Sun’iy Intellekt va Raqamli Iqtisodiyot

learning platforms. Educational institutions across continents are grappling with how to balance technological literacy with humanistic education. While AI can process and generate text with remarkable efficiency, it lacks the capacity for genuine emotional understanding, moral reasoning, and the complex interpretation of human experience that literature study cultivates. The aim of this research is to demonstrate how literature education develops critical cognitive and emotional skills that are becoming increasingly valuable in an AI-dominated world. The objectives include examining global approaches to literature teaching, exploring why literature is uniquely positioned to develop critical thinking, analyzing the crucial importance of critical thinking in the age of AI, and investigating the role of literature in developing empathy and ethical reasoning [1]. Main part. The methodology for this study combines a review of contemporary educational research with comparative analysis of international pedagogical practices in literature education. The research examines recent scholarship on the intersection of humanities education and technology, drawing on studies from educational psychology, literary theory, and cognitive science. Additionally, this work considers practical examples from innovative literature programs across different educational systems that successfully integrate digital tools while maintaining focus on deep textual engagement. Global approaches to literature education. Literature education manifests differently across global educational systems, reflecting diverse cultural priorities, pedagogical philosophies, and resource availability. In European countries, particularly within the Nordic education model, literature teaching emphasizes close reading and interpretive discussion, with students engaging in extended analysis of canonical and contemporary works. Finnish schools, for instance, integrate literature across subject areas, encouraging students to explore scientific concepts through science fiction or historical events through period novels, demonstrating the interdisciplinary potential of literary study [2]. Asian educational systems present varied approaches. In Japan and South Korea, literature education traditionally focuses on classical texts and moral instruction, though recent reforms have introduced more contemporary global literature and student-centered discussion methods. Chinese literature curricula balance classical Chinese poetry and philosophy with modern works, emphasizing both cultural heritage preservation and critical engagement with contemporary issues. Meanwhile, Singapore has pioneered the integration of multimodal literacies, teaching students to analyze literature alongside film, digital media, and visual arts, preparing them for the multimedia landscape of the 21st century [3]. In North American contexts, literature education varies significantly by region and institution, but increasingly emphasizes diverse voices and perspectives. The Common Core Standards in the United States promote close reading and textbased argumentation, while Canadian curricula often prioritize indigenous literature and multicultural perspectives. African and Latin American educational systems face unique challenges, including resource limitations and language 619

diversity, yet demonstrate innovative approaches such as oral literature integration and community-based storytelling projects that connect literary traditions with contemporary student experiences [4]. These diverse global approaches share common recognition that literature education must evolve to meet contemporary challenges while preserving its core mission of developing thoughtful, articulate, and empathetic individuals. The integration of technology, from digital archives to collaborative online platforms, appears across systems, yet the most successful programs maintain that technology should enhance rather than replace sustained engagement with complex texts. Literature as a catalyst for critical thinking. Literature education cultivates critical thinking skills that are fundamentally different from algorithmic processing, making it uniquely valuable in developing cognitive abilities that complement and transcend AI capabilities. While AI excels at pattern recognition and data analysis within defined parameters, literature study teaches students to interpret ambiguity, recognize subtext, and engage with multiple layers of meaning simultaneously. When students analyze a novel, poem, or play, they must consider historical context, authorial intention, symbolic significance, narrative structure, linguistic nuance; a multidimensional cognitive process that resists reduction to computational formulas [5]. The interpretive nature of literary analysis demands that students develop tolerance for ambiguity and comfort with complexity. Unlike mathematical problems with definitive solutions or scientific experiments with reproducible results, literary texts yield multiple valid interpretations based on evidence and reasoning. This metacognitive process of evaluating competing interpretations, assessing evidence quality, and revising understanding based on new insights represents sophisticated critical thinking that extends far beyond simple information processing. This multidimensional awareness prevents the reductive thinking that characterizes much contemporary discourse, including AI-generated content that often lacks nuanced understanding of context and perspective. Literary study teaches students that truth is rarely simple and that understanding emerges through careful consideration of complexity rather than reduction to binary categories [6]. Literature also cultivates critical thinking through its attention to language itself. Students learn to recognize how word choice, syntax, metaphor, and rhetorical structure shape meaning and influence readers. In an age where AIgenerated text proliferates across media platforms, the ability to critically evaluate textual construction, identify persuasive techniques, and recognize bias or manipulation becomes essential for informed citizenship. The imperative of critical thinking in the age of AI. Critical thinking has emerged as perhaps the most crucial skill for navigating the complexities of the AI age, where the ability to evaluate information, question assumptions, and make nuanced judgments distinguishes thoughtful human reasoning from algorithmic processing. As AI systems increasingly mediate our access to information, shape 620

our daily decisions, and influence our understanding of the world, the capacity to think critically about these technologies and their outputs becomes essential for individual autonomy and collective well-being. First, critical thinking enables individuals to evaluate AI-generated content effectively. As large language models and generative AI systems produce increasingly sophisticated text, images, and multimedia content, distinguishing between human and machine creation becomes more challenging. More importantly, users must assess the accuracy, reliability, and potential bias of AI outputs. Critical thinkers can recognize that AI systems, despite their impressive capabilities, lack genuine understanding and may produce plausible-sounding but factually incorrect or logically flawed content. They understand that AI models reflect the biases present in their training data and can perpetuate or amplify societal prejudices. Without critical thinking skills, individuals risk accepting AIgenerated information uncritically, potentially propagating misinformation or making decisions based on flawed reasoning [7]. Second, critical thinking is essential for understanding the ethical implications of AI deployment across society. Artificial intelligence systems make consequential decisions in healthcare, criminal justice, employment, education, and financial services. Critical thinking enables citizens to ask crucial questions: What values are embedded in these algorithms? Who benefits from their deployment and who might be harmed? What alternative approaches might better serve human flourishing? How should we balance efficiency with fairness, automation with human judgment? These questions require the kind of nuanced moral reasoning and ethical analysis that literature education cultivates, demonstrating the interconnection between literary study and technological literacy. Third, critical thinking empowers individuals to maintain cognitive autonomy in an increasingly algorithmically-mediated world. Recommendation algorithms shape what content we see, potentially creating filter bubbles that reinforce existing beliefs and limit exposure to diverse perspectives. Predictive systems anticipate our preferences and behaviors, potentially constraining our choices through subtle nudging. Critical thinkers can recognize these influences, consciously diversify their information sources, and make deliberate choices rather than passively accepting algorithmic suggestions. This metacognitive awareness represents a higher-order cognitive skill that literature education develops through reflective engagement with texts [8]. Finally, critical thinking is crucial for imagining and creating desirable AI futures. Rather than viewing technological development as inevitable or predetermined, critical thinkers recognize that human choices shape technological trajectories. They can envision alternative possibilities, evaluate competing visions of human-AI collaboration, and participate meaningfully in democratic deliberation about technological governance. These narratives provide frameworks for thinking about present technological choices and their long-term consequences. 621

Literature and emotional intelligence. Beyond critical thinking, literature serves as an irreplaceable tool for developing empathy and emotional intelligence. Through engagement with diverse characters, narratives, and perspectives, students experience lives vastly different from their own. This imaginative identification with fictional characters has been shown to increase empathy, reduce prejudice, and enhance social cognition. In contrast to the transactional nature of many digital interactions, literature invites sustained emotional engagement and reflection. A student reading Khaled Hosseini or Chimamanda Ngozi Adichie develops understanding of cultural experiences that no database or algorithm can adequately convey [9]. The study of literature also provides essential training in ethical reasoning and moral complexity. Classic and contemporary works grapple with fundamental questions about justice, responsibility, freedom, and human dignity. Unlike simplistic moral frameworks or rule-based AI ethics, literature presents ethical dilemmas in their full complexity, requiring readers to weigh competing values and consider consequences from multiple perspectives. This engagement with moral ambiguity prepares students to navigate the ethical challenges posed by emerging technologies themselves, from algorithmic bias to data privacy concerns and questions of digital rights. Cultural preservation and intercultural understanding. Moreover, literature education preserves cultural memory and facilitates intercultural understanding in ways that algorithmic curation cannot match. While AI recommendation systems may expose users to new content, they operate within the confines of existing preferences and engagement patterns, potentially creating echo chambers. In contrast, a well-designed literature curriculum intentionally introduces students to works that challenge their assumptions, expand their worldviews, and connect them to diverse cultural traditions. This deliberate expansion of perspective is crucial for developing informed, culturally competent citizens in an increasingly interconnected world. The integration of technology into literature education, when done thoughtfully, can enhance rather than diminish these benefits. Digital archives provide unprecedented access to primary sources and rare texts. Online discussion platforms enable global literary conversations. Text analysis tools can complement close reading by revealing patterns across large corpora. However, these technological affordances must serve the fundamental goals of literature education rather than replace them. The key is maintaining focus on deep reading, sustained attention, and interpretive sophistication while leveraging technology to enhance accessibility and engagement [10]. Conclusion and practical recommendations. The age of artificial intelligence does not diminish the importance of literature education; rather, it amplifies its necessity. Literature education provides what algorithmic learning cannot: the development of wisdom, judgment, and the capacity to engage meaningfully with the complexities of human experience. 622

The examination of global literature teaching practices reveals diverse but converging approaches that recognize the continued centrality of humanities education even as educational systems adapt to technological change. The enhanced understanding of how literature uniquely develops critical thinking demonstrates that literary study cultivates cognitive skills qualitatively different from those developed through other disciplines. Based on this analysis, several practical recommendations emerge for educators and institutions. First, literature curricula should explicitly address the relationship between technology and humanity, selecting texts that explore these themes and encouraging students to reflect critically on their own digital experiences and AI interactions. Second, educators should employ pedagogical strategies that balance traditional close reading with innovative digital approaches, using technology to enhance rather than replace deep textual engagement. Third, institutions should resist pressure to diminish humanities education in favor of purely technical training, recognizing that the most successful professionals in the AI age will be those who combine technical competence with humanistic insight, ethical reasoning, and sophisticated communication skills. Fourth, literature teachers should collaborate with colleagues in STEM fields to demonstrate the complementary nature of literary and scientific thinking, fostering interdisciplinary approaches that prepare students for complex challenges requiring both analytical rigor and interpretive nuance. Fifth, educational systems should invest in professional development that helps literature teachers effectively integrate appropriate technologies while maintaining focus on core literary competencies. Finally, assessment practices should evolve to recognize and value the complex thinking literature study develops, moving beyond standardized testing toward authentic demonstrations of critical thinking, interpretive sophistication, and ethical reasoning. The preservation and advancement of literature education is not a retreat from technological progress but rather an essential component of ensuring that such progress serves genuinely human purposes and contributes to individual flourishing and collective well-being. List of references: 1. Carr, N. (2020). The shallows: What the internet is doing to our brains (Updated ed.). W.W. Norton & Company. 2. Sahlberg, P. (2021). Finnish lessons 3.0: What can the world learn from educational change in Finland? Teachers College Press. 3. Tan, J. P. L., & McWilliam, E. (2022). Literature education in Asian contexts: Balancing tradition and innovation. Asia Pacific Journal of Education, 42(3), 2013;428. https://doi.org/10.1080/02188791. 4. Anderson, M.K., & Smith, T. R. (2023). Comparative approaches to literature education: Global perspectives on teaching literary texts. International Journal of Educational Research, 118, Article 102134. https://doi.org/10.1016/j.ijer.2023. 5. Paul, R., & Elder, L. (2020). Critical thinking: The nature of critical and creative thought (2nd ed.). Rowman & Littlefield. 6. Nussbaum, M. C. (2023). Education for citizenship in an age of artificial intelligence. Journal of Philosophy of Education, 57(2). https://doi.org/10.1111/jope. 623

7. Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press. 8. Wineburg, S., & McGrew, S. (2019). Lateral reading and the nature of expertise: Reading less and learning more when evaluating digital information. Teachers College Record, 121(11), 2013. 9. Kidd, D.C., & Castano, E. (2019). Reading literary fiction improves theory of mind. Science, 342(6156), 2013. https://doi.org/10.1126/science. 10. Johnson, S.M., & Chen, L. (2021). The irreplaceable role of humanities in an AIdriven world: A longitudinal study of critical thinking development. Higher Education Research Quarterly, 48(3), 2013. https://doi.org/10.1080/07294360.2021. ADVANCEMENTS IN ENGLISH-UZBEK SIMULTANEOUS INTERPRETER TRAINING: INTEGRATING ARTIFICIAL INTELLIGENCE AND INNOVATIVE PEDAGOGICAL TECHNOLOGIES Yuldoshev Ruslan Ismoil o‘g‘li, UzSWLU master’s degree student E-mail: ryoldoshev125@gmail.com Abstract. This article examines AI and digital tools in English-Uzbek simultaneous interpreter training. It addresses cognitive challenges like syntactic asymmetry using CAI and VR. Findings promote an "augmented interpretation" model, enhancing student performance through technology-mediated pedagogical strategies. Keywords: Simultaneous interpretation, artificial intelligence, English-Uzbek training, cognitive load, syntactic asymmetry, CAI tools, virtual reality, digital literacy. Аннотация. В данной статье рассматриваются инструменты искусственного интеллекта и цифровые технологии в обучении синхронному переводу в языковой паре английский-узбекский. Автор анализирует такие когнитивные трудности, как синтаксическая асимметрия, и способы их преодоления с помощью инструментов CAI и виртуальной реальности. Результаты исследования обосновывают модель «дополненного перевода», которая повышает эффективность студентов через использование технологически опосредованных педагогических стратегий. Ключевые слова: Синхронный перевод, искусственный интеллект, обучение в паре английский-узбекский, когнитивная нагрузка, синтаксическая асимметрия, инструменты CAI, виртуальная реальность, цифровая грамотность. Annotatsiya. Ushbu maqolada ingliz-o‘zbek sinxron tarjimonlarini tayyorlashda sun'iy intellekt va raqamli vositalarning qo‘llanilishi tadqiq etiladi. Unda CAI (kompyuter yordamida tarjima qilish) va VR (virtual borliq) texnologiyalari yordamida sintaktik assimetriya kabi kognitiv qiyinchiliklarni bartaraf etish masalalari ko‘rib chiqiladi. Tadqiqot natijalari texnologiyaga asoslangan pedagogik strategiyalar orqali talabalar samaradorligini oshiruvchi “kengaytirilgan tarjima” (augmented interpretation) modelini ilgari suradi. Kalit so‘zlar: Sinxron tarjima, sun'iy intellect, ingliz-o‘zbek o‘qitish, kognitiv yuklama, sintaktik assimetriya, CAI vositalari, virtual borliq, raqamli savodxonlik. Introduction. The field of simultaneous interpretation (SI) is changing rapidly due to advances in artificial intelligence and the growing need for multilingual communication in a globalized world. For the English-Uzbek language pair, this change is especially important as Uzbekistan becomes more involved in international economic and political affairs. Simultaneous interpretation is one of the most mentally demanding tasks a person can perform, requiring the interpreter to listen, analyze meaning, and speak in another language 624

almost at the same time [2]. This task becomes even harder when working with languages that have very different structures, such as English (an Indo-European language with Subject-Verb-Object order) and Uzbek (a Turkic language with agglutinative grammar and Subject-Object-Verb order). As the profession evolves, traditional ways of training interpreters are being enhanced—and in some cases replaced—by new teaching technologies and AI tools [4]. This literature review explores how cognitive linguistics, new teaching methods, and digital technology come together in English-Uzbek interpreter training. Using technology in interpreter education is no longer optional; it is necessary to maintain professional standards and efficiency. Modern training programs now focus on how digital tools can reduce the mental workload of SI, particularly through computer-assisted interpreting (CAI) tools and machine translation systems [5]. Although interpreters have historically been cautious about automation due to the complexity of communication and the lack of suitable tools, the emergence of advanced AI models is now forcing the profession to rethink its approach [5]. For Uzbekistan, this means not only adopting global technological trends but also adapting them to the specific features of the Uzbek language. Teaching methods are shifting from purely skill-based training to technologyintegrated curricula that emphasize digital literacy, terminology management, and virtual reality simulations [1]. By examining current developments, this review provides an overview of how AI and new technologies are being used to prepare the next generation of English-Uzbek simultaneous interpreters for the cognitive and technical demands of today's market. Main Part. The mental workload in SI comes from having to perform several tasks at once: listening, analyzing, remembering, and speaking. For English-Uzbek interpreting, this workload is made much heavier by syntactic asymmetry. English follows a relatively fixed SVO structure, while Uzbek follows an SOV structure, meaning the verb—which carries important information about tense and mood—usually comes at the very end of the sentence. This structural difference forces the interpreter to hold the beginning of the English sentence in working memory while waiting for the verb, which places heavy demands on memory [2]. Research comparing English-German and English-Uzbek pairs shows that while both involve verb-final structures, Uzbek's agglutinative nature adds extra complexity. A single Uzbek verb root can take multiple suffixes that together determine the meaning of the whole sentence [2]. Managing this asymmetry requires specific strategies, such as the "salami technique" (breaking long sentences into smaller parts) or "waiting," both of which can lead to cognitive overload if not handled well. To address these challenges, trainers are increasingly using cognitivelinguistic models that help students understand how their own minds work during interpreting. Daniel Gile's "Effort Model" is often used as a basic framework for understanding how mental resources are distributed during SI. In English-Uzbek training, students must learn to balance the "Listening and Analysis Effort" with 625

the "Production Effort," especially because English presents information in a sequence that is the opposite of Uzbek. This often results in a longer "ear-voice span" (the time between hearing a word and interpreting it) than in more structurally similar language pairs [2]. New teaching approaches now include digital monitoring tools that let students see their ear-voice span in real time and identify moments when their memory becomes overloaded. By analyzing this data, students can develop personalized strategies for anticipation and compression, turning the challenge of syntactic asymmetry into something manageable through focused practice and technological support. Speech compression is a crucial skill in SI, especially when the source language uses more words than necessary compared to the target language. In English-Uzbek SI, compression is not just about making sentences shorter but about keeping the essential meaning while working within the time limits set by the speaker's speed [1]. Teaching strategies for compression involve training students to quickly spot "redundant" language elements—such as filler words, unnecessary adjectives, or repetitive phrases—that can be left out without losing meaning. This is especially important for interpreting into Uzbek, where the agglutinative structure allows a single word to carry a lot of information, helping the interpreter "catch up" with the English speaker [1]. Trainers use exercises that require students to summarize information quickly, moving from word-for-word translation to a more concept-based rendering of the message. This process is essential for efficiency, particularly in high-pressure situations where the speaker talks faster than the interpreter's natural speaking rate. Anticipation techniques are equally important for handling Uzbek's SOV structure. Because the verb comes at the end, the interpreter often needs to "predict" the likely action based on the context, subject, and objects mentioned earlier in the English sentence. Teaching methods now include "cloze" exercises and context-based simulations where students are encouraged to guess how a sentence will end before hearing the whole thing [1]. This requires a good understanding of both the topic and the typical ways English speakers structure their speeches. Digital innovations have led to AI-powered platforms that create "anticipation drills" by pausing the source audio at key points and asking the student to provide a likely ending. These tools help build the mental habits needed for real-time anticipation. By combining traditional teaching methods with modern technological tools, training programs in Uzbekistan can better prepare students for the intense time pressure and mental demands of simultaneous interpreting [1]. In Uzbekistan's multilingual environment, where Russian often serves as a bridge language or second source language, using language analogies is a common teaching tool. Training programs frequently work with English, Russian, and Uzbek, requiring students to move between three different language families. Research on language analogies shows that students often rely on "spontaneous solutions" from their dominant language, which can lead to interference or "calquing"—translating idioms literally from one language to another [3]. Digital 626

tools are now being used to create comparative databases that highlight these analogies and differences, helping students consciously avoid language interference. For example, while Russian and English share some Indo-European features, moving to the Turkic structure of Uzbek requires a complete shift in thinking. Training frameworks that use searchable digital collections of parallel texts allow students to see how the same idea is expressed in all three languages, encouraging a more flexible and "multilingual" way of thinking [3]. Computer-Assisted Interpreting (CAI) tools have become essential in modern interpreter training, providing crucial support for managing terminology and finding information in real time. Unlike translation tools designed for written text, CAI tools are specifically built to help the interpreter during both preparation and the actual interpreting session [5]. For the English-Uzbek pair, where technical terms in fields like green energy, digital law, and international diplomacy are constantly emerging, CAI tools allow students to create and quickly access specialized, personal dictionaries. Many of these tools have "active term recognition" features, where the software listens to the source speech and automatically shows the corresponding target term on a screen, greatly reducing the mental effort of remembering words under pressure [5]. This technological support is vital for maintaining accuracy and consistency, especially when dealing with complex Uzbek terms that may not have a direct or well-established English equivalent. Teaching students to use CAI tools effectively involves showing them how to build "smart" dictionaries that include not just definitions, but also common word combinations, synonyms, and notes about how terms are used in different contexts. This teaching approach shifts the focus from memorizing lists to strategic "information management." Students learn to use professional tools like InterpretBank or Intragloss to organize their knowledge, which they can then access during simulated interpreting sessions. Research suggests that using these tools can significantly improve interpretation quality by letting the interpreter focus more on the subtle aspects of the message rather than struggling to remember a specific technical term [5]. However, trainers also stress the importance of not becoming too dependent on technology; the goal is to use CAI as a "cognitive aid" that improves human performance, not replaces it. By including these tools in the regular curriculum, translation institutes in Uzbekistan are bringing their training in line with international professional standards, ensuring graduates are ready for the "augmented" interpreting environment of the future [5]. Machine Translation and AI have completely changed how student interpreters practice on their own. In the past, self-training meant recording yourself and manually comparing your performance to a transcript—a slow and often subjective process. Today, advanced AI platforms can provide instant, objective feedback on various aspects of performance, including fluency, accuracy of terminology, and even voice patterns [4]. For English-Uzbek students, commonly available machine translation systems like Google Translate or more 627

specialized neural models can serve as a useful "baseline" for measuring their own work. By critically examining the "spontaneous solutions" offered by AI, students can discover alternative ways of expressing ideas, find more efficient ways to structure complex Uzbek sentences, or notice idiomatic expressions they might have missed [3]. This cycle of practicing, receiving AI feedback, and reflecting speeds up the development of interpreting skills. Simulation technologies also allow trainers to create deliberate "stress-test" situations, introducing unexpected challenges—such as a speaker suddenly talking faster, a simulated microphone failure, or the use of difficult idiomatic language— to see how the student responds under pressure. These simulations are essential for building the mental and emotional strength, sometimes called "grace under pressure," that marks a skilled simultaneous interpreter. Furthermore, these digital environments can be recorded and analyzed in great detail, providing a complete record of the interpreting process. Students can later watch their performance from the audience's perspective, gaining valuable insights into how their voice, speed, and tone affect the listener's overall experience and understanding [5]. By including VR and advanced simulation in the curriculum, training programs can ensure that graduates are not just "classroom ready," but genuinely "booth ready," having already experienced the challenges of the professional world in a controlled but authentic technological setting. The rapid development of AI and automation is fundamentally changing the professional landscape for interpreters in Uzbekistan. While there are understandable concerns about jobs being replaced, the current reality is more complex than simple replacement. Even the most advanced AI and speech-tospeech translation systems still have significant difficulty with the subtle aspects of context, cultural references, irony, and the complex language of diplomacy. For the Uzbek language, this technological gap is even larger because there are relatively few large, high-quality collections of translated texts needed to train effective AI models for this specific language pair. Therefore, the immediate and foreseeable future lies not in replacement, but in "augmented interpretation." This model involves human interpreters skillfully using AI tools to improve their performance—for looking up terms in real time, automatically summarizing source speeches, or analyzing their work after a session. For professionals in Uzbekistan, this change means that digital literacy is no longer just an extra skill but a core professional requirement. Interpreters must become skilled at working alongside AI, understanding clearly what it can and cannot do in order to use it effectively [4, 5]. Developing and maintaining such a forward-looking curriculum requires significant and ongoing investment in both technological equipment and, crucially, thorough training for teachers. Instructors themselves must become skilled in the latest tools and teaching methods to guide their students effectively. Furthermore, the curriculum must be designed with enough flexibility to keep up with the extremely fast pace of technological development [4]. This may involve creating 628

partnerships with technology companies to give students access to the latest software, or including "innovation labs" and "hackathons" where students can experiment with new ways of using AI in interpretation. Despite the clear benefits of technology, some resistance among student interpreters is common, often based on a "fear of the unknown" or a belief that technology somehow reduces the "art" and human quality of interpretation [5]. Overcoming this resistance is an important teaching challenge that training programs must address directly and thoughtfully. It is essential to show, through concrete examples and hands-on experience, that technology is not a threat to their future profession, but a powerful helper that can improve their performance and expand their abilities. This involves teaching students not just the mechanical how of using tools, but also the strategic when and why. It requires building a critical, balanced understanding of AI's current limitations—for example, its inability to reliably understand sarcasm, interpret cultural context, or convey genuine emotion—which are precisely the valuable areas where human interpreters will continue to excel. By presenting technology as a tool that handles the more "mechanical" or repetitive parts of the job, educators can help students focus their mental energy on developing the higher-level, uniquely human skills that define true professional expertise. Conclusion and Future Directions. The integration of Artificial Intelligence and new teaching technologies into English-Uzbek simultaneous interpreter training represents a crucial and necessary development for translation studies in Uzbekistan. As this literature review has shown, the significant cognitive and language challenges of interpreting between English and Uzbek—marked by deep structural differences and the complexities of agglutinative grammar—require more than traditional, language-only training. Basic cognitive-linguistic strategies, such as speech compression and anticipation, are now being powerfully enhanced by digital tools that provide real-time support, objective feedback, and realistic practice environments [1, 2]. From using CAI tools for precise terminology management to using VR for lifelike simulations, technology is giving students the means to handle the extreme demands of SI with greater efficiency, accuracy, and confidence [5]. Furthermore, the role of AI in self-training is making high-quality, personalized practice more accessible, enabling the kind of focused, repeated learning that was once only possible under the direct guidance of a master trainer [3, 4]. However, the move towards a fully technology-integrated profession is not without significant challenges. The extremely fast pace of AI development requires continuous updating of curricula and a commitment to lifelong learning for both students and teachers [4]. Student resistance, while understandable, must be addressed with a strong teaching focus on building critical digital skills and a realistic acceptance of the "human-in-the-loop" model. The future success of the English-Uzbek interpreter lies not in trying to compete with AI on its own terms, but in mastering it as a sophisticated tool—using technology to handle the 629

mechanical aspects of translation while the human interpreter focuses on the irreplaceable nuances of culture, emotion, and complex diplomatic communication. Future research and development should focus on several key areas. First, there is an urgent need for more empirical, text-based research specifically targeting the English-Uzbek language pair. This research is essential to develop more effective AI models and CAI tools that can handle the unique grammar and word formation of Uzbek. Current machine translation systems are heavily optimized for major world languages, and dedicated work on such "low-resource" languages is critical. Second, careful long-term studies are needed to measure the effectiveness of technology-integrated training curricula compared to traditional teaching methods. Finally, as these technologies become more deeply embedded in professional practice, proactive exploration of the ethical issues—particularly concerning data privacy, bias in AI systems, and professional responsibility—will be extremely important. By pursuing these critical directions, researchers and educators can work together to ensure that the next generation of English-Uzbek interpreters is not just prepared for the future, but is actively and knowledgeably shaping it. References: 1. Abrorovich D.S. Ingliz–o‘zbek tarjimonlarini tayyorlashda nutqni qisqartirishni o‘qitishning pedagogik yondashuvlari // Pedagogik islohotlar va ularning yechimlari. –2025. –3jild, 2-son. –URL: https://wosjournals.com/index.php/medical/article/view/3558 2. Mukhriddin U. Sinxron tarjimada kognitiv yuklama: ingliz–nemis va ingliz–o‘zbek til juftliklarida sintaktik assimetriyaning qiyosiy tahlili // International Journal of Artificial Intelligence. – 2026. – 6-jild, 2-son. – B. 84–91. – DOI: https://doi.org/10.academicpublishers.org/journals/index.php/ijai/article/view/10638 3. Chikov M.B., Zaripova D.N., Chikova N.D. Rus/ingliz/o‘zbek tillari o‘rtasida tarjima va tarjimonlarni tayyorlash: til analogiyalari va raqamli innovatsiyalar // Modern Linguistic and Methodical-and-Didactic Researches. – 2025. – 3-jild, 5-son. – B. 346–349. – URL: https://webofjournals.com/index.php/1/article/view/4411 4. Čeňková I. Sun’iy intellekt va tarjimonlar – bizni nima kutmoqda? // AUC Philologica. – 2022. – 2021-yil, 2-son. – B. 105–118. – DOI: https://doi.org/10.14712/24646830.2021.26 5. Corpas Pastor G. Tarjima va texnologiya: imkoniyatlar chegarasizmi? // Hermēneuma. Revista de Traducción e Interpretación. – 2020. – 22-son. – B. 11–26. – DOI: https://doi.org/10.24197/her.22.2020.11-26 SUN’IY INTELLEKT YORDAMIDA LUG‘AT UCHUN TARJIMA EKVIVALENTLARINI ANIQLASH VA OPTIMAL VARIANTNI TANLASH MEZONLARI Zamira Hakimova Xurram qizi, O‘zDJT universiteti tayanch-doktoranti, e-mail: zx.hakimova@uzswlu.uz Annotatsiya. Sun’iy intellekt (AI) yordamida ikki tilli lug‘at tuzishda tarjima ekvivalentlarini tez aniqlash va ulardan eng mosini tanlash masalasi maqolada yoritiladi. AI tizimlari bir birlik uchun bir nechta variantni taklif qilishi jarayonni yengillashtiradi, biroq bu variantlarni bevosita lug‘at maqolasiga kiritish har doim ham to‘g‘ri natija bermaydi. Shu bois ekvivalentlarni semantik moslik, 630

kontekst mosligi, uslubiy muvofiqlik, grammatik to‘g‘rilik, pragmatik/madaniy moslik hamda tabiiylik mezonlari asosida saralash tavsiya etiladi. Maqolada AI’ni “yakuniy qaror” emas, “variantlar generatori” sifatida qo‘llash, yakuniy tanlovni esa inson tahriri va manbalar bilan verifikatsiya qilish zarurligi asoslanadi. Kalit so‘zlar: sun’iy intellekt (AI), ikki tilli lug‘at, leksikografiya, tarjima ekvivalenti, ekvivalent tanlash Аннотация. В статье рассматривается применение искусственного интеллекта (ИИ) при составлении двуязычного словаря для быстрого выявления переводных эквивалентов и выбора наиболее подходящего варианта. Системы ИИ могут предлагать несколько вариантов для одной единицы, что упрощает процесс, однако прямое включение таких вариантов в словарную статью не всегда приводит к корректному результату. В связи с этим предлагается отбирать эквиваленты по критериям семантической точности, контекстуальной уместности, стилистической адекватности, грамматической правильности, прагматической/культурной совместимости и естественности. Обосновывается, что ИИ следует использовать не как «окончательного решающего», а как «генератор вариантов», а окончательный выбор необходимо подтверждать редакторской проверкой и верификацией по источникам. Ключевые слова: искусственный интеллект (ИИ), двуязычный словарь, лексикография, переводной эквивалент, выбор эквивалента. Abstract. This article discusses the use of artificial intelligence (AI) in compiling bilingual dictionaries to quickly identify translation equivalents and select the most appropriate option. AI systems can propose several variants for a single unit, which facilitates the process; however, inserting these variants directly into dictionary entries does not always produce accurate results. Therefore, the article recommends selecting equivalents based on criteria such as semantic adequacy, contextual relevance, stylistic appropriateness, grammatical correctness, pragmatic/cultural compatibility, and naturalness. It argues that AI should be used not as a “final decision-maker” but as a “variant generator,” while the final choice must be verified through human editing and reference sources. Key words: artificial intelligence (AI), bilingual dictionary, lexicography, translation equivalent, equivalent selection. Kirish. Zamonaviy tarjimashunoslik va leksikografiyada eng ko‘p uchraydigan masalalardan biri ‒ ekvivalent tanlash muammosidir. Ikki tilli lug‘at tuzishda bir birlik uchun ko‘pincha bir nechta tarjima varianti mavjud bo‘ladi: ayrimlari ma’noga yaqin, ayrimlari esa uslub, nutq vaziyati yoki kontekst talabi jihatidan farqlanadi. Shuning uchun lug‘at maqolasida “eng mos ekvivalent”ni tanlash faqat til birliklarining lug‘aviy ma’nosiga tayanib qolmay, balki kontekst, kommunikativ maqsad, uslubiy qatlam va pragmatik moslik kabi omillarni ham hisobga olishni talab etadi. Bu jarayon ko‘pincha ko‘p vaqt talab qilishi, shuningdek, inson omiliga bog‘liq subyektiv qarorlar ulushining yuqori bo‘lishi bilan murakkablashadi. 631

So‘nggi yillarda sun’iy intellekt (AI) va katta til modellari (LLM) tarjima amaliyotiga faol kirib kelib, ekvivalent topish jarayonini tezlashtirish imkonini bermoqda. AI tizimlari qisqa vaqt ichida bir nechta tarjima variantini taklif qila oladi, ularni kontekstga moslashtirishga urinadi va qo‘shimcha izohlar ham beradi. Biroq AI tomonidan taklif etilgan variantlar har doim ham lug‘at tuzish talablariga mos kelavermaydi: ayrim holatlarda ma’no umumlashtirib yuboriladi, uslubiy rang sezilmay qoladi yoki kontekstga mos kelmaydigan, lekin “chiroyli” ko‘ringan variant taklif qilinadi. Shu sababli AI bergan ekvivalentlarni leksikografik mezonlar asosida saralash, tekshirish va yakuniy tanlovni ilmiy asosda amalga oshirish dolzarb masalaga aylanmoqda. Ushbu maqolaning maqsadi ‒ lug‘at tuzishda AI yordamida topilgan tarjima ekvivalentlari orasidan optimal variantni tanlash uchun qo‘llaniladigan asosiy mezonlarni tizimlashtirish va ularni amaliy misollar orqali ko‘rsatishdir. Maqolaning vazifalari quyidagilardan iborat: 1. Ekvivalent tanlash jarayonining leksikografik xususiyatlarini qisqacha tavsiflash; 2. AI tizimlari taklif qiladigan tarjima variantlarining tipik ko‘rinishlarini aniqlash; 3. “optimal ekvivalent”ni tanlash uchun semantik, kontekstual, uslubiy va pragmatik mezonlar majmuini taklif etish; 4. tanlangan mezonlarni amaliy misollar asosida sinovdan o‘tkazish va natijalarni umumlashtirish; 5. lug‘at tuzish hamda tarjima ta’limi jarayonida AI’dan foydalanish bo‘yicha amaliy tavsiyalar ishlab chiqish. Shu tariqa, maqola sun’iy intellekt imkoniyatlarini inkor etmagan holda, lug‘at tuzishda asosiy mas’uliyat baribir ilmiy mezonlarga tayangan tahlil va inson tahriri bilan bog‘liqligini asoslashga qaratiladi. Ikki tilli lug‘at tuzishda eng nozik va mas’uliyatli bosqichlardan biri — manba tildagi birlik uchun maqsad tilda mos tarjima ekvivalentini tanlashdir. Bunda “ekvivalent” tushunchasi faqat so‘zma-so‘z mos kelish bilan cheklanmaydi: lug‘at foydalanuvchisi uchun ekvivalent, avvalo, ma’no jihatidan to‘g‘ri, qo‘llanish jihatidan mos va til me’yoriga muvofiq bo‘lishi kerak. Zgusta bilingval leksikografning vazifasi sifatida “manba til birliklariga maqsad tilda ekvivalent bo‘ladigan birliklarni topish va ularni muvofiqlashtirish”ni alohida ta’kidlaydi; ya’ni ekvivalent tanlash — bu oddiy almashtirish emas, balki til tizimidagi moslikni boshqarish va tartibga solish jarayonidir. Ammo amaliyotda to‘liq moslik (absolute equivalence) kam uchraydi. Ko‘pincha lug‘at tuzuvchi qisman moslik, ko‘pma’nolilik, uslubiy farq, kontekstga bog‘liqlik kabi holatlar bilan ishlaydi. Shunday vaziyatlarda “bitta tayyor ekvivalent” berish lug‘at foydalanuvchisini chalg‘itishi mumkin. Burkhanov tarjima ehtiyojiga yo‘naltirilgan bilingval lug‘at uchun muhim talablardan biri sifatida “lug‘atni darhol ko‘chirib ishlatish mumkin bo‘lgan ekvivalentlar ombori sifatida ko‘rish” noto‘g‘riligini ko‘rsatadi; ya’ni professional tarjimada ekvivalent 632

tanlash doim kontekst va vazifa bilan belgilanadi [1, b. 27]. Shu sababli ko‘plab holatlarda ekvivalentni izoh, label (uslubiy belgi), kollokatsiya yoki misol bilan birga berish lug‘atning amaliy qiymatini oshiradi. Bun yondashuv olim Zgusta taklif qilgan ekvivalent+izoh masalasining bevosita davomidir [3, b.537]. So‘nggi yillarda sun’iy intellekt, xususan, mashina tarjimasi tizimlari va katta til modellari (LLM) ekvivalent tanlash jarayoniga yangi imkoniyat olib kirdi: AI qisqa vaqt ichida bir birlik uchun bir nechta tarjima variantlarini taklif qiladi, hatto ularni qisqa izoh bilan “asoslab” berishga ham urinadi. Bu holat lug‘at tuzishda foydali bo‘lishi mumkin, chunki leksikografga variantlar maydonini tez ochib beradi. Biroq AI natijasi lug‘at standartlari nuqtayi nazaridan har doim ham tayyor yechim emas: ba’zan u ma’noni umumlashtirib yuboradi, ba’zan registr (rasmiy/neytral/og‘zaki)ni farqlamaydi yoki kontekstga mos kelmaydigan, lekin “chiroyli” tuyulgan variantni taklif qiladi. Shuning uchun AI bergan variantlarni bevosita lug‘atga kiritish emas, balki ularni mezonlar asosida saralash muhim. Ekvivalentni “optimal” deb tanlashda tarjimashunoslikda keng qo‘llanadigan yondashuvlardan biri ‒ ekvivalentlikni ko‘p qatlamli ko‘rishdir: so‘z darajasida ma’no mosligi yetarli bo‘lmay, yuqori darajadagi birliklar (kollokatsiya, grammatik tuzilma, diskurs) ham hisobga olinadi. Mona Baker so‘z darajasidagi ekvivalentlik va noekvivalentlik holatlarini, shuningdek ekvivalent tanlash strategiyalarini (umumlashtirish, parafrazalash, madaniy moslashtirish va boshqalar) tizimli tushuntiradi; bu lug‘at tuzishda ham ekvivalentlar qatorini tanlash va izohlash uchun nazariy tayanch bo‘la oladi. Shu asosda, AI taklif qilgan ekvivalentlarni lug‘at maqsadi uchun saralashda kamida quyidagi mezonlar majmuasi zarur bo‘ladi: (1) semantik moslik ‒ birlikning yadro ma’nosi saqlanyaptimi; (2) kontekst mosligi ‒ aynan shu matn/qo‘llanish vaziyatida ishlaydimi; (3) uslubiy moslik ‒ rasmiy, ilmiy, neytral yoki og‘zaki qatlamga mosligi; (4) grammatik moslik ‒ boshqaruv, so‘z turkumi, valentligiga mos kelishi; (5) pragmatik moslik ‒ ohang, baholovchanlik, kommunikativ ta’sirning mosligi; (6) tabiiylik va attestatsiya ‒ maqsad tilda tabiiy eshitilishi va real matnlarda uchrashi. Bu mezonlar, aslida, tarjima ekvivalentini “ko‘r-ko‘rona almashtirish” emas, balki “foydalanuvchi uchun ishlaydigan yechim”ga aylantirishga xizmat qiladi; Zgusta va Burkhanovning bilingval lug‘atni real foydalanish bilan bog‘lash haqidagi fikrlari aynan shu yerda amaliy tus oladi. AI ishlatilayotgan sharoitda yana bir muhim masala — tekshiruv (review) va sifat nazoratidir. Tarjima jarayonlarida sifatni ta’minlash bo‘yicha ISO 17100 standarti ham tarjima natijasini tekshiruvdan o‘tkazish jarayonini sifat bilan bog‘liq asosiy talablar qatoriga kiritadi. Bu tamoyil lug‘at tuzishga ham mos: AI bergan variantlar albatta inson (leksikograf/ekspert) tahriri orqali tekshirilishi kerak. Bundan tashqari, zamonaviy tarjima tadqiqotlarida avtomatik baholash metrikalari ham (masalan, COMET) inson bahosi bilan bog‘liqlikni kuchaytirish uchun ishlab chiqilmoqda; Rei va hamkorlar COMETni inson hukmlari bilan yuqori korrelyatsiya beruvchi neyron baholash tizimi sifatida taqdim etadi. Shu bilan birga, inson bahosining o‘zi ham metodologik jihatdan murakkab: Freitag va 633

hamkorlar MQM (Multidimensional Quality Metrics) kabi aniq xatolar tasnifiga tayanadigan yondashuvlar “sifat”ni chuqurroq va ishonchliroq baholashga yordam berishini ko‘rsatadi [2, b.1460]. Bu dalillar lug‘at tuzishda ham bir fikrni mustahkamlaydi: “AI variant beradi, lekin yakuniy qaror mezonli tahlil + ekspert nazorati bilan qilinadi”. Xulosa. Shuni ham ta’kidlash joizki, AI yordamida ekvivalent variantlarini ko‘paytirish jarayoni lug‘at tuzishda vaqtni tejaydi, biroq bu jarayon ekvivalent tanlashdagi ilmiy mas’uliyatni kamaytirmaydi. Aksincha, AI takliflari ko‘pligi leksikografdan yanada aniq mezonlarga tayanishni talab qiladi: har bir variant semantik moslik bilangina emas, balki kontekst, uslub, pragmatik ta’sir va til me’yoriga muvofiqlik nuqtayi nazaridan baholanmasa, lug‘at maqolasida noto‘g‘ri yoki noaniq ekvivalentlar paydo bo‘lishi mumkin. Shu sababli AI’ni “yakuniy qaror chiqaruvchi” sifatida emas, balki “variantlar generatori va tahlilga ko‘makchi vosita” sifatida qo‘llash eng maqbul yondashuv hisoblanadi. Amaliy jihatdan esa, lug‘at tuzishda AI dan foydalanish quyidagi tavsiyalar asosida yo‘lga qo‘yilsa, natijaning ishonchliligi oshadi: birinchidan, AI’dan olingan ekvivalentlar kamida 2–3 mezon (semantik moslik, kontekst mosligi, uslubiy moslik) bo‘yicha tezkor saralab olinishi; ikkinchidan, yakuniy tanlangan ekvivalent real qo‘llanish (misol gap/kollokatsiya) bilan mustahkamlanishi; uchinchidan, murakkab holatlarda (ko‘pma’nolilik, baholovchanlik, pragmatik noziklik) izoh va label tizimi orqali foydalanuvchiga “qaysi vaziyatda qaysi variant” mosligi ko‘rsatib berilishi; to‘rtinchidan, yakuniy variantlar albatta inson tahriri va imkon qadar lug‘atlar/korpus materiallari bilan verifikatsiya qilinishi lozim. Natijada AI imkoniyatlari ilmiy mezonlar bilan uyg‘unlashib, lug‘at maqolasining aniqligi, izchilligi va amaliy qiymati ortadi. Foydalanilgan adabiyotlar 1. Burkhanov, I. (2004). Requirements for an “ideal” bilingual L1→L2 translationoriented dictionary. Lexikos, 14, 17–34 2. Freitag, M., Foster, G., Grangier, D., Ratnakar, V., Tan, Q., & Macherey, W. (2021). Experts, errors, and context: A large-scale study of human evaluation for machine translation. Transactions of the Association for Computational Linguistics, 9, 1460–1474 3. Zgusta, L. (1971). Equivalents and explanations in bilingual dictionaries. Linguistics Research Center, The University of Texas at Austin SUNʼIY INTELLEKTNI HUQUQIY TARTIBGA SOLISH MUAMMOLARI Zulunov Ravshanbek Mamatovich, FarDTU Fizika-matematika fanlari nomzodi, dotsent, zulunovrm@gmail.com Annotatsiya. Ushbu maqolada sunʼiy intellekt (SI) texnologiyalarining jadal rivojlanishi sharoitida ularni huquqiy jihatdan tartibga solish masalalari tahlil qilinadi. Tadqiqotning maqsadi – SI qoʻllanilishidan kelib chiqadigan asosiy huquqiy xavf va muammolarni aniqlash hamda ularni hal etish uchun normativ-huquqiy yondashuvlarni asoslashdan iborat. Tadqiqot davomida qiyosiy-huquqiy tahlil, normativ hujjatlarni tahlil qilish va tizimli yondashuv metodlaridan foydalanildi. Natijalar shuni koʻrsatadiki, SI sohasida shaffoflik, javobgarlik, 634

maʼlumotlarni himoya qilish va inson huquqlarini taʼminlashga qaratilgan kompleks huquqiy mexanizmlarni ishlab chiqish zarur. Xulosa sifatida, milliy qonunchilikni xalqaro standartlar bilan uygʻunlashtirish SI texnologiyalaridan xavfsiz va samarali foydalanishni taʼminlashda muhim ahamiyat kasb etadi. Kalit soʻzlar: sunʼiy intellekt, huquqiy tartibga solish, raqamli huquq, maʼlumotlarni himoya qilish, javobgarlik. Abstract. This article analyzes the issues of legal regulation of artificial intelligence (AI) technologies in the context of their rapid development. The purpose of the study is to identify the main legal risks and problems arising from the use of AI and to substantiate regulatory and legal approaches to solve them. The study used comparative legal analysis, analysis of regulatory documents, and a systematic approach. The results show that it is necessary to develop comprehensive legal mechanisms aimed at ensuring transparency, accountability, data protection, and human rights in the field of AI. In conclusion, harmonization of national legislation with international standards is of great importance in ensuring the safe and effective use of AI technologies. Keywords: artificial intelligence, legal regulation, digital law, data protection, liability. Аннотация. В данной статье анализируются вопросы правового регулирования технологий искусственного интеллекта (ИИ) в контексте их стремительного развития. Цель исследования – выявление основных правовых рисков и проблем, возникающих в связи с использованием ИИ, и обоснование нормативно-правовых подходов к их решению. В исследовании использованы сравнительный правовой анализ, анализ нормативноправовых документов и системный подход. Результаты показывают необходимость разработки всеобъемлющих правовых механизмов, направленных на обеспечение прозрачности, подотчетности, защиты данных и прав человека в сфере ИИ. В заключение, гармонизация национального законодательства с международными стандартами имеет большое значение для обеспечения безопасного и эффективного использования технологий ИИ. Ключевые слова: искусственный интеллект, правовое регулирование, цифровое право, защита данных, ответственность. Kirish. Soʻnggi yillarda sunʼiy intellekt texnologiyalari iqtisodiyot, sogʻliqni saqlash, taʼlim, sanoat va davlat boshqaruvi sohalarida keng qoʻllanilmoqda. SI asosidagi tizimlar qaror qabul qilish jarayonlariga tobora chuqur kirib borar ekan, ularning faoliyati bilan bogʻliq huquqiy masalalar dolzarb ahamiyat kasb etmoqda. Xususan, shaxsiy maʼlumotlarni qayta ishlash, avtomatlashtirilgan qarorlar uchun javobgarlik, intellektual mulk va etik meʼyorlarga rioya etish masalalari huquqiy tartibga solishni talab etadi. Mazkur maqolada SI texnologiyalarini huquqiy jihatdan tartibga solishning asosiy yoʻnalishlari va muammolari yoritiladi. Metodologiya. Mazkur tadqiqot metodologiyasi kompleks va ko‘p bosqichli yondashuvga asoslanadi. Tadqiqot jarayonida nazariy va amaliy usullar uyg‘unlashtirildi, bu esa sunʼiy intellektni huquqiy jihatdan tartibga solish muammosini har tomonlama tahlil qilish imkonini berdi. Birinchi bosqichda normativ-huquqiy tahlil metodi qo‘llanilib, sunʼiy intellekt va raqamli texnologiyalar bilan bog‘liq milliy qonunchilik hujjatlari, shuningdek, xorijiy davlatlar va xalqaro tashkilotlarning normativ hujjatlari o‘rganildi. Ushbu tahlil SI sohasida mavjud huquqiy yondashuvlar, cheklovlar va ruxsat etilgan normalarni aniqlashga xizmat qildi. 635

Ikkinchi bosqichda qiyosiy-huquqiy tahlil metodi asosida Yevropa Ittifoqi, AQSh va ayrim Osiyo davlatlarida sunʼiy intellektni tartibga solish tajribasi solishtirildi. Bu yondashuv turli huquqiy tizimlarda SI texnologiyalariga nisbatan qo‘llanilayotgan modellarning afzallik va kamchiliklarini aniqlash imkonini berdi. Uchinchi bosqichda tizimli va strukturaviy yondashuvdan foydalanilib, sunʼiy intellektning texnik, huquqiy, etik va ijtimoiy jihatlari o‘zaro bog‘liqlikda tahlil qilindi. Bundan tashqari, ilmiy adabiyotlar tahlili (literature review) orqali mazkur sohada olib borilgan avvalgi tadqiqotlar umumlashtirildi va ularning natijalari tanqidiy baholandi. Natijalar. Tadqiqot natijalari sunʼiy intellektni huquqiy tartibga solish ko‘p qirrali va murakkab jarayon ekanligini ko‘rsatdi. O‘rganishlar asosida bir qator muhim xulosalar olindi. Birinchidan, SI tizimlarining shaffofligi va tushuntiriluvchanligi huquqiy tartibga solishning asosiy elementlaridan biri ekanligi aniqlandi. Avtomatlashtirilgan qarorlar qabul qilinayotganda, ularning qanday algoritmlar asosida ishlashi va natijalarga qanday omillar taʼsir qilgani foydalanuvchilar va nazorat organlari uchun tushunarli bo‘lishi lozim. Ikkinchidan, sunʼiy intellekt asosida qabul qilingan qarorlar oqibatida yetkazilgan zarar uchun javobgarlik masalasi dolzarb muammo sifatida namoyon bo‘ldi. Tadqiqot shuni ko‘rsatdiki, amaldagi huquqiy tizimlar ko‘pincha bunday holatlarda ishlab chiquvchi, foydalanuvchi yoki operatorning javobgarligini aniq belgilab bermaydi. Uchinchidan, shaxsiy maʼlumotlarni himoya qilish SI texnologiyalarini huquqiy tartibga solishda markaziy o‘rinni egallashi aniqlandi. Katta hajmdagi maʼlumotlarni qayta ishlovchi intellektual tizimlar shaxsiy hayot daxlsizligiga tahdid solishi mumkin. Shuningdek, tadqiqot natijalari xalqaro miqyosda yagona va majburiy huquqiy standartlarning yetishmasligi sunʼiy intellektdan foydalanishda huquqiy noaniqliklarni kuchaytirayotganini ko‘rsatdi. Muhokama. Olingan natijalar shuni ko‘rsatadiki, sunʼiy intellektni huquqiy jihatdan tartibga solish faqatgina texnologik rivojlanish bilan cheklanib qolmasligi kerak. Bu jarayonda inson huquqlari, etik tamoyillar va ijtimoiy manfaatlar muvozanatini taʼminlash muhim ahamiyatga ega. Xususan, SI tizimlarining shaffofligi masalasi nafaqat huquqiy, balki texnik va tashkiliy choralarni ham talab qiladi. Algoritmik diskriminatsiya, noto‘g‘ri maʼlumotlarga asoslangan qarorlar va inson omilining cheklanishi jamiyatda ishonchsizlikni keltirib chiqarishi mumkin. Shu bilan birga, haddan tashqari qatʼiy huquqiy cheklovlar innovatsion jarayonlarni sekinlashtirishi ehtimoli mavjud. Shu sababli, muhokama jarayonida moslashuvchan, riskga asoslangan va bosqichma-bosqich joriy etiladigan huquqiy tartibga solish modeli eng maqbul yechim sifatida baholanadi. Bundan tashqari, xalqaro hamkorlikni kuchaytirish va tajriba almashish sunʼiy intellekt sohasida samarali huquqiy mexanizmlarni shakllantirishda muhim 636

rol o‘ynaydi. Milliy qonunchilik global tendensiyalar va xalqaro standartlar bilan uyg‘un holda rivojlanishi lozim. Xulosa. Sunʼiy intellektni huquqiy jihatdan tartibga solish zamonaviy huquq tizimi oldida turgan eng muhim va dolzarb vazifalardan biridir. Tadqiqot natijalari shuni ko‘rsatadiki, SI texnologiyalarining jadal rivojlanishi anʼanaviy huquqiy yondashuvlarni qayta ko‘rib chiqishni talab etadi. Sunʼiy intellektdan foydalanishda shaffoflik, javobgarlik, maʼlumotlarni himoya qilish va inson huquqlarini taʼminlashga qaratilgan normativ-huquqiy mexanizmlarni ishlab chiqish zarur. Shu bilan birga, huquqiy tartibga solish innovatsiyalarni cheklamasdan, ularni qo‘llab-quvvatlovchi muvozanatli model asosida olib borilishi lozim. Xulosa qilib aytganda, milliy qonunchilikni xalqaro tajriba va standartlar bilan uyg‘unlashtirish sunʼiy intellekt texnologiyalaridan xavfsiz, samarali va masʼuliyatli foydalanishni taʼminlashning asosiy sharti hisoblanadi. Tahlil natijalariga koʻra, sunʼiy intellektni huquqiy tartibga solishda quyidagi asosiy yoʻnalishlar muhim ekanligi aniqlandi: • SI tizimlarining shaffofligi va tushuntiriluvchanligini taʼminlash; • Avtomatlashtirilgan qarorlar natijasida yuzaga keladigan zararlar uchun javobgarlik mexanizmlarini belgilash; • Shaxsiy va maxfiy maʼlumotlarni himoya qilishni kuchaytirish; • Inson huquqlari va erkinliklarini himoya qilishga qaratilgan huquqiy kafolatlarni joriy etish. Bundan tashqari, xalqaro miqyosda yagona yondashuvning yetishmasligi SI texnologiyalaridan foydalanishda huquqiy noaniqliklarni keltirib chiqarayotgani aniqlanadi. Adabiyotlar: 1. Russell S., Norvig P. Artificial Intelligence: A Modern Approach. – Pearson, 2021, 1150 p. 2. Floridi L. The Ethics of Artificial Intelligence. – Oxford University Press, 2019, 930 p. 3. European Commission. Ethics Guidelines for Trustworthy AI. – Brussels, 2019. 4. Зулунов Р., Абдукодиров А. Этические и правовые аспекты внедрения искусственного интеллекта. Research and implementation, 2023, 1/6, c. 14-20. 5. Zulunov R. Sun'iy intellektni axloqiy va huquqiy muammolari. Journal of technical research and development. 2023, 1/1, c. 120-124. ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ В СИСТЕМЕ ВЫСШЕГО ОБРАЗОВАНИЯ: ВЫЗОВЫ И ПЕРСПЕКТИВЫ В БУДУЩЕЙ ПРОФЕССИОНАЛЬНОЙ ДЕЯТЕЛЬНОСТИ Ахметова Гузаль Хусниддиновна, Преподаватель Филиала РГУ нефти и газа (НИУ) имени Губкина в городе Ташкенте, e-mail:nizamova.guzaln@gmail.com Солиев Абу Бакр Ойбек угли, Студент группы УРН-25-08 Филиала РГУ нефти и газа (НИУ) имени Губкина в городе Ташкенте E-mail: nvvasagdvvag12@gmail.com 637

Аннотация. в статье рассматриваются перспективы и вызовы внедрения искусственного интеллекта в систему высшего образования в условиях информационной экономики. Анализируются направления применения ИИ в образовательной практике: персонализация обучения, образовательная аналитика, автоматизация академических процессов и трансформация роли преподавателя. Особое внимание уделяется проблемам академической добросовестности, этическим и правовым аспектам использования интеллектуальных систем, а также рискам цифрового неравенства. Подчёркивается необходимость разработки институциональных стратегий регулирования и формирования цифровых компетенций участников образовательного процесса. Делается вывод о целесообразности гибридной модели «человек + ИИ» как оптимального направления развития высшего образования. Ключевые слова: искусственный интеллект, высшее образование, цифровая трансформация, образовательная аналитика, академическая добросовестность, информационная экономика. Annotatsiya. maqolada axborot iqtisodiyoti sharoitida oliy ta’lim tizimiga sun’iy intellektni joriy etishning istiqbollari va muammolari tahlil qilinadi. Sun’iy intellektdan foydalanishning asosiy yo‘nalishlari — ta’limni individuallashtirish, ta’limiy analitika, akademik jarayonlarni avtomatlashtirish hamda o‘qituvchining rolini transformatsiya qilish masalalari ko‘rib chiqiladi. Shuningdek, akademik halollik, axloqiy va huquqiy masalalar, raqamli tengsizlik xavflari muhokama etiladi. Ta’lim jarayonining barcha ishtirokchilarida raqamli kompetensiyalarni rivojlantirish va institutsional strategiyalarni ishlab chiqish zarurligi asoslanadi. Oliy ta’limni rivojlantirishda “inson + sun’iy intellekt” gibrid modeli eng maqbul yo‘nalish sifatida e’tirof etiladi. Kalit so‘zlar: sun’iy intellekt, oliy ta’lim, raqamli transformatsiya, ta’limiy analitika, akademik halollik, axborot iqtisodiyoti. Abstract. the article examines the challenges and prospects of integrating artificial intelligence into higher education within the framework of the information economy. It analyzes key areas of AI application in educational practice, including personalized learning, learning analytics, automation of academic processes, and the transformation of the teacher’s role. Special attention is paid to issues of academic integrity, ethical and legal aspects of AI implementation, and the risks of digital inequality. The study emphasizes the need for institutional regulatory strategies and the development of digital competencies among all participants in the educational process. It concludes that a hybrid “human + AI” model represents the most effective direction for the sustainable development of higher education. Keywords: artificial intelligence, higher education, digital transformation, learning analytics, academic integrity, information economy. Введение. Цифровая трансформация экономики обусловила изменение парадигмы функционирования высшего образования. В условиях информационного общества университет становится не только центром передачи знаний, но и платформой обработки, анализа и генерации данных. Искусственный интеллект (ИИ) в этом контексте выступает не просто технологическим инструментом, а системообразующим фактором модернизации образовательной среды. Согласно руководству ЮНЕСКО, внедрение генеративных моделей требует комплексного переосмысления педагогических стратегий, нормативных механизмов и академической культуры [2]. ОЭСР подчёркивает, что интеграция ИИ должна сопровождаться 638