Sun’iy Intellekt va Raqamli Iqtisodiyot

THE IMPACT OF ARTIFICIAL INTELLIGENCE ON EDUCATION: EVOLUTION OR COLLAPSE ? Boyturayev Fakhriyor Husan o‘g‘li, faxriyotboyturayev4@gamil.com O‘sarov Humoyun Sherbek o‘g‘li, sultonfotih2007@gmail.com Undergraduate students, International Relations Superviser: Munirakhon Mukhitdinova, PhD, Associate Professor University of World Economy and Diplomacy, m.mukhitdinova@list.ru Annotation. This paper examines the dual impact of artificial intelligence on contemporary education systems, investigating whether AI functions as a genuine catalyst for pedagogical development or as a mechanism that leads students toward cognitive passivity and intellectual dependence. According to research, adaptive AI systems are shown to construct personalised learning trajectories for individual students, reduce teachers’ administrative workloads by an average of 40 percent, and raise measurable student engagement by 25 percent. In Uzbekistan specifically, AI-enabled distance learning platforms have increased rural student retention by 28 percent, providing a powerful equity argument for accelerated adoption. At the same time, the data document serious deterioration: independent writing quality has declined by 18 percent among AI-exposed cohorts, plagiarism has surged 62 percent globally and 133 percent at Uzbek urban universities, and unaided recall performance among heavy AI users has dropped 15 percent. Keywords: Artificial intelligence in education, Personalised learning, Cognitive offloading, Intellectual prosthetic, AI pedagogy, Academic integrity, Adaptive learning systems, Digital divide Teacher role transformation, Cognitive load theory. Аннотация. В данной статье рассматривается двойное воздействие искусственного интеллекта на современные системы образования, исследуется вопрос о том, выступает ли ИИ в качестве подлинного катализатора педагогического развития или же как механизм, ведущий учащихся к когнитивной пассивности и интеллектуальной зависимости. Согласно исследованиям показано, что адаптивные системы ИИ создают персонализированные траектории обучения для отдельных учащихся, снижают административную нагрузку на учителей в среднем на 40 процентов и повышают измеримую вовлеченность учащихся на 25 процентов. В частности, в Узбекистане платформы дистанционного обучения с поддержкой ИИ увеличили удержание учащихся из сельской местности на 28 процентов, что является веским аргументом в пользу ускоренного внедрения в интересах равенства. В то же время данные свидетельствуют о серьезном ухудшении: качество самостоятельного письма снизилось на 18 процентов среди групп, подвергшихся воздействию ИИ, уровень плагиата вырос на 62 процента в глобальном масштабе и на 133 процента в городских университетах Узбекистана, а показатели самостоятельного запоминания среди активных пользователей ИИ упали на 15 процентов. Ключевые слова: искусственный интеллект в образовании, персонализированное обучение, когнитивная разгрузка, интеллектуальный протез, педагогика ИИ, кадемическая добросовестность, адаптивные обучающие системы, цифровое неравенство, трансформация роли учителя, теория когнитивной нагрузки. Annotatsiya. Ushbu maqola sun'iy intellektning zamonaviy ta'lim tizimlariga ikki tomonlama ta'sirini o'rganadi, sun'iy intellekt pedagogik rivojlanish uchun haqiqiy katalizator sifatida ishlaydimi yoki o'quvchilarni kognitiv passivlik va intellektual qaramlikka olib boradigan mexanizm sifatida ishlaydimi, degan savolga javob beradi. Tadqiqotlarga ko‘ra, 959

Adaptiv AI tizimlari individual o'quvchilar uchun shaxsiylashtirilgan o'quv traektoriyalarini yaratishi, o'qituvchilarning ma'muriy ish yukini o'rtacha 40 foizga kamaytirishi va o'lchanadigan talabalarning ishtirokini 25 foizga oshirishi ko'rsatilgan. Xususan, O'zbekistonda AI yordamida masofaviy o'qitish platformalari qishloq o'quvchilarining o'qishni davom ettirishini 28 foizga oshirdi, bu esa tezlashtirilgan qabul qilish uchun kuchli tenglik argumentini taqdim etdi. Shu bilan birga, ma'lumotlar jiddiy yomonlashuvni hujjatlashtiradi: sun'iy intellektga duchor bo'lgan kohortlar orasida mustaqil yozish sifati 18 foizga pasaygan, plagiat global miqyosda 62 foizga va O'zbekiston shahar universitetlarida 133 foizga oshgan va sun'iy intellektdan foydalanuvchilarning yordamsiz eslab qolish ko'rsatkichlari 15 foizga pasaygan. Kalit so‘zlar: ta’limda sun’iy intellekt, shaxsiylashtirilgan ta’lim, kognitiv yengillashuv, intellektual protez, SI pedagogikasi, akademik halollik, moslashuvchan o‘quv tizimlari, raqamli tafovut, o‘qituvchi rolining transformatsiyasi, kognitiv yuklama nazariyasi. Introduction. The integration of artificial intelligence into educational systems represents one of the most consequential and contested developments in the history of modern pedagogy. It is not simply the arrival of a new technological tool. It is the emergence of a technology that operates at the cognitive register that formal education has always treated as its core territory: reasoning, synthesis, argumentation, and independent intellectual construction. Understanding the nature and scale of that challenge requires more than enthusiasm or caution in isolation. It requires a careful, empirically grounded analysis of what AI is already doing to educational outcomes for better and for worse and what institutional choices will determine which trajectory prevails. Consider a concrete scene that captures the stakes. A teacher in Tashkent, marking undergraduate essays in the spring of 2024, notices something unusual: the writing is smoother than expected. Sentences that normally stumble flow with unexpected fluency. Arguments that typically meander converge with unusual precision. She runs three of the most impressive submissions through an AI detection tool. Two come back flagged. the third is the one that impressed her most scores the highest of all for AI-generated content. She assigns failing grades to all three. The students protest: they used AI “only for editing.” She has no way to verify that claim. Neither does anyone else. Neither, in the current state of institutional policy, does the institution she works for.[6] This small scene is not primarily about academic dishonesty, though it certainly involves that. What it illustrates is a far more fundamental challenge: the century-old architecture of educational assessment is the essay, the take-home assignment, the independent research report was built on the assumption that written output was a reliable proxy for a student’s cognitive process. AI has severed that relationship, not partially or occasionally, but systematically and at scale. Institutions are now being forced to confront what they were actually building when they assigned essays: the document, or the capacity to reason that the document was supposed to represent?[2] The trajectory of educational technology across the past four decades helps contextualise the current moment without naturalising it. Chalk-and-talk instruction gave way to computers in the 1980s, computers yielded to the internet 960

in the 1990s, the internet was supplemented by e-learning platforms and blended models through the 2000s and 2010s. Each transition was met with a characteristic double response: genuine excitement about expanded access and scale, and genuine anxiety about what depth or quality would be sacrificed. Both reactions proved partially justified in each case. The gains were real, so were the losses and over time, institutions adapted their practices to preserve what mattered most.[4] Artificial intelligence, however, represents a qualitatively different kind of break from that trajectory. The calculator automated a procedure is arithmetic. The internet automated access to a resource is information. Generative AI automates a process: the synthesis and construction of text, argument, and apparent reasoning itself. These are not peripheral activities in formal education. They are, in most disciplines and at most levels, precisely the activities that formal education has historically claimed to be developing. When AI can produce in seconds an essay that would require days of genuine intellectual struggle from most students. When that essay is grammatically and rhetorically plausible enough to confuse experienced teachers, the challenge to existing pedagogy is not incremental. It is structural.[7] The central hypothesis of this paper reflects that structural character. Artificial intelligence, deliberately integrated into a redesigned pedagogical environment, represents an evolutionary advance for education: an intellectual prosthetic that extends human cognitive capacity by managing routine cognitive friction and enabling learners to work at higher levels of synthesis and creativity. But the same AI, inserted without modification into unchanged assessment structures and without institutional intentionality, is already producing in measurable, documented ways the cognitive passivity, declining independence, and institutional norm collapse that its critics feared. These are not future risks. They are present realities, visible in PISA data, UNESCO reports, Turnitin statistics, and cognitive performance research. The technology is the same in both scenarios. What differs is the institutional context in which it is deployed. The paper draws on two recent Uzbekistan-specific studies Murtozaqulova (2025) on AI integration in higher education and Abdunabiyeva and Abdulatipov (2025) on pedagogical implications alongside major international datasets from PISA, UNESCO, Turnitin, NeuroEd, and leading AIED research published between 2020 and 2025. Section 2 describes the methodology. Section 3 presents the empirical evidence, positive and negative, with detailed reference to Uzbekistan’s specific situation. Section 4 develops four analytical arguments that together map the evolution-versus-collapse divide. Section 5 offers a concluding thesis and concrete, actionable recommendations directed primarily at Uzbekistan and comparable developing educational contexts. Method. This study employs a qualitative research design that combines a structured, systematic literature review with a comparative historical analysis of educational technology transitions. No new primary empirical data were collected. 961

The research addresses two primary questions. First, what does the available empirical evidence demonstrate about AI’s effects on measurable educational outcomes, including learning gains, teacher efficiency, academic integrity, and cognitive performance? Second, under what institutional conditions do those effects tend toward development rather than deterioration? The framing of these questions as a productive tension, rather than as a binary choice between enthusiasm and rejection, shapes the paper’s analytical structure throughout. The systematic literature review covered peer-reviewed publications in the AIED field published between January 2020 and December 2025. Databases searched included Scopus, Web of Science, and ERIC, supplemented by grey literature from UNESCO, OECD, and major EdTech research organisations. Inclusion criteria required that studies report empirical findings on at least one of the following: learning outcome changes under AI-mediated instruction, teacher workload patterns in AI-integrated environments, academic integrity and plagiarism trends or measurable cognitive performance metrics, including recall, sustained attention, and independent writing quality. The quantitative evidence base for this paper draws from the following primary sources: PISA 2022 and 2025 student performance surveys and teacher workload reports[9]. UNESCO annual education reports covering 2021 through 2024, with particular attention to AI adoption rates and equity indicators[12].Turnitin’s 2024 Global Academic Integrity Report, covering academic dishonesty patterns across 62 countries[11]. NeuroEd’s 2025 longitudinal cognitive performance tracking study, examining recall, sustained attention, and metacognitive awareness across AI-usage cohorts [8]. The Ministry of Education of Uzbekistan’s 2025 country report on digital technology integration and the institutional research of Murtozaqulova (2025)[6] and Abdunabiyeva and Abdulatipov (2025)[1], which provide the Uzbekistan-specific granularity that grounds the paper’s regional analysis. Three prior educational technology transitions were selected as comparative anchors: the introduction of affordable calculators into mathematics education in the 1970s and 1980s. The integration of the internet and digital information retrieval into research pedagogy in the 1990s and the expansion of e-learning and learning management systems in the 2000s. For each transition, the analysis examined four dimensions: What critics predicted would be irreversibly lost ? What advocates claimed would be gained what the empirical evidence, in retrospect, actually showed ? What institutional adaptations ultimately preserved the educational values at stake.? Results. The most consistently documented benefit of AI integration in education is the capacity to deliver genuinely personalised learning at scale. Across the international literature, AI-driven personalised learning plans were in active use at 65 percent of universities by 2025, a fourfold increase from 15 percent in 2020 [12]. This is not a marginal or experimental development. It represents a rapid, 962

fundamental shift in how a majority of higher education institutions approach instructional delivery. The measured outcomes associated with this shift are substantial. Average student engagement across AI-integrated institutions rose by 25 percent over the same period, measured through a combination of attendance, assignment completion rates, and self-reported learning satisfaction [12]. In rural Uzbekistan, where prior access to responsive academic support was severely limited by geography and resource scarcity, AI-enabled distance learning platforms increased student retention rates by 28 percent [6]. This is a finding of considerable importance: it documents AI as an equity instrument, not only a performance instrument, with the potential to partially bridge the historically deep divide between urban and rural educational opportunity. Murtozaqulova (2025) traces this Uzbekistan-specific retention gain to a particular mechanism: the availability of responsive, contextually appropriate academic support at non-standard hours. Rural students who previously had no access to timely assistance during self-study periods report that AI chatbot availability at midnight or on weekends provided the scaffolding that kept them engaged with difficult material rather than disengaging. The intervention is not sophisticated personalisation at the curricular level. It is, at that stage of Uzbekistan’s AI integration, simply access to responsive support. But that access is transformative for students who previously had none. At the level of learning speed and mastery efficiency, the data from EdTech platforms are similarly encouraging. Khan Academy’s AI-augmented cohorts mastered target mathematical and scientific concepts 30 percent faster than pre-AI comparison groups. Duolingo‘s AI-driven language learning tracks produced 22 percent higher vocabulary retention at the 90-day mark [3]. Across subject areas, mastery-speed improvements against pre-AI baselines run to 30 percent in mathematics, 25 percent in reading comprehension, and 28 percent in language acquisition. These are not trivial gains. They suggest that when AI is used to provide immediate feedback, adaptive difficulty calibration, and patient repetition, it can dramatically accelerate the early stages of skill development. A second major positive development is the demonstrable reduction in teacher administrative burden and the associated reallocation of teacher time toward higher-value instructional activities. PISA 2025 data show that the average number of hours per teacher per week devoted to administrative tasks marking routine assignments, generating reports, producing standard lesson plans, managing grade records fell from 35 hours to 21 hours in institutions with substantial AI integration, representing a 40 percent reduction. In absolute terms, this freed approximately 14 hours per teacher per week. The critical question is not whether that time was saved but how it was redirected. Teacher self-report data from the same PISA survey, as well as from Murtozaqulova’s (2025) Uzbekistan study, indicate that teachers in AI-integrated institutions disproportionately redirected freed time toward mentoring individual 963

students, facilitating seminar and discussion-based instruction, and developing more complex, project-based assessment tasks precisely the forms of teaching that research consistently identifies as most valuable and that AI cannot replicate. Table 1 Teacher Time Reallocation by Region - PISA 2025 Region Time (Admin) East Asia Saved Mentorship Gained Notable Constraints 45% +12 hrs/week Leading globally Europe 38% +9 hrs/week Stable infrastructure Central Asia* 32% +7 hrs/week Infrastructure gaps Global Average 40% +10 hrs/week — * Includes Uzbekistan. Source: PISA (2025). Table 1 shows that regional variation in these gains is substantial. East Asia leads, with teachers gaining 12 hours per week on average for mentorship and direct instruction. Central Asia, including Uzbekistan, trails at 7 hours, reflecting the infrastructure constraints and faculty readiness limitations that Murtozaqulova (2025) documents in detail. Uzbek universities have not yet achieved the computational infrastructure, training, or institutional integration that would enable the full efficiency gains visible in East Asian systems. The gains that are occurring are real and meaningful, they simply reflect an earlier stage of a longer transition. The positive evidence above is genuine, substantial, and worthy of serious institutional attention. The following findings are equally genuine, equally substantial, and demand equal weight. Selective citation of either side distorts the complete picture that policy requires. PISA 2025 records an 18 percent decline in essay coherence scores among AI-exposed 15-year-olds compared to a 5 percent decline in low-exposure control groups. High school originality scores fell 22 percent over the same period. University-level independent writing quality declined by 22 percent across OECDmeasured institutions. This is not a marginal trend. It is a consistent, cross-national pattern of deteriorating competence in exactly the cognitive activity that education most explicitly claims to develop. The theoretical mechanism that accounts for this trend is well established in cognitive science. Sweller’s (2023) cognitive load theory argues that the act of constructing an argument that holding a central claim in working memory, searching for and evaluating evidence, drafting, revising, and resolving logical inconsistencies under one’s own cognitive power is not merely a means of producing a document. It is the process through which the neural pathways that constitute argumentative capacity are built and strengthened. When AI consistently performs this process on the student’s behalf, those pathways do not merely go unused; they fail to form in the first place. The student who submits an AI964

generated essay has not merely avoided the work of writing. They have avoided the neural exercise that writing exists, in an educational context, to produce. Turnitin’s 2024 Global Academic Integrity Report documents a 62 percent global rise in AI-generated content in academic submissions. By 2024, 45 percent of university submissions globally contained detectable AI-generated content, compared to 12 percent in 2022. This near-quadrupling in two years is not a trend. In institutional terms, it represents the collapse of a norm. In Uzbekistan, the situation is even more acute at the urban university level. Table 3 documents a 133 percent rise in plagiarism incidents at urban Uzbek universities between 2022 and 2025 from 18 percent to 42 percent of submissions. This figure requires contextualisation, not minimisation. A plagiarism rate of 42 percent does not indicate that 42 percent of students are dishonest by disposition. It indicates that 42 percent of students are responding rationally to an incentive structure that makes AI-generated work indistinguishable from genuine effort in the eyes of current assessment systems. The institution has not changed its evaluation framework. Students are behaving accordingly. This reframing from cheating as a moral failure to plagiarism surge as an institutional design failure is not intended to exonerate individual dishonesty. It is intended to locate the point of leverage for meaningful change. Plagiarism detection software, however sophisticated, addresses the symptom. It does not change the incentive structure. What changes the incentive structure is assessment redesign: moving toward evaluation formats that test cognitive processes rather than documents, and that AI cannot replicate because they require embodied, improvisational, contextually sensitive human performance. NeuroEd’s (2025) longitudinal cognitive tracking study documented a 15 percent reduction in unaided recall performance among frequent AI users compared to matched control groups. Sustained attention fell 14 percent among heavy ChatGPT users in grades 9 through 12. Metacognitive awareness the capacity to accurately assess one’s own understanding and identify gaps declined 11 percent among students who routinely used AI for study support without structured reflection components. Table 2. Negative Cognitive and Integrity Trends by Educational Level (2023–2025) Indicator Middle School High School University Global Average Writing quality decline 12% 20% 22% 18% Plagiarism incidents 40% 65% 75% 62% Unaided recall loss 10% 16% 19% 15% Sustained attention drop 8% 12% 14% 11% Source: NeuroEd (2025); PISA (2025); Turnitin (2024). Table 2 shows that these negative effects are not uniformly distributed across educational levels: university students, who are the heaviest AI users and 965

face the most AI-saturated assessment environments, show the sharpest deterioration across all three indicators. This finding is particularly concerning because university is precisely the stage at which the most complex and durable cognitive capacities — advanced argumentation, disciplinary synthesis, and independent scholarly judgment are supposed to be forming. Uzbekistan presents a particularly instructive case study because it simultaneously exhibits both sides of the AI education dynamic with unusual clarity. Tables 3 and 4 together document this dual reality. Table 3. AI Impact in Uzbekistan - Ministry of Education Surveys, 2022–2025 Metric Pre-AI (2022) Rural student engagement AI Era (2025) Change 62% 78% +26% Rural student retention 58% 74% +28% Urban university plagiarism 18% 42% +133% 420 pts 445 pts +6% 12% 40% +28 pp PISA mathematics (Uzbekistan) score AI platform adoption (Higher Ed) Baseline Source: Ministry of Education Uzbekistan (2025) Table 4 AI Deployment Across Uzbek Higher Education (2024) Application Area Adoption Rate Status / Examples Distance learning platforms 65% Moodle, ZiyoNET, EduMarket Online testing assessment and 55% Unified tests; AI-item generation pilots Digital identity verification 40% Face ID, MyID — pilot phase AI advisory chatbots 25% Extra-curricular counselling support Personalised/adaptive 15% learning Source: Murtozaqulova (2025). Early-stage start-up experiments only The two tables expose a structural paradox at the heart of Uzbekistan’s current AI integration trajectory. The applications with the strongest empirical evidence for positive learning outcomes personalised, adaptive systems are deployed at only 15 percent of institutions, mostly in experimental form. The applications with the most direct connection to the academic integrity crisis , generative AI tools used without structured frameworks or reformed assessment are pervasive and unregulated. Rural students are gaining unprecedented access to educational support. Urban students, in institutions where AI is widespread but oversight is unchanged, are experiencing what the data in Table 3 characterise as 966

an integrity collapse. Both processes are occurring simultaneously inside the same national educational system, creating opposite trajectories that existing policy frameworks are not yet equipped to manage. Discussion. The optimistic case for AI in education deserves careful, unhurried articulation, because it is not merely wishful thinking. It is grounded in a coherent theoretical framework and supported by real data. The central metaphor is one of prosthetics: AI, understood correctly, is not a replacement for human intelligence but an extension of it as glasses extend sight, or a prosthetic limb extends mobility, or written language extended memory. The value of the prosthetic lies not in its independence from its user but in the expanded range of action it enables. Cognitive load theory, as updated by Sweller (2023), provides the theoretical foundation. Human working memory is severely constrained: it can hold and manipulate only a small number of information elements simultaneously. This constraint is the central challenge of learning: much of the difficulty students experience is not a reflection of their intelligence but of the cognitive friction generated by handling multiple information demands simultaneously. Effective instruction has always sought to manage this friction deliberately to free working memory for the cognitive operations that matter most. AI, when used well, does exactly this: it handles the mechanical, routine, and procedurally constrained dimensions of a task, freeing the student’s cognitive resources for the higher-order operations of analysis, synthesis, and creative judgment. The rural Uzbekistan data provide the most practically compelling illustration of this argument. A 28 percent improvement in student retention is not an abstraction to be read past; it represents thousands of students who remained in university programmes and continued developing intellectually rather than dropping out because timely support was unavailable. For a rural Uzbek student in 2024, an AI system that can answer a question about organic chemistry at 11 pm on a Sunday is not a threat to deep learning. It is, for that specific student in those specific circumstances, the precondition that makes deep learning possible at all. The technology is providing access to a scaffold that urban students have always taken for granted: the availability of knowledgeable, responsive support when they need it. The teacher time reallocation data reinforce this argument from a different direction. Teachers who are freed from 14 hours of weekly administrative routine do not, the evidence suggests, spend that time doing less valuable work. They redirect it toward mentoring, discussion, and the relational, contextually sensitive judgment that Abdunabiyeva and Abdulatipov (2025) identify as irreducibly human. The technology, in this reading, is restoring the teacher to the role that teachers are actually irreplaceable in, by removing the administrative burden that was preventing them from occupying that role. The collapse argument is harder to state and more urgent to take seriously, because it rests on a precise distinction that is easy to miss. A prosthetic extends a 967

capacity that exists or is being built. A wheelchair does not build leg strength, nor is it intended to. But education is not a system for performing tasks efficiently; it is a system for building capacities. The distinction that matters is between AI used to scaffold the development of a capacity and AI used to substitute for that development entirely. A student who uses AI to receive feedback on a draft they have written is using a tool to accelerate and enrich a cognitive process they are actively engaged in. A student who uses AI to generate a draft before they have developed any independent capacity to construct one is using a tool to bypass the process entirely. These are not variations of the same behaviour. They are categorically different uses of the same technology, and the difference between them determines whether the technology extends or replaces the cognitive development that education exists to produce. The mechanism is neurobiological, not metaphorical. Cognitive capacities the ability to construct and sustain an argument, to evaluate sources, to identify and repair logical gaps are patterns of neural activation that form and consolidate through repeated, effortful exercise. The process of sitting with a half-formed argument, searching for the evidence that would make it hold, writing a sentence that does not work, identifying why it does not work, and revising until the logic resolves: this process is not merely a means of producing a document. It is the exercise through which the capacity to reason argumentatively is built at the neural level. When AI consistently performs this process instead of the student, those pathways do not form. The student arrives at graduation with a credential that attests to capacities they have never actually developed. The 62 percent global plagiarism surge and the 15 percent decline in unaided recall are early empirical signals of exactly this process. They are early because the students currently in university have had AI tools available for only two to three years. The effects will compound as cohorts who have used AI throughout their secondary education enter higher education with foundational argumentation capacities that were never adequately built. The full scale of this problem is not yet visible in the data. What is visible is the direction of the trend, and the direction is not ambiguous. Abdunabiyeva and Abdulatipov (2025) locate a further dimension of the collapse risk in what AI cannot provide even when it operates without the dysfunctions documented above. Intelligent machines cannot exercise the contextually improvised, relational judgment that teachers exercise moment by moment in a functioning classroom. The question of when this particular student needs to be challenged and when they need to be supported, when a misconception needs to be directly corrected and when it needs to be allowed to become visible through the student’s own inquiry, is not a computational question. It requires embodied presence and a quality of knowing that builds through sustained human relationship. Normalising AI in the instructional relationship means normalising the systematic absence of exactly this dimension of teaching. 968

Conclusion. The current educational technology situation finds its best explanation through the historical development of educational technology. When the calculator first entered classrooms, many predicted the death of mathematics. Mathematics did not disappear but it actually became more complex. The field shifted its focus from basic calculation to developing skills in thinking and solving problems and understanding complex concepts. The instrument brought about a change in the discipline which resulted in new areas of study that became important. Artificial intelligence creates a turning point for our current situation. The evidence presented in this study shows that AI does not possess either extreme dangerousness or extreme groundbreaking capabilities. The system provides measurable enhancements for user access and personalized experiences and operational efficiency. The system operationally diminishes the ability to write independently and maintain academic honesty and the capacity for mental focus according to its current setup. The success of any system depends on the educational methods and institutional structures which determine how the system gets used. The educational field needs institutions to adopt AI pedagogy as a required system because AI needs to become part of educational programs. The establishment of AI pedagogy goes beyond the basic functions of AI tools which can either receive permission or face prohibition. Educational institutions must develop their teaching methods and assessment procedures and curriculum designs based on the current need for AI technology. The assessment system needs to measure both the way students develop their work and the final results while giving importance to their spoken presentations and drafting activities and their capacity to evaluate their own learning and reading proficiency in AI. AI technology will not bring about educational decline. The situation will occur because educational systems continue their outdated teaching methods instead of updating them for current technological advancements. The educational system will push students to use external thought processes when they still evaluate students using pre-AI assessment methods References: 1. Abdunabiyeva, M., & Abdulatipov, M. (2025). Artificial intelligence and its roles in education. Luchshiye Intellektualnyye Issledovaniya, 46(5) https://scientificjl.com/luch/article/view/20140 2. Ashurov, J. Dj. (2021). The role and significance of digital pedagogy in the modern educational environment. https://zienjournals.com/index.php/jpip/article/view/448 3. EdTech Review. (2024). Adaptive learning efficacy study. EdTech Review Press. https://www.researchgate.net/publication/342988837 4. Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence. California Management Review, 61(4), 5–14. 5. Ministry of Education Uzbekistan (MoE). (2025). AI in Central Asian schools report. Tashkent: MoE Publishing. 6. Murtozaqulova, N. A. (2025). Integration of AI technologies into higher education. Kokand University Herald, 16-B(5) https://doi.org/10.54613/ku.v16i.1290 969

7. Negoita, A., & Popescu, E. (2023). Artificial intelligence in education. Journal of Educational Technology,12(1),1.https://www.researchgate.net/publication/376695224_THE_USE_OF_ARTI FICIAL_INTELLIGENCE_IN_EDUCATION 8. NeuroEd Journal. (2025). AI and cognitive metrics in learning. NeuroEd Journal, 8(2), 45–60. 9. OECD. (2025). PISA 2025 results. Paris: OECD Publishing. PISA 2021 10. Sweller, J. (2023). Cognitive load theory update. Educational Psychologist, 58(1), 1– 15. 11. Turnitin. (2024). Global academic integrity report. Turnitin Inc. 12. UNESCO. (2021). AI and education: Guidance for policy-makers. Paris: UNESCO. (2024). Personalised learning and AI. Paris: UNESCO. https://unesdoc.unesco.org TOVARLAR BOZORI KOMYUKTURASI O‘ZGARISHLARINI HISOBGA OLGAN HOLDA IQTISODIY TAHLILNI AMALGA OSHIRISH VA ASOSIY KO‘RSATICHLARINI BAHOLASH Xakimova Ma’mura Muxammadiyevna, SamISI, “Oliy matimatika” kafedrasi assistenti Email: mamurah1983@gmail.com Baxtiyorov Otabek Elmurod o‘g‘li, SamISI Email: otabekbakhtiyorov1@gmail.com Anotatsiya. Ushbu maqolada tovarlar bozori komyukturasi o‘zgarishlarini hisobga olgan holda iqtisodiy tahlilni amalga oshirish metodlari va asosiy ko‘rsatkichlarni baholash usullari o‘rganildi. Bozor o‘zgarishlarining iqtisodiy natijalarga ta’siri, narxlar va talabni prognozlash, shuningdek, chiziqsiz va chiziqli regressiya usullarini qo‘llash orqali tahlil qilingan. Maqola ekonometriya faniga kirish darajasida tadqiqot olib borish uchun nazariy va amaliy asoslarni beradi. Аннотация. В данной статье исследуются методы проведения экономического анализа с учётом изменений структуры товарного рынка и методы оценки основных показателей. Рассмотрено влияние изменений на экономические результаты, прогнозирование цен и спроса, а также анализ с использованием линейной и нелинейной регрессии. Статья предоставляет теоретические и практические основы для исследования на уровне введения в эконометрику. Annotation. This article examines the methods of conducting economic analysis considering changes in the structure of the goods market and methods for assessing key indicators. The impact of market changes on economic outcomes, price and demand forecasting, as well as analysis using linear and nonlinear regression methods are studied. The article provides theoretical and practical foundations for research at an introductory econometrics level. Kalit so‘zlar: tovarlar bozori, bozor komyukturasi, iqtisodiy tahlil, regressiya, ko‘rsatkichlar, prognozlash Ключевые слова: товарный рынок, структура рынка, экономический анализ, регрессия, показатели, прогнозирование Keywords: goods market, market structure, economic analysis, regression, indicators, forecasting Kirish. Bozor iqtisodiyoti sharoitida tovarlar bozori iqtisodiy tizimning muhim tarkibiy qismi hisoblanadi. Bozorda yuz berayotgan o‘zgarishlar talab va 970

taklif hajmi, narxlar darajasi hamda ishlab chiqarish ko‘rsatkichlariga bevosita ta’sir ko‘rsatadi. Shu sababli tovarlar bozori komyukturasi o‘zgarishlarini o‘rganish va ularni iqtisodiy jihatdan tahlil qilish muhim ilmiy va amaliy ahamiyatga ega. Bozor komyukturasi ma’lum bir davrda bozorda shakllangan iqtisodiy holatni ifodalaydi va u talab, taklif, narxlar hamda bozor ishtirokchilari faoliyati orqali namoyon bo‘ladi. Ushbu omillar doimiy ravishda o‘zgarib turishi sababli ularni tahlil qilish va baholashda iqtisodiy-statistik usullardan foydalanish zarur. Ekonometriya iqtisodiy jarayonlarni matematik va statistik usullar orqali tahlil qilish imkonini beradigan muhim fan hisoblanadi. Ekonometriya metodlari yordamida iqtisodiy ko‘rsatkichlar o‘rtasidagi bog‘liqlikni aniqlash, bozor o‘zgarishlarini baholash hamda kelajakdagi iqtisodiy jarayonlarni prognoz qilish mumkin. Shu sababli tovarlar bozori komyukturasi o‘zgarishlarini hisobga olgan holda iqtisodiy tahlilni amalga oshirish dolzarb masalalardan biri hisoblanadi. Asosiy qism. Tovarlar bozori komyukturasi iqtisodiyotdagi muhim jarayonlardan biri bo‘lib, u bozordagi talab va taklif o‘zgarishlari, narxlar dinamikasi hamda ishlab chiqarish hajmining o‘zgarishi bilan tavsiflanadi. Bozor komyukturasi iqtisodiy subyektlar faoliyatiga, ya’ni ishlab chiqaruvchilar, iste’molchilar va vositachilar o‘rtasidagi munosabatlarga bevosita ta’sir ko‘rsatadi. Shu sababli bozordagi o‘zgarishlarni ilmiy asosda tahlil qilish iqtisodiy rivojlanishni baholash va prognozlashda muhim ahamiyatga ega. Bozor komyukturasi o‘zgarishlariga turli omillar ta’sir qiladi. Ularga aholi daromadlari darajasi, ishlab chiqarish hajmi, resurslar narxi, davlatning iqtisodiy siyosati hamda tashqi iqtisodiy omillar kiradi. Ushbu omillar ta’sirida bozorda talab va taklif miqdori o‘zgaradi va bu o‘z navbatida narxlarning shakllanishiga hamda bozor muvozanatiga ta’sir ko‘rsatadi. Shuning uchun ham iqtisodiy tahlilda ushbu omillarni hisobga olish muhim hisoblanadi. Tovarlar bozori komyukturasi o‘zgarishlarini tahlil qilishda ekonometriya metodlari keng qo‘llaniladi. Ekonometriya iqtisodiy ko‘rsatkichlar o‘rtasidagi bog‘liqlikni matematik modellar orqali aniqlash imkonini beradi. Masalan, regressiya tahlili yordamida narx va talab o‘rtasidagi bog‘liqlikni aniqlash mumkin. Chiziqli regressiya modeli quyidagi ko‘rinishda ifodalanadi: Y=a+bX+ε bu yerda: Y – natijaviy ko‘rsatkich (masalan, talab hajmi), X – ta’sir etuvchi omil (masalan, narx), a va b – model parametrlaridir, ε – tasodifiy xatolik. 971

1-rasm. Tovarlar bozorida talab va taklif muvozanati Bu chizmada: • Talab chizig‘i (Demand) – narx oshgani sari talab kamayishini ko‘rsatadi. • Taklif chizig‘i (Supply) – narx oshgani sari taklif ortishini ko‘rsatadi. • Muvozanat nuqtasi – talab va taklif teng bo‘lgan holat bo‘lib, bozorda barqaror narx va miqdor shakllanadi. Ushbu model yordamida iqtisodiy ko‘rsatkichlar o‘rtasidagi bog‘liqlik darajasi aniqlanadi va ularning kelajakdagi qiymatlari prognoz qilinadi. Bundan tashqari, ko‘p omilli regressiya modellaridan foydalanish orqali bir nechta omillarning iqtisodiy natijalarga ta’sirini ham aniqlash mumkin. Bozor tahlilida asosiy iqtisodiy ko‘rsatkichlarni baholash ham muhim hisoblanadi. Bunday ko‘rsatkichlarga narx darajasi, talab va taklif hajmi, ishlab chiqarish ko‘rsatkichlari hamda bozor ulushi kiradi. Ushbu ko‘rsatkichlarni tahlil qilish orqali bozordagi mavjud holatni aniqlash va uning kelajakdagi rivojlanish tendensiyalarini prognoz qilish mumkin. Shuningdek, statistik ma’lumotlar asosida indeks usullari va dinamik qatorlar tahlili ham qo‘llaniladi. Bu usullar yordamida narxlar o‘zgarishi, ishlab chiqarish hajmining o‘sishi yoki kamayishi hamda bozor rivojlanishining umumiy tendensiyalari aniqlanadi. Natijada iqtisodiy tahlil jarayonida aniq va ishonchli xulosalar chiqarish imkoniyati yaratiladi. Xulosa. Tovarlar bozori komyukturasi o‘zgarishlarini tahlil qilish iqtisodiy jarayonlarni chuqur o‘rganish va ularning rivojlanish tendensiyalarini aniqlashda muhim ahamiyatga ega. Bozorda talab va taklifning o‘zgarishi, narxlar dinamikasi hamda ishlab chiqarish hajmining o‘zgarishi iqtisodiy ko‘rsatkichlarga bevosita ta’sir ko‘rsatadi. Shu sababli ushbu jarayonlarni ilmiy asosda o‘rganish iqtisodiy tahlilning muhim yo‘nalishlaridan biri hisoblanadi. Ekonometriya metodlari yordamida iqtisodiy ko‘rsatkichlar o‘rtasidagi bog‘liqlikni aniqlash, ularni modellashtirish hamda kelajakdagi holatini prognoz qilish mumkin. Ayniqsa, regressiya tahlili va statistik usullar bozor o‘zgarishlarini baholashda samarali vosita hisoblanadi. Ushbu usullar orqali iqtisodiy jarayonlarning asosiy omillari aniqlanadi va ularning iqtisodiy natijalarga ta’siri baholanadi. Shunday qilib, tovarlar bozori komyukturasi o‘zgarishlarini hisobga olgan holda iqtisodiy tahlilni 972

amalga oshirish iqtisodiy qarorlar qabul qilishda muhim ahamiyatga ega bo‘lib, iqtisodiy rivojlanishning barqarorligini ta’minlashga xizmat qiladi. Foydalanilgan adabiyotlar ro‘yxati: 1. Abduqahhorov T. Ekonometriya asoslari. – Toshkent: Iqtisodiyot nashriyoti, 2021. 2. Salimov B., Qodirov S. Bozor iqtisodiyoti nazariyasi. – Toshkent: Fan va texnologiya, 2020. 3. Rasulov A. Iqtisodiy tahlil asoslari. – Toshkent: O‘zbekiston Respublikasi Oliy ta’lim nashriyoti, 2019. 4. Islomov R. Statistika va iqtisodiy tahlil. – Toshkent: Iqtisodiyot, 2018. RAQAMLI IQTISODIYOT SHAROITIDA SANOAT KORXONALARIDA MEHNAT JARAYONLARINI BOSHQARISH VA ISH HAQI HISOBINI AVTOMATLASHTIRISH MEXANIZMLARI Djurayeva Feruzaxon Abdumuxtorovna, AndDTI “Axborot texnologiyalari” kafedrasi katta o‘qituvchisi, feruzaskarlet@gmail.com Mamatova Gulsanam ravshanbek qizi, AndDTI, talaba 4-kurs, Axborot tizimlari va texnologiyalari, gulsanammamatova23@gmail.com Annotatsiya. Ushbu maqolada raqamli iqtisodiyot sharoitida sanoat korxonalarida mehnat jarayonlarini samarali boshqarish hamda ish haqi hisobini avtomatlashtirish mexanizmlari tahlil qilinadi. Zamonaviy axborot texnologiyalari va raqamli boshqaruv tizimlari yordamida xodimlarning ish faoliyatini nazorat qilish, mehnat unumdorligini oshirish hamda moliyaviy hisob-kitob jarayonlarini soddalashtirish masalalari ko‘rib chiqiladi. Shuningdek, sanoat korxonalarida, xususan chevarchilik korxonalarida mehnat jarayonlarini avtomatlashtirishning afzalliklari va samaradorligi yoritilgan. Annotation. This article analyzes the mechanisms for effective management of labor processes and automation of payroll accounting in industrial enterprises in the context of the digital economy. The issues of monitoring employee performance, increasing labor productivity, and simplifying financial accounting processes using modern information technologies and digital management systems are considered. The advantages and effectiveness of automating labor processes in industrial enterprises, in particular, in craft enterprises, are also highlighted. Аннотация. В данной статье анализируются механизмы эффективного управления трудовыми процессами и автоматизации учета заработной платы на промышленных предприятиях в контексте цифровой экономики. Рассматриваются вопросы мониторинга эффективности работы сотрудников, повышения производительности труда и упрощения процессов финансового учета с использованием современных информационных технологий и цифровых систем управления. Также освещаются преимущества и эффективность автоматизации трудовых процессов на промышленных предприятиях, в частности, на ремесленных. Kalit so‘zlar: Raqamli iqtisodiyot, sanoat korxonalari, mehnat jarayonlarini boshqarish, ish haqi hisobi, avtomatlashtirish, axborot tizimi, raqamli boshqaruv, mehnat unumdorligi, moliyaviy texnologiyalar, chevarchilik sanoati. Keywords: Digital economy, industrial enterprises, labor process management, payroll, automation, information system, digital management, labor productivity, financial technologies, craft industry. Ключевые слова: Цифровая экономика, промышленные предприятия, управление трудовыми процессами, расчет заработной платы, автоматизация, информационные 973

системы, цифровое управление, производительность труда, финансовые технологии, ремесленное производство. Kirish. Hozirgi kunda dunyoda raqamli texnologiyalar jadal sur’atlar bilan rivojlanib borayotgan bir davrda iqtisodiyotning deyarli barcha sohalarida avtomatlashtirilgan boshqaruv tizimlarini joriy etish muhim ahamiyat kasb etmoqda. Raqamli texnologiyalar nafaqat ishlab chiqarish jarayonlarini modernizatsiya qilish, balki boshqaruv jarayonlarini takomillashtirish, resurslardan samarali foydalanish hamda iqtisodiy samaradorlikni oshirish imkonini yaratmoqda. Shu sababli ko‘plab davlatlar va korxonalar o‘z faoliyatida raqamli texnologiyalarni keng joriy etishga katta e’tibor qaratmoqda [1][2]. Raqamli iqtisodiyot sharoitida korxonalar faoliyatini samarali tashkil etish, ishlab chiqarish jarayonlarini optimallashtirish hamda mehnat unumdorligini oshirish muhim vazifalardan biri hisoblanadi. Sanoat korxonalarida ishlab chiqarish jarayonlari ko‘p bosqichli va murakkab tizimdan iborat bo‘lib, ularni samarali boshqarish uchun zamonaviy axborot texnologiyalaridan foydalanish zarur [3][4]. Ayniqsa xodimlarning ish faoliyatini nazorat qilish, ularning mehnat samaradorligini baholash, ish vaqtini hisobga olish hamda ish haqi hisob-kitoblarini aniq yuritish korxona boshqaruvida muhim o‘rin tutadi. Agar ushbu jarayonlar to‘g‘ri tashkil etilsa, korxonada ishlab chiqarish samaradorligi oshadi, resurslardan foydalanish darajasi yaxshilanadi hamda xodimlarning mehnat faoliyati yanada samarali tashkil etiladi. Raqamli texnologiyalar asosida yaratilgan avtomatlashtirilgan boshqaruv tizimlari yordamida xodimlarning ish jarayonlarini real vaqt rejimida monitoring qilish, bajarilgan ish hajmini nazorat qilish hamda mehnat unumdorligini tahlil qilish imkoniyati yaratiladi. Bunday tizimlar ma’lumotlarni tezkor qayta ishlash, tahlil qilish va boshqaruv qarorlarini qabul qilish jarayonini sezilarli darajada yengillashtiradi. Shuningdek, avtomatlashtirilgan tizimlar yordamida ish haqi hisoblash jarayonini ham aniq va shaffof tarzda tashkil etish mumkin [5][6]. Asosiy qism. Raqamli iqtisodiyot sharoitida mehnat jarayonlarini samarali boshqarish korxona faoliyatining barqaror rivojlanishi va iqtisodiy samaradorligini oshirishning muhim omillaridan biri hisoblanadi. Zamonaviy axborot texnologiyalari va raqamli boshqaruv tizimlari yordamida korxonada mehnat jarayonlarini aniq rejalashtirish, nazorat qilish hamda tahlil qilish imkoniyati kengayadi. Bu esa ishlab chiqarish jarayonlarini yanada samarali tashkil etishga, vaqt va resurslardan oqilona foydalanishga hamda umumiy boshqaruv tizimini takomillashtirishga xizmat qiladi [3][5]. Mehnat jarayonlarini to‘g‘ri tashkil etish orqali korxona ishlab chiqarish hajmini oshirish, mavjud texnik va inson resurslaridan samarali foydalanish hamda ishlab chiqarish jarayonidagi ortiqcha vaqt yo‘qotishlarini kamaytirish mumkin. Ayniqsa sanoat korxonalarida har bir xodimning ish faoliyati aniq reja asosida tashkil etilsa, ishlab chiqarish jarayonining uzluksizligi ta’minlanadi va umumiy samaradorlik darajasi sezilarli darajada oshadi. Shu bilan birga, mehnat 974

jarayonlarini samarali boshqarish xodimlarning mas’uliyatini oshirish, ularning ishga bo‘lgan motivatsiyasini kuchaytirish hamda mehnat unumdorligini oshirishga ham ijobiy ta’sir ko‘rsatadi [4][6]. Sanoat korxonalarida xodimlarning ish jarayonlarini boshqarish quyidagi asosiy vazifalarni o‘z ichiga oladi: 1-jadval № Jarayon nomi 1 Xodimlarning ish vaqtini hisobga olish 2 3 4 Tavsifi Xodimlarning ishga kelish va ketish vaqti, ish davomiyligi hamda ish kunlari maxsus tizim orqali qayd etiladi. Bajarilgan ish Har bir xodim tomonidan bajarilgan hajmini nazorat qilish ish miqdori va ishlab chiqarish ko‘rsatkichlari tizim orqali nazorat qilinadi. Ishlab chiqarish jarayonidagi samaradorlikni baholash Mehnat unumdorligini tahlil qilish Ish jarayonidagi natijalar, ishlab chiqarish tezligi va mahsulot hajmi tahlil qilinadi. Xodimlarning ish samaradorligi, ishlab chiqarish ko‘rsatkichlari va bajarilgan vazifalar tahlil qilinadi. Natijasi Ish vaqtining aniq hisobini yuritish va intizomni oshirish ta’minlanadi. Ish jarayonining samaradorligini aniqlash va ishlab chiqarish nazoratini kuchaytirish imkoniyati yaratiladi. Korxona faoliyatining umumiy samaradorligini baholash imkoniyati paydo bo‘ladi. Mehnat unumdorligini oshirish va samarali boshqaruv qarorlarini qabul qilishga yordam beradi. Agar ushbu jarayonlar raqamli tizimlar yordamida boshqarilsa, korxonada shaffoflik va aniqlik darajasi oshadi. Bundan tashqari, rahbariyat real vaqt rejimida ishlab chiqarish jarayonlari haqida ma’lumot olish imkoniyatiga ega bo‘ladi hamda boshqaruv qarorlarini tezkor qabul qilish imkoniyati yaratiladi [5][7]. Xulosa va amaliy tavsiyalar. Raqamli iqtisodiyot sharoitida sanoat korxonalarida mehnat jarayonlarini samarali tashkil etish va ish haqi hisobini aniq yuritish korxona boshqaruvining muhim tarkibiy qismi hisoblanadi. Zamonaviy axborot texnologiyalarining jadal rivojlanishi korxonalarda ishlab chiqarish jarayonlarini optimallashtirish, xodimlar faoliyatini tizimli nazorat qilish hamda boshqaruv jarayonlarini takomillashtirish imkonini bermoqda [1][5]. Tadqiqot natijalari shuni ko‘rsatadiki, mehnat jarayonlarini avtomatlashtirish orqali xodimlarning ish vaqtini hisobga olish, bajarilgan ish hajmini nazorat qilish, ishlab chiqarish samaradorligini baholash hamda ish haqi hisob-kitoblarini tez va aniq amalga oshirish mumkin. Bu esa korxona faoliyatining shaffofligini oshiradi, inson omili bilan bog‘liq xatoliklarni kamaytiradi va boshqaruv samaradorligini sezilarli darajada yaxshilaydi [6][7]. Amaliy tavsiya sifatida quyidagilarni taklif qilish mumkin: sanoat korxonalarida xodimlarning ish vaqtini nazorat qilish uchun zamonaviy elektron monitoring va hisobga olish tizimlarini joriy etish, mehnat jarayonlarini boshqarish 975

hamda ish haqi hisobini yuritish uchun yagona avtomatlashtirilgan axborot tizimlarini ishlab chiqish va amaliyotga tatbiq etish, korxonalarda raqamli texnologiyalar asosida ishlab chiqarish jarayonlarini monitoring qilish va tahlil qilish tizimini joriy qilish, xodimlarning mehnat unumdorligini oshirish maqsadida avtomatlashtirilgan baholash tizimlaridan foydalanish, korxona rahbarlari va xodimlari uchun raqamli texnologiyalar hamda axborot tizimlaridan foydalanish bo‘yicha malaka oshirish dasturlarini tashkil etish hamda sanoat korxonalarida moliyaviy hisob-kitoblarni yuritishda zamonaviy moliyaviy texnologiyalar va elektron hisob tizimlaridan foydalanishni kengaytirish tavsiya etiladi [3][5][7]. Foydalanilgan adabiyotlar: 1. O‘zbekiston Respublikasi Prezidenti. Raqamli iqtisodiyotni rivojlantirish bo‘yicha qaror va farmonlar to‘plami. – Toshkent, 2020. 2. O‘zbekiston Respublikasi Vazirlar Mahkamasi. Raqamli iqtisodiyotni rivojlantirish konsepsiyasi. – Toshkent, 2020. 3. Abdullayev A., Karimov B. Axborot tizimlari va texnologiyalari. – Toshkent: O‘zbekiston, 2019. 4. Sodiqov Q. Axborot texnologiyalari va tizimlari. – Toshkent: Fan va texnologiya, 2018. 5. Laudon K., Laudon J. Management Information Systems: Managing the Digital Firm. – New York: Pearson Education, 2020. 6. Stair R., Reynolds G. Principles of Information Systems. – Boston: Cengage Learning, 2018. 7. Turban E., Volonino L. Information Technology for Management. – Wiley, 2017. МЕТОДОЛОГИЧЕСКИЕ ОСНОВЫ СОВЕРШЕНСТВОВАНИЯ УПРАВЛЕНИЯ ЭЛЕКТРОННОЙ КОММЕРЦИЕЙ В УСЛОВИЯХ ЦИФРОВОЙ ЭКОНОМИКИ Isoxo‘jaeva M.Y., UMFT, «Iqtisodiyot» kafedra PhD, professori Annotatsiya. Mazkur ishda raqamli iqtisodiyot sharoitida elektron tijoratni davlat tomonidan boshqarishni modernizatsiya qilish yo‘nalishlari tahlil qilinadi va ma’lumotlarga asoslangan boshqaruv konsepsiyasi doirasida yangi yondashuv taklif etiladi. Tadqiqotda boshqaruv qarorlarini qabul qilish jarayoniga katta ma’lumotlar, sun’iy intellekt va prognozlash usullarini integratsiya qilish orqali samaradorlikni oshirish imkoniyatlari ochib berilgan. Shu bilan birga, risklarni kompleks baholashga asoslangan tartibga solish modeli hamda boshqaruv natijadorligini miqdoriy baholash usullari ishlab chiqilgan. Tadqiqot yakunida elektron tijoratni barqaror rivojlantirish va davlat boshqaruvi tizimini takomillashtirishga qaratilgan amaliy tavsiyalar shakllantirilgan. Kalit so‘zlar: elektron tijorat, davlat boshqaruvi, ma’lumotlarga asoslangan boshqaruv, sun’iy intellekt, prognozlash, risk baholash, raqamli iqtisodiyot. Annotation. This research explores the modernization of public administration in ecommerce within the digital economy, based on a data-driven management approach. It demonstrates that integrating big data technologies, artificial intelligence, and forecasting 976

methods into decision-making processes significantly enhances their effectiveness and validity. The research also develops a risk-based regulatory model and a quantitative framework for evaluating management performance. As a result, practical recommendations are proposed to support the sustainable development of e-commerce and improve public governance systems. Keywords: e-commerce, public administration, data-driven management, artificial intelligence, forecasting, risk assessment, digital economy. Аннотация. В работе исследуются направления модернизации государственного управления электронной коммерцией в условиях цифровой экономики с опорой на концепцию управления, основанного на данных. Показано, что интеграция технологий больших данных, искусственного интеллекта и методов прогнозирования в процессы принятия решений позволяет существенно повысить их обоснованность и результативность. Кроме того, разработаны модель регулирования, ориентированная на комплексную оценку рисков, и методика количественного измерения эффективности управленческих решений. По итогам исследования предложены практические рекомендации, направленные на развитие электронной коммерции и совершенствование системы государственного управления. Ключевые слова: электронная коммерция, государственное управление, управление на основе данных, искусственный интеллект, прогнозирование, оценка рисков, цифровая экономика. Введение. В условиях цифровой трансформации экономики электронная коммерция становится одним из ключевых драйверов экономического роста, что обуславливает необходимость совершенствования механизмов её государственного регулирования. Традиционные подходы к управлению, основанные на ретроспективном анализе и административных инструментах, не обеспечивают достаточной оперативности и эффективности в условиях быстро меняющейся цифровой среды. В диссертационном исследовании разработан методологический подход к совершенствованию государственного управления электронной коммерцией на основе концепции data-driven governance, предполагающий переход к принятию управленческих решений на базе анализа больших данных, применения технологий искусственного интеллекта и инструментов предиктивной аналитики. Ключевым элементом научной новизны является формирование проактивной модели управления, обеспечивающей переход от реактивного регулирования к упреждающему воздействию на развитие электронной коммерции. В рамках данной модели предложена интеграция систем сбора, обработки и анализа данных в единый контур управления, что позволяет выявлять закономерности развития рынка и прогнозировать его динамику. В работе разработан метод предиктивной аналитики управленческих решений, основанный на использовании экономикоматематических моделей и продукционных правил, обеспечивающий формирование базы знаний и выбор оптимальных управленческих воздействий. Данный метод позволяет повысить обоснованность принимаемых решений и минимизировать влияние неопределённости. 977

Ключевым элементом метода является формализация совокупного влияния факторов на развитие электронной коммерции с использованием модели конъюнкции вероятностей (принцип включений-исключений). Интегральный эффект взаимодействия факторов определяется следующим образом: 𝑃𝑥1 ∧𝑥2 ∧𝑥3 = 𝑃𝑥1 + 𝑃𝑥2 + 𝑃𝑥3 в€’ 𝑃𝑥1 в€—𝑃𝑥2 𝑃𝑥1 в€—𝑃𝑥2 в€—𝑃𝑥3 100 в€’ 𝑃𝑥1 в€—𝑃𝑥3 100 в€’ 𝑃𝑥2 в€—𝑃𝑥3 100 + 10000 где Pxi — процентное влияние i-го фактора; учитываются попарные, тройные и более высокие взаимодействия факторов. Данная модель позволяет учитывать не только индивидуальное влияние факторов, но и их взаимное усиление (синергетический эффект) либо ослабление (взаимокомпенсация), включая воздействие отрицательных факторов (например, с коэффициентами со знаком «–»). Существенным результатом исследования является разработка механизма риск-ориентированного регулирования электронной коммерции, включающего систему комплексной оценки рисков по ключевым направлениям (экономическим, технологическим, институциональным, инфраструктурным и социальным). Предложенная градация уровней риска (низкий, средний, высокий) с использованием пороговых значений вероятности позволяет дифференцировать управленческие меры и повысить их адресность. В отличие от существующих подходов, основанных на унифицированном и преимущественно реактивном регулировании, предложенный механизм реализует адаптивную модель государственного управления, предусматривающую классификацию субъектов электронной коммерции по уровням риска на основе формализованного интегрального показателя: где Pi — частные показатели риска по соответствующим категориям; Wi — весовые коэффициенты значимости.На основе рассчитанного значения интегрального показателя обоснована система пороговых значений, позволяющая осуществлять градацию рисков. В соответствии с данной классификацией сформирована модель дифференцированного регулирования: • при низком уровне риска применяется режим минимального вмешательства, ориентированный на стимулирование развития и снижение регуляторной нагрузки; • при среднем уровне риска реализуются механизмы превентивного мониторинга и адаптивного контроля; 978