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6.
Ko‘p tilli qo‘llab-quvvatlash:
Ushbu tizimlar bir vaqtning o‘zida bir nechta tilni o‘rganish
imkoniyatini taqdim etadi, bu esa foydalanuvchilarga bir nechta tilda muloqot qilishni
osonlashtiradi.
7.
Maxsus talablar uchun moslashish:
Sun’iy intellekt nogiron foydalanuvchilar uchun
maxsus xususiyatlarni qo‘shish imkonini beradi. Masalan, ko‘zi ojizlar uchun ovozli tahlil yoki
nogironlar uchun maxsus interfeyslar mavjud bo‘lishi mumkin.
Kelajak istiqbollari
. Sun’iy intellekt asosida til o‘rgatish tizimlarining kelajagi juda
istiqbolli. Kelajakda ushbu tizimlarning yanada moslashuvchan va interaktiv bo‘lishi kutilmoqda.
Xususan, virtual haqiqat (VR) va kengaytirilgan haqiqat (AR) texnologiyalarining qo‘shilishi bilan
til o‘rganish yanada realistik va samarali bo‘lishi mumkin. Shuningdek, hissiy intellektni
rivojlantirish orqali sun’iy intellekt foydalanuvchi kayfiyatiga moslashib, yanada samarali ta’lim
taklif etishi kutilmoqda.
Foydalanilgan adabiyotlar:
1.
Turing, A. M. (1950). "Computing Machinery and Intelligence." Mind, 59(236), 433-460.
2.
Weizenbaum, J. (1966). "ELIZA - A Computer Program for the Study of Natural Language
Communication Between Man and Machine." Communications of the ACM, 9(1), 36-45.
3.
Russell, S., & Norvig, P. (2021). "Artificial Intelligence: A Modern Approach." Pearson.
4.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). "Deep Learning." MIT Press.
5.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). "Deep Learning." Nature, 521(7553), 436-444.
6.
Brown, T. et al. (2020). "Language Models are Few-Shot Learners." NeurIPS.
7.
Vaswani, A. et al. (2017). "Attention is All You Need." Advances in Neural Information
Processing Systems.
8.
Krashen, S. (1982). "Principles and Practice in Second Language Acquisition." Pergamon
Press.
9.
O’Reilly, R. C. & Munakata, Y. (2000). "Computational Explorations in Cognitive
Neuroscience." MIT Press.
10.
Bishop, C. M. (2006). "Pattern Recognition and Machine Learning." Springer.
INNOVATIONS IN MODERN LINGUISTICS AND LANGUAGE TEACHING
Ubaydullaeva Malika Barlikbaevna,
Master's student of KSU
Abstract:
Modern linguistic innovations have significantly transformed the landscape of language
teaching. The integration of digital technology, artificial intelligence, and data-driven
methodologies has enabled educators to enhance language acquisition processes. This article
explores these advancements, examining their implications for pedagogy, learner engagement,
and linguistic diversity. The study also considers challenges associated with implementing
innovative practices in linguistics and proposes pathways for further research.
Keywords:
Linguistics, language teaching, innovation, digital technology, pedagogy, artificial
intelligence.
The field of linguistics has undergone profound transformations in the 21st century, largely
due to technological advancements and interdisciplinary approaches. These innovations have
reshaped how languages are taught and learned, offering more personalized, efficient, and
125
engaging methods for diverse learners. The emergence of artificial intelligence (AI) tools, such as
language models and adaptive learning platforms, has played a pivotal role in modernizing
traditional pedagogical frameworks (Reinders & White, 2016).
This article investigates the major innovations in linguistics and their applications in
language teaching. The focus is on how these advancements address contemporary challenges such
as maintaining learner motivation, fostering intercultural competence, and preserving linguistic
diversity.
Artificial Intelligence and Natural Language Processing (NLP):
The integration of AI
and NLP has revolutionized language teaching. AI-driven platforms, such as Duolingo and Rosetta
Stone, employ algorithms that adapt to individual learner needs, providing customized feedback
and lesson plans (Xie et al., 2019). Additionally, NLP technologies have enabled the development
of sophisticated language translation tools and speech recognition systems, facilitating bilingual
and multilingual education (Yang, 2021).
The proliferation of digital resources, including MOOCs (Massive Open Online Courses)
and mobile applications, has democratized access to language learning. These platforms offer
interactive content, real-time assessments, and gamified activities to enhance user engagement
(Godwin-Jones, 2018). Moreover, virtual reality (VR) and augmented reality (AR) tools have
emerged as immersive solutions, enabling learners to practice languages in simulated
environments (Lan, 2020).
Big data analytics has provided educators with insights into learner behaviors and
preferences. By analyzing data from online platforms, instructors can identify trends, predict
challenges, and optimize teaching strategies (Chen et al., 2021). For example, predictive models
can suggest interventions for struggling learners, improving overall language proficiency
outcomes.
TBLT has gained prominence as an innovative approach emphasizing real-world
communication tasks. This method encourages learners to use language authentically, promoting
practical proficiency (Ellis, 2018).
Innovations in linguistic research have highlighted the importance of intercultural
competence in language teaching. Integrating cultural elements into lessons fosters an
understanding of sociolinguistic nuances, preparing learners for global communication (Byram,
2021).
Technological tools now support collaborative learning, allowing students to engage in
group activities across geographical boundaries. For instance, online discussion forums and video
conferencing platforms facilitate real-time language practice (Ware & O’Dowd, 2008).
Despite these advancements, challenges remain. Issues such as the digital divide, data
privacy, and resistance to technological adoption hinder the full realization of innovative practices.
Future research should address these barriers while exploring emerging technologies like quantum
computing and brain-computer interfaces in linguistics (Chowdhury, 2022).
Innovations in modern linguistics and language teaching have reshaped traditional
paradigms, offering new opportunities for learners and educators alike. By leveraging AI, digita l
resources, and data-driven methodologies, linguistic education can become more accessible,
engaging, and effective. However, addressing existing challenges is crucial to maximizing the
potential of these advancements.
126
References
1. Byram, M. (2021).
Teaching and assessing intercultural communicative competence
.
Multilingual Matters.
2. Chen, X., Zou, D., & Xie, H. (2021). Big data-driven language learning analytics: Theories,
methodologies, and challenges.
Journal of Educational Technology Development and Exchange
,
14(1), 45-61.
3. Chowdhury, G. G. (2022). Emerging technologies and future trends in linguistics.
Journal of
Language and Technology
, 39(2), 12-28.
4. Ellis, R. (2018).
Task-based language teaching: Theory and practice
. Cambridge University
Press.
5. Godwin-Jones, R. (2018). Using mobile devices for language learning: Potential and
pitfalls.
Language Learning & Technology
, 22(3), 4-19.
6. Lan, Y. J. (2020). Immersion, interaction, and experience-oriented learning: Bringing virtual
reality into language education.
Educational Technology Research and Development
, 68(4), 1659-
1683.
7. Reinders, H., & White, C. (2016). Twenty-first-century language teaching and learning: The
role of technology.
Language Teaching
, 49(4), 461-476.
8. Ware, P. D., & O’Dowd, R. (2008). Peer collaboration and cultural learning in online
intercultural exchanges.
Language Learning & Technology
, 12(1), 43-63.
9. Xie, H., Chu, H. C., Hwang, G. J., & Wang, C. C. (2019). Trends and development in
technology-enhanced language learning: A review of meta-analytic research.
Educational
Technology & Society
, 22(2), 43-56.
10. Yang, Y. (2021). The role of NLP in advancing multilingual education.
Applied Linguistics
Review
, 12(3), 389-412.
РОЛЬ СОПОСТАВИТЕЛЬНОЙ ГРАММАТИКИ В РАЗВИТИИ РЕЧИ СТУДЕНТОВ
НАЦИОНАЛЬНЫХ ГРУПП
Хакимова Гузалина,
Студентка университета бизнеса и науки
Научный консультант: Тургунова Сайера Ахмаджановна
Аннотация:
В данной статье рассматривается роль сравнительной грамматики в
развитии речевой компетенции студентов узбекских вузов. Анализируется влияние
различий в грамматическом строе русского и узбекского языков на формирование языковой
интуиции студентов. Подчеркивается значимость сопоставительного подхода в
обучении, который позволяет минимизировать интерференционные ошибки и
способствует осознанному усвоению грамматических конструкций.
Ключевые термины:
Сравнительная грамматика, Речевая компетенция, Интерференция,
Грамматические категории, Метод контрастивного анализа, Падежная система,
Глагольные формы, Лингвистическая интуиция
Современные процессы глобализации и академической мобильности ставят перед
высшими учебными заведениями задачу формирования у студентов не только
профессиональных, но и развитых коммуникативных навыков. Вузовское образование
