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Thus, the first, second, and third stages of reading skill formation take place at the child’s
individual pace, and these stages usually last around three to four years. At the first stage, every
element of the letter is tracked. At this stage, parents often say: “They know the letters but don’t
want to read.” They don’t refuse; they just can’t do it yet! Only by 9-10 years old do mechanisms
of voluntary regulation of activity and attention organization form. After all, to focus, to
differentiate, one must not be distracted and must concentrate.
References
1.
Анисимов В.М., Андреева К.Е., Сокорутова Л.В. Методика преподавания русского
языка в начальных классах. Якутск: 2001.
2.
Климанова Л. Обучение чтению в начальных классах. // Школа, 1999. № 18.
3.
Львов М.Р., Горецкий В.Г., Сосновская О.В. Методика преподавания русского языка в
начальных классах. – М.: 2000.
4.
Оморокова М.И. Совершенствование чтения младших школьников – М.: 1997.
5.
Светловская Н.Н. Методика обучения чтению: что это такое?// Начальная школа, 2005,
№2.
6.
Чечерина Н.Ю. Формирование навыка беглого чтения у детей старшего дошкольного
и младшего школьного возраста: рекомендации родителям. // Я-мама, 2006, №2.
7.
Безруких
М.М.
Формирование
навыков
чтения
и
письма
в процессе обучения детей. Российская государственная российская библиотека.
http://metodisty.narod.ru/vsd04.htm
THE EFFECT OF ARTIFICIAL INTELLIGENCE SYSTEMS SUCH AS ChatGPT ON
ENGLISH LANGUAGE AND TRANSLATION PROCESSES
Jalgʻasbaeva Tumaris Alimbay qizi,
Student of Nukus Innovation Institute
Abstract:
Over the past few years, artificial intelligence (AI) has transformed many domains,
including linguistics and translation. AI-driven systems such as ChatGPT have had a profound
effect on English language learning, content generation, and translation processes. This paper
discusses the effect of AI on translation quality, its benefits and limitations, and how it is
influencing contemporary English usage. Although AI-driven translation tools ensure more
efficiency and convenience, they still lag behind in understanding context, cultural nuances, and
accuracy. The essay also addresses the future of AI in translation and the destiny of human
translators.
Keywords:
Artificial Intelligence, ChatGPT, Machine Translation, English Language, Linguistics,
AI in Translation
The development of artificial intelligence at a rapid rate has transformed various aspects of
human life, including communication and language processing. Language models such as
ChatGPT, Google Translate, and DeepL, which are powered by AI, have revolutionized how
people learn and utilize English, and how translation processes are carried out. These language
models offer faster and more accessible translations, making communication across languages
more efficient. AI-driven translation tools also present challenges, particularly in addressing
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cultural sensitivities and contextual accuracy. This essay explores the influence of AI on the
English language and translation, highlighting its strengths and weaknesses.
Artificial intelligence has significantly improved machine translation with the shift from
rule-based to neural network-based systems. AI-powered translation software uses deep learning
algorithms to learn from vast amounts of linguistic data, enabling them to generate more natural
and contextually relevant translations.
Speed and Efficiency: AI translation tools can translate large volumes of text in a matter of
seconds, making them ideal for real-time communication and business use (Wu et al., 2016).
Cost-effectiveness: Compared to human translators, AI tools require minimal operational
costs, making translation services more cost-effective (Koehn & Knowles, 2017).
Continuous Learning: AI models like ChatGPT improve and learn over time through
machine learning, refining their accuracy and linguistic expertise (Vaswani et al., 2017).
Contextual Misinterpretation: AI cannot fully understand complex sentences, idioms, and
slang, thus providing wrong translations (Zhang et al., 2020).
Cultural Sensitivity Issues: Language carries cultural overtones that AI might not always be
able to understand, and thus it provides inappropriate or wrong translations (Toral & Way, 2018).
Dependence on Data: AI tools rely on existing linguistic data, which may not necessarily
reflect the most recent developments in language or regional dialects (Lakew et al., 2019).
AI-powered tools not only assist in translation but also influence the way people use and
learn English. These technologies have helped shape vocabulary, writing styles, and
communication patterns.
Personalized Learning: AI-powered platforms like Duolingo and Grammarly provide
personalized language lessons and writing suggestions (Choi, 2020).Instant Corrections: AI-
powered grammar and spell-checking tools enhance writing quality with immediate feedback
(Nagata, 2019).
New Words and Phrases: English is developed by AI through the introduction of new words
and phrases related to technology (Huang et al., 2021).
Simplification of Language: Machine translation software encourages the use of simpler
sentences to make machine reading easier (Post, 2018).
The future of AI in translation appears to be bright, with consistent innovations in deep
learning and natural language processing. Yet, human translators will continue to have an
important function to perform in terms of cultural accuracy, emotional tone, and preserving
context. AI is likely to augment human expertise, not supplant it, resulting in a hybrid model in
which both technologies and human intelligence collaborate together to produce translations of
better quality (Way, 2018).
Artificial intelligence has made significant strides in translation and English learning with
numerous advantages in speed, accessibility, and efficiency. It continues to lag, however, in
correctly interpreting context and cultural sensitivities. While AI will continue to develop and
enhance its capabilities, human translators remain essential to ensure translation quality. The
combination of AI and human strengths can lead the way to more effective and precise language
solutions in the future.
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References
1.Choi, J. (2020). Artificial Intelligence in Language Learning: Opportunities and Challenges.
Language & Education, 34(3), 211–225.
2.Huang, Y., Liu, C., & Wang, X. (2021). AI and Linguistic Evolution: How Artificial Intelligence
is Changing English Usage. Journal of Linguistic Studies, 29(2), 89–103.
3.Koehn, P., & Knowles, R. (2017). Six Challenges for Neural Machine Translation. In
Proceedings of the First Workshop on Neural Machine Translation, 28–39.
4.Lakew, S. M., Federico, M., Negri, M., & Turchi, M. (2019). On the Impact of Neural Machine
Translation on Professional Translation Productivity. Machine Translation Journal, 33(2), 73–92.
5.Nagata, R. (2019). AI-based Grammar Checking and Its Influence on Writing Practices.
Computers & Composition, 51, 37–52.
6.Post, M. (2018). A Call for Clarity in Machine Translation Output: The Role of Sentence
Simplification. ACL Conference Proceedings, 45–53.
7.Toral, A., & Way, A. (2018). What Level of Quality Can Neural Machine Translation Attain on
Literary Text?. Translation & Interpreting Studies, 13(2), 203–225.
8.Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., &
Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing
Systems, 30, 5998–6008.
9.Way, A. (2018). Quality versus Speed in Neural Machine Translation: A Trade-off?. Machine
Translation Journal, 32(1), 47–63.
10.Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y.,
Gao, Q., & Macherey, K. (2016). Google's Neural Machine Translation System: Bridging the Gap
between Human and Machine Translation. arXiv preprint arXiv:1609.08144.
11.Zhang, J., Wang, Y., & Liu, P. (2020). Challenges in AI-Based Translation: A Linguistic
Perspective. Journal of Artificial Intelligence Research, 65, 113–129.à
INNOVATIONS IN MODERN LINGUISTICS AND LANGUAGE TEACHING
Jaqsimuratova D.M,
Student of KSU
Scientific advisor: Seytniyazova Guljakhan
Abstract:
This article explores the concept of "resilience," looking at its definition, etymology, and
contextual usage across various fields, including psychology, literature, and everyday life.
Resilience is defined as the ability to recover from adversity and adapt to challenging
circumstances. The article delves into the historical evolution of the term, highlighting its
significance in both individual and collective experiences. Additionally, the article reflects on the
importance of cultivating resilience in today’s fast-paced and often unpredictable world, offering
insights into its relevance for personal growth and societal well-being.
Keywords:
resilience, adaptability, psychological resilience, etymology, cultural significance,
personal development, overcoming adversity, emotional strength, coping mechanisms, literature
and resilience.
