultivating abilities 852

and competencies."[13] This is a revolutionary change that necessitates a fundamental rethinking of educational philosophy. The New Division of Labor Between Human and Machine: Longitudinal study results indicate that students prefer to view AI as a "supplement" rather than a "substitute."[14] In their opinion, AI cannot fully replace humans, especially in areas requiring critical thinking, empathy, and complex ethical decision-making. This reinforces the concept of "human-AI collaboration." According to the model proposed by Wong and colleagues, graduates must now possess not only AI literacy but also human-distinctive capacities and ethical competencies.[15] The Inequality Issue: From Digital Divide to Cognitive Divide? One of the most critical issues requiring discussion is AI's impact on inequality. A "cognitive divide" is now being added to the traditional "digital divide" (access to internet and devices). This means that the skills necessary for effectively using AI (crafting accurate prompts, critically evaluating AI outputs, understanding ethical boundaries) are not equally distributed. Research shows that international students and male students use AI tools more frequently and often in a more permissive manner (interpreting permitted boundaries more broadly), while female students and local students are more cautious. This could, in turn, create new disparities in educational outcomes and subsequent success in the labor market between different demographic groups. The International Association of Universities also highlights this risk, concluding that "AI development could further exacerbate global inequalities in higher education." Conclusion. Artificial Intelligence presents a dual-faced reality for higher education: a tool for unprecedented democratization of knowledge and a source of complex ethical challenges. For institutions like the University of World Economy and Diplomacy, the path forward involves integrating AI into the curriculum not as a shortcut, but as a sophisticated tool for research and global analysis. The goal is to produce graduates who are not just users of AI, but its ethical masters. References: 1. UNESCO, Artificial Intelligence in Higher Education: A Guideline for Policy Makers, Paris, 2023. 2. Mukhiddinova, M. R., Digital Transformation of Uzbekistan's Higher Education System, UWED Journal, 2024. 3. EDUCAUSE, 2024 Horizon Report: Teaching and Learning Edition, EDUCAUSE Library, 2024. 4. Smith, J. & Brown, L., Generative AI and the Future of Academic Integrity, Journal of Educational Technology, Vol. 15, No. 2, 2025. 5. Luckin, R., Machine Learning and Human Intelligence: The Future of Education in the 21st Century, UCL Press, 2022. 6. WCET, The Changing Landscape of Artificial Intelligence in Higher Education, Boulder, 2025 7. WCET, The Changing Landscape of Artificial Intelligence in Higher Education, Boulder, 2025 8. Chen, L., Zhang, Y., and Wang, H., A bibliometric analysis of twenty years of research on artificial intelligence in education, ScienceDirect, Amsterdam, 2025 853

9. Thompson, P., and Roberts, L., Trust and Reliance on Generative AI Among UK Undergraduate Students, University of Bristol, Bristol, 2025 10. IAU, Global Survey on Artificial Intelligence in Higher Education: 2025 Report, Paris, 2025 11. Asian Universities Alliance, AI Readiness Assessment in Asian Higher Education Institutions, Beijing, 2025 12. WCET, The Changing Landscape of Artificial Intelligence in Higher Education, Boulder, 2025 13. Li, W., and Chen, X., Redefining Pedagogy in the Age of AI: A Chinese Perspective, Peking University, Beijing, 2025) 14. Martinez, A., and Kim, S., Longitudinal Study on Student Perceptions of AI Tools in Higher Education, Journal of Learning Analytics, Vancouver, 2025 15. Wong, J., Patel, R., and Garcia, M., AI Literacy and Beyond: A Framework for 21st Century Graduates, Harvard Educational Review, Cambridge, 2026 TIL BIRLIKLARI SEMANTIKASINING KOGNITIV TAHLILI Muratova Manzura, QarDU katta o‘qituvchisi muratovamanzura27@gmail.com Annotatsiya. Mazkur maqolada til birliklarining leksik ma’nosi semantik, pragmatik va sintaktik jihatlar birligi asosida tahlil qilinadi. Leksik ma’noning struktur (sintagmatik va paradigmatik), denotativ hamda emotiv komponentlari o‘zaro bog‘liqlikda yoritiladi. Shuningdek, leksik birliklarning tizimlilik xususiyati, ularning semantik guruhlarga birlashish tamoyillari hamda leksik-semantik maydon nazariyasining asosiy yo‘nalishlari ilmiy jihatdan asoslanadi. Kalit so‘zlar: denotativ ma’no, emotiv ma’no, leksik-semantik guruh, semantik maydon, konseptual tizim. Аннотация. В данной статье лексическое значение языковых единиц рассматривается в единстве семантического, прагматического и синтаксического аспектов. Освещаются структурные (синтагматические и парадигматические), денотативные и эмотивные компоненты лексического значения и их взаимосвязь. Анализируются принципы системности лексики, объединение слов в семантические группы, а также теоретические основы концепции лексико-семантического поля. Ключевые слова: денотативное значение, эмотивное значение, лексикосемантическая группа, семантическое поле, концептуальная система. Abstract. This article examines the lexical meaning of linguistic units within the unity of semantic, pragmatic, and syntactic aspects. The structural (syntagmatic and paradigmatic), denotative, and emotive components of lexical meaning are analyzed in their interrelation. The study explores the systematic nature of vocabulary, the principles of grouping lexical units into semantic classes, and the theoretical foundations of the lexical-semantic field approach. Keywords: denotative meaning, emotive meaning, lexical-semantic group, semantic field, conceptual system. Kirish. Til birliklarining leksik ma’nosi uning semantikasi, pragmatikasi va sintaktikasi bilan belgilanadigan murakkab tuzilmadir. Semantik ma’noda leksik ma’no tarkibida belgi va denotativ tomonlar farqlanadi. Pragmatik jihatga ko‘ra, so‘zning leksik ma’nosi ekspressiv-emotsional baho va konnotatsiyalarni o‘z ichiga oladi. Ma’noning sintaktik tomoni sintagmatik tarzda - ibora va gapdagi til birliklarining boshqa ma’nolari bilan bog‘lanish orqali va paradigmatik jihatdan sinonimik qator ichidagi pozitsiyasi bilan belgilanadi. 854

Ma’noning turli tomonlari "til birligining leksik ma’nosi - bir butunlik doirasida chambarchas bog‘liq va o‘zaro ta’sirga ega bo‘ladi", chunki "ular bir xil mohiyatni turli tomonlardan, turli yo‘llar bilan tavsiflaydilar."72 Tilshunoslikda leksik ma’noning quyidagi jihatlari ajratiladi: struktur (sintagmatik va paradigmatik), emotiv va denotativ. Struktur ma’no - "leksik birlikning til tizimidagi o‘rnini belgilovchi xususiyatlarining rasmiy xarakteristikasi xisoblanadi". Unda sintagmatik va paradigmatik belgilarning (birliklarning) bir-biriga munosabati ochiladi, shu munosabat bilan tarkibiy ma’noning ikkita kichik turi ajratiladi. Sintagmatik tuzilmali ma’no - leksik birliklarning matnda bir-biri bilan bir vaqtda bog‘lanishida ma’lum ketma-ketlikni tashkil etuvchi munosabatlarining o‘ziga xos xususiyatini namoyon etadi. U valentlik (tilda potensial moslik) yoki moslik (nutqda) deb ataladi. Paradigmatik struktur ma’no – leksik birliklarning semantik jihatdan o‘zaro bog‘langan va bir-biriga (paradigmatik jihatdan) qarama-qarshi qo‘yilgan munosabatlar sanaladi. U ma’no yoki differensial ma’no anglatuvchi deb nomlanadi.73 Emotiv ma’no – “stilistik buyoqlar va leksik birliklar yordamida ifodalangan maxsus lingvistik ifoda xisoblanadi." Bu leksik ma’noning baholovchi, emotsional, ekspressiv, stilistik xususiyatga ega bo‘lgan komponentidir. Emotiv ma’no pragmatik xususiyatga ega bo‘lib, so‘zlovchining til amaliyotida qo‘llaydigan so‘zlari va bu so‘zlarning mazmuniga munosabati, shuningdek, bunday so‘zlarning odamlarga tegishli ta’siri sifatida aniqlanadi.74 Denotativ ma’no “leksik birlikning predmet ma’nosi”dir. U til birligining voqelikning muayyan obyektiga, berilgan vaziyatga munosabati orqali aniqlanadi.75 Ma’lumki, tizimlilik tilning butun lug‘at tarkibiga asoslanadi, shu bilan birga so‘zlarning semantik guruhlarini tahlil qilish biz uchun dolzarbdir. M.M.Pokrovskiy ta’kidlaganidek, "so‘zlar va ularning ma’nolari bir-biridan alohida hayot kechirmaydi, balki bizning qalbimizda, ongimizdan qat’i nazar, turli guruhlarga birlashadi va guruhlash uchun asos asosiy ma’nodagi o‘xshashlik yoki to‘g‘ridan-to‘g‘ri qarama-qarshilikdir."76 Asosiy qisim. Leksik birliklarni tasniflashda so‘z birikmalarining nomenklaturasini o‘rnatish va ularning tipologiyasini aniqlash muammosi paydo bo‘ladi. So‘zlarning o‘zaro birikishi turli nomlarni oladi: "guruhlar", "maydonlar", "leksik", "leksiko-semantik", "semantik", "tematik", "ideografik", "kontseptual", "og‘zaki", "lingvistik."77 Hozirgacha tilning leksik-semantik tizimidagi so‘zlar o‘rtasidagi ko‘p o‘lchovli munosabatlarni ifodalashga xizmat qiladigan Новиков Л.А. Семантика русского языка. – Москва, Высшая школа, 1982. – 97 с. Новиков Л.А. Семантика русского языка. – Москва, Высшая школа, 1982. – 180 с. 74 Новиков Л.А. Семантика русского языка. – Москва, Высшая школа, 1982. – 183 с. 75 O’sha manba. – 101 с. 76 Покровский М.М. Семасиологические исследования в области античности. М.: КомКнига. 1986. - 82 77 Слесарева И.П. Проблемы описания и обучения русской лексике: учебник. – 3-е изд., перераб. – М.: Книжный Дом «ЛИБРОКОМ», 2010. – 49 с. 72 73 855

terminologiyada to‘liq bir xillik mavjud emas. Og‘zaki guruhlarga bo‘linish tamoyillari va atamalarning xilma-xilligi bo‘yicha turli nuqtai nazarlarga qaramasdan, atamalarning ma’lum bir tartibida ifodalangan birlashmalarning alohida turlarini farqlashning umumiy tendensiyasi ham mavjud. E.V.Kuznetsovaning ta’rifiga ko‘ra, "so‘z sinflari - leksik paradigmalarning namoyon bo‘lishining maksimal shaklidir. Ular so‘zlarning kichik yoki katta ko‘lamdagi bog‘lanishi bilan birikib, o‘zida semantik paradigmalarni namoyon qilib, ular tarkibiga kiradigan paradigmalarning og‘zaki oppozitsiyalarga qaraganda ancha hajmli va murakkabroqdir.78 L.M.Vasilyevning fikricha, bir so‘z turkumining semantik sinfi, paradigmatik tipdagi leksik soha bo‘lib, a’zolari umumiy ma’no bilan bog‘langan guruhlardan iborat bo‘ladi.79 Zamonaviy tilshunoslikda lisoniy birliklar tabiatini o‘rganish so‘zlarning semantik guruhlari doirasida tahlil qilinadi shuning uchun ham turlichaleksik-semantik guruhlar, tematik guruhlar, semantik paradigmalar, semantik, assotsiativ, kontseptual sohalar, o‘zgarmas va funksional sinflar kabi atamalar bilan yuritiladi. Zamonaviy semantik tadqiqotlarning vazifalaridan biri til birliklarining ma’nolarini, ularning turli bog‘lanishlarini, turli semantik sohalarning tarkibi va tuzilishini o‘rganish bo‘lib, ko‘plab tilshunoslar lingvistik semantikani maydon usuli bilan tahlil qilish samarali va istiqbollidir, deb hisoblaydilar.80 Leksik birliklarning semantikasini o‘rganishda semantik maydon muhim o‘rin tutadi. Umuman olganda semantik maydonda har bir so‘z faqat kontekstga mos ma’no bilan ishtirok etadi. Nutq jarayonida so‘zning ma’nosi kontekstdagi boshqa so‘zlar ma’nosi bilan anglanadi. Shuning uchun ham so‘zlarni xususiy qo‘llaganda fe’llarning semantikasini to‘liq anglash uchun semantik maydon ifoda kabilar yordamiga murojat etiladi. Tilshunoslikda semantik maydon "umumiy mazmun bilan (ba’zan umumiy rasmiy ko‘rsatkichlar bilan) birlashtirilgan va belgilangan hodisalarning kontseptual, predmetli yoki funksional o‘xshashligini aks ettiruvchi til birliklari to‘plami" deb ta’riflanadi. Kobcheva semantik maydoni o‘ziga xos xususiyatlari va turlarini aniqlaydi. Semantik maydonni quyidagi asosiy xususiyatlar bilan tavsiflanadi:81 1-jadval. Semantik maydonning tasniflanishi Кузнецова Э.В. Русская лексика как система: учеб. пособие. – Свердловск: Изд-во Уральского гос. ун-та, 1980. – 48 с. 79 Васильев Л.М. Теория семантических полей // Вопросы языкознания. – 1971. – № 5. – 126 с. 80 Васильев Л.М. Теория семантических полей // Вопросы языкознания. – 1971. – № 5. – 20 с. 81 Кобозева И.М. Лингвистическая семантика: учеб. пособие. – Москва, Эдиториал УРСС, 2000. –99 с. 78 856

Aksariyat asarlarda hozirda semantik soha va leksik-semantik maydon atamalari sinonim sifatida qo‘llanadi. I.P. Slesareva til birliklarining semantik yoki leksiko-semantik maydonlari LSGdan kengroq deb ataydi.82 Albatta, leksiksemantik maydon o‘z ichiga lug‘aviy ma’nodan tashqari ma’lum birlikning kontekstdagi semantikasi, boshqa turga tegishli bo‘lgan birliklar bilan munosabatga kirishishi boshqa birlik ma’nosi bilan birlashishi va tildagi ma’lum konseptual sathani aks ettiruvchi vositalarni qamrab oladi. Leksik – semantik maydonni konseptual tizimga aloqador jihatlari I.P. Slesareva ishlarida kuzatish mumkin. Muallif har qanday keng semantik maydon konseptual asosga ega, deb ta’kidlaydi.83 Ba’zi tadqiqotchilar semantik maydonni ohzaki nutq bilan bog‘laydilar. E.I. Zinovyevaning ta’kidlashicha, semantik maydon tushunchasi ancha kengroqdir va bunday maydonlarni turli ilmiy yo‘nalishlar ma’lumotlari yordamida tahlil qilish kerak, shu bilan birga, leksik-semantik soha umumiylik asosida birlashtirilgan birliklarning verbal qatorini - ular ifodalagan ma’no so‘z va so‘z birikmalarni qamrab olishi zarur.84 Mazkur qarash semantik maydonni pragmatik imkoniyatlarini ham ochib berishi mumkin. Verbal ifodada leksik-semantik guruh faollashuvi har doim ham kuzatilmaydi. Ammo verbal ifoda semantik maydonni hosil bo‘lishida muhim o‘rin tutadi. Semantik maydonda nutq turlari farqlanib, leksik birliklarning semantik maydondagi keng qamrovi, turli darajadagi so‘z guruhlarning faollashuvi yuzaga keladi. Semantik maydon qator leksik birliklardan tashqari boshqa lisoniy birliklarni birlashtiradi. Semantik maydonning paradigmatik xususiyatga ega bo‘lgan leksik-semantik bog‘lanishlar bevosita maqsadni ifodani yuzaga keltiradi. Bevosita fe’l guruhlari semantik maydoni R.M.Gaysina ishlarida ko‘rish mumkin. Muallif maydon munosabatlarini tavsiflab, unda markaziy o‘rinni fe’l so‘z turkumiga, maydonning chekka qismiga til birliklarini ifodalavchi sifat so‘z turkumi va turli vaziyatlarda ushbu til birliklariga nom beruvchi ot so‘z turkumiga ishora qiladi.85 Semantik sohalarni, shu jumladan nutqning turli qismlarining birliklarini tavsiflashga bunday yondashuv eng istiqbolli xisoblanadi, chunki nutqning turli qismlariga tegishli bo‘lgan, ammo umumiy kategorik-leksik semaga ega bo‘lgan birlik orasida nafaqat semantik bog‘lanishlar bir xil obyektiv vaziyatga, fe’llarning funksional jihatlariniyoritish uchun ham xizmat qiladi. Слесарева И.П. Проблемы описания и обучения русской лексике: учебник. – 3-е изд., перераб. – М.: Книжный Дом «ЛИБРОКОМ», 2010. – 49 с. 82 83 Слесарева И.П. Проблемы описания и обучения русской лексике: учебник. – 3-е изд., перераб. – М.: Книжный Дом «ЛИБРОКОМ», 2010. – 54 с. 84 Зиновьева Е. И. Лингвокультурология: от теории к практике. Учебник. – 2005.35 с. СПб. СПбГУ. 85 Гайсина Р.М. Лексико-семантическое поле глаголов отношения в современном русском языке. – Изд-во СГУ. - Саратов, 1981. – 196 с. 857

Foydalanilgan adabiyotlar: 1. Новиков Л.А. Семантика русского языка. – М.: Высшая школа, 1982. – 97 с. 2. Покровский М.М. Семасиологические исследования в области античности. М.: КомКнига. 1986. - 82 3. Слесарева И.П. Проблемы описания и обучения русской лексике: учебник. – 3-е изд., перераб. – М.: Книжный Дом «ЛИБРОКОМ», 2010. – 49 с. 4. Кузнецова Э.В. Русская лексика как система: учеб. пособие. – Свердловск: Издво Уральского гос. ун-та, 1980. – 48 с. 5. Васильев Л.М. Теория семантических полей // Вопросы языкознания. – 1971. – № 5. – 126 с. 6. Кобозева И.М. Лингвистическая семантика: учеб. пособие. – Москва, Эдиториал УРСС, 2000. –99 с. 7. Слесарева И.П. Проблемы описания и обучения русской лексике: учебник. – 3-е изд., перераб. – М.: Книжный Дом «ЛИБРОКОМ», 2010. – 49 с. 8. Слесарева И.П. Проблемы описания и обучения русской лексике: учебник. – 3-е изд., перераб. – М.: Книжный Дом «ЛИБРОКОМ», 2010. – 54 с. 9. Зиновьева Е.И. Лингвокультурология: от теории к практике. Учебник. – 2005.35 с. СПб. СПбГУ. 10. Гайсина Р.М. Лексико-семантическое поле глаголов отношения в современном русском языке. – Изд-во СГУ. - Саратов, 1981. – 196 с. RAQAMLI SAVODXONLIK VA SUN’IY INTELLEKT KOMPETENSIYALARINI SHAKLLANTIRISH Mustayev Ruslan Damirovich, mayor Abduvositov Husniddin Faxriddin o‘g‘li, kursant FarDU, abduvositovhusniddin86@gmail.com Annotatsiya. Mazkur maqolada raqamli savodxonlik va sun’iy intellekt kompetensiyalarini shakllantirishning nazariy asoslari, zamonaviy ta’lim tizimidagi o‘rni hamda ularni rivojlantirish mexanizmlari tahlil qilinadi. Raqamli transformatsiya sharoitida kadrlar tayyorlash jarayonida sun’iy intellekt bilan ishlash ko‘nikmalarini rivojlantirish zarurati asoslab beriladi. Shuningdek, ta’lim jarayoniga innovatsion pedagogik texnologiyalarni integratsiya qilish orqali samarali natijalarga erishish yo‘llari ko‘rsatib o‘tiladi. Kalit so‘zlar: raqamli savodxonlik, sun’iy intellekt, kompetensiya, innovatsion ta’lim, raqamli pedagogika, adaptiv ta’lim, axborot madaniyati. Аннотация. В данной статье анализируются теоретические основы формирования цифровой грамотности и компетенций в области искусственного интеллекта, их роль в современной системе образования, а также механизмы их развития. В условиях цифровой трансформации обосновывается необходимость развития навыков работы с искусственным интеллектом в процессе подготовки кадров. Кроме того, рассматриваются пути достижения эффективных результатов посредством интеграции инновационных педагогических технологий в образовательный процесс. Ключевые слова: цифровая грамотность, искусственный интеллект, компетенция, инновационное образование, цифровая педагогика, адаптивное обучение, информационная культура. Abstract. This article analyzes the theoretical foundations of developing digital literacy and artificial intelligence competencies, their role in the modern education system, and the mechanisms for their advancement. In the context of digital transformation, the necessity of developing skills for working with artificial intelligence in the process of professional training is 858