Fhen M. won the weekly poetry contest on DYVL radio station on May 2, 2026, with his Waray poem “Bara ha Kasingkasing.” Earlier, on April 26, 2026, he was interviewed by DYNF 91.3 Radyo Kauswagan Teleradyo. He was also interviewed on February 7, 2026, by Bombo Radyo Tacloban, where he shared his thoughts on literature and poetry. He recalls “Basuni” as one of the songs that often played on his grandmother’s radio. A soft, melancholic tune, it filled their home with gentle, lingering notes. In Waray, “basuni” means a splinter lodged deep in the heart, and the song captures the sharp, enduring ache of heartbreak.
THE RELEVANCE OF CLASSICAL RUSSIAN LITERATURE AMONG MODERN YOUTH
Jizzakh State Pedagogical University
Faculty of Philology
Field of Study: Russian Language and Literature
Student of group 723-24
Murtazoeva Shakhnozabonu
Annotation:
This paper examines the relevance of classical Russian literature among modern youth. It analyzes young people’s interest in reading and emphasizes the educational and moral significance of classical literary works. Special attention is given to the role of literature in shaping values and worldview. The study concludes that classical Russian literature remains important and influential in contemporary society.
Classical Russian literature occupies a special place in the global cultural and spiritual treasury of humanity. It was formed over several centuries and has absorbed a rich experience of philosophical reflections, moral searches, and artistic interpretation of reality. In modern conditions, when information technologies are rapidly developing and young people’s interest in reading is gradually declining, the issue of the relevance of classical Russian literature becomes particularly significant.
First of all, it should be noted that classical literature performs an important educational function. The works of such outstanding writers as Leo Tolstoy, Fyodor Dostoevsky, Alexander Pushkin, Ivan Turgenev, and Anton Chekhov contain profound reflections on the meaning of life, good and evil, and moral choice. These works contribute to the formation of moral values among young people, develop empathy, critical thinking, and spiritual self-improvement. In the context of modern globalization, when young people are faced with many contradictory values, classical literature becomes a reliable guide in the search for life meanings.
In addition, classical Russian literature plays an important role in the development of thinking and intellectual abilities of the younger generation. Reading complex literary texts requires concentration, an analytical approach, and the ability to interpret what is read. Unlike short informational messages and visual content that dominate the digital environment, classical works stimulate deep reflection and shape a culture of thinking. This is especially important in modern society, where the ability to analyze information and draw independent conclusions becomes a key skill.
The relevance of classical Russian literature is also manifested in its ability to reflect universal problems of human existence. Themes such as love, freedom, responsibility, the meaning of life, and inner struggle remain unchanged over the centuries. Despite historical and cultural differences, classical works continue to resonate with modern readers. By engaging with these works, young people gain an opportunity to better understand themselves and the world around them.
However, it should be acknowledged that today the interest of young people in classical literature faces a number of challenges. One of the main reasons is the development of digital technologies and changes in the ways information is perceived. Social media, video content, and entertainment platforms often displace traditional reading. As a result, many young people perceive classical literature as complex and outdated. In addition, the language of 19th-century works may seem difficult to understand, which also reduces interest in reading.
Despite these challenges, there are effective ways to increase young people’s interest in classical Russian literature. The education system plays a key role in this process. Modern pedagogical methods, such as interactive learning, discussions, dramatization, and the use of digital technologies, make the study of literature more engaging and accessible. For example, film adaptations of classical works, audiobooks, and electronic resources can serve as additional incentives for exploring literary heritage.
Personal example and the cultural environment are also of great importance. If respect for books and reading is fostered in families and society, it positively influences young people’s interest in literature. In this context, libraries, cultural centers, and educational institutions play an important role by creating conditions for promoting classical literature.
Classical Russian literature also remains relevant in the context of forming national and cultural identity. It reflects historical experience, traditions, and social values, contributing to the preservation of cultural heritage. For modern youth, this is especially important, as it helps maintain a connection with the past and better understand their place in the world.
Thus, despite modern challenges, classical Russian literature has not lost its significance. It continues to perform important educational, moral, and cultural functions. Its relevance is обусловлена (determined by) the universality of its themes, the depth of its philosophical ideas, and its ability to shape the spiritual world of an individual. The task of modern society is to preserve and pass on this rich heritage to future generations by adapting its presentation to the conditions of the digital age.
In conclusion, it should be emphasized that classical Russian literature is not only a part of cultural heritage but also an important tool for personal development. It helps young people comprehend complex life issues, forms moral guidelines, and contributes to intellectual growth. That is why its relevance among modern youth remains high and requires further study and popularization.
CONCLUSION
The study has shown that classical Russian literature retains its relevance among modern youth despite significant changes in the informational and cultural environment. The analysis demonstrated that classical works continue to perform important educational, moral, and spiritual functions. They contribute to the formation of moral values, the development of critical thinking, and the expansion of the worldview of the younger generation.
The particular significance of classical literature lies in its universality. The themes addressed in the works of Russian writers—such as love, moral choice, the meaning of life, freedom, and responsibility—remain relevant regardless of time. This allows modern youth to find answers to important life questions and reflect on their own experiences through classical works.
At the same time, it has been found that young people’s interest in reading classical literature is somewhat declining under the influence of digital technologies and changing forms of information perception. However, this process is not irreversible. With the right approach to teaching and promoting literature, it is possible to revive interest in classical works. The use of modern educational technologies, interactive teaching methods, and the adaptation of classical texts to contemporary conditions contribute to a more effective perception of literary heritage.
LIST OF REFERENCES
Ivanova N.V. Modern Perception of Classical Literature by Youth. – Moscow: Yurait, 2023. – 256 p.
Petrov A.S. Russian Classics in the Digital Age. – Saint Petersburg: Piter, 2024. – 312 p.
Sidorova E.M. Literary Education in the Context of Globalization. – Moscow: Akademiya, 2023. – 198 p.
Kuznetsov V.I. Reading and Culture of Modern Youth. – Moscow: Prosveshchenie, 2024. – 224 p.
Melnikova T.A. Classical Literature and Its Role in Personality Formation. – Kazan: University Press, 2023. – 176 p.
Zakharov D.P. Youth Interest in Classical Literature: Problems and Prospects. // Pedagogy. – 2023. – No. 7. – pp. 45–52.
Orlova L.S. Methods of Promoting Classical Literature among Students. // Education Issues. – 2024. – No. 2. – pp. 88–96.
Renowned Chinese director and music producer He Taiji presents an original poem to praise Lan Xin for winning the World Literary Practitioner Grand Prize.
Sincere congratulations to Ms. Lan Xin on receiving this magnificent global honor, stepping into the core palace of world literature.
Moonlight Shines on Lan Xin, Ode to Her Glory
Poem By He Taiji
Translated by Lan Xin (Lanxin Samei）
In the quiet corridor of time
Her grace shines like the moon with gentle glow sublime
Her voice resounds as nightingales sweetly sing
Notes dance along weaving dreams into a delicate ring
Within her eyes lies endless starlight bright
Reflecting all mortal scenes in depths of quiet night
Her smile blooms fair as spring flowers in full sway
Whose fragrance lingers enchanting time along the way
Her heart remains pure as frost and snow
Kindness flows like springs where hopeful spirits grow
IS ONLINE LEARNING MORE EFFECTIVE THAN OFFLINE LEARNING?
Author: Jumanazarova Nafisa
Email:davlatmuradovna@gmail.com
ORCID:0009-0001-5442-4349
University:National university of Uzbekistan
Field: Foreign language and literature
Annotation: This study examines the effectiveness of online and traditional education. The relevance of this topic is due to the rapid development of technology and the widespread adoption of distance learning in recent years. The main objective of the research is to identify the advantages and disadvantages of both online and offline education and to evaluate their impact on the learning process. The study employs comparative, analytical, and generalization methods. Both forms of education are explored, and their distinctive features are highlighted. The findings indicate that online education provides convenience and flexibility, whereas offline education enhances direct interaction and supervision.In conclusion, both forms of education are important, and their combined application can lead to more effective outcomes.
Keywords: Online education, traditional education, distance learning, educational effectiveness, learning process, educational technologies, interactive learning, flexibility, quality of education, digital education.
Annotatsiya: Ushbu ishda onlayn va an’anaviy ta’limning samaradorligi tahlil qilinadi. Mazkur mavzu hozirgi kunda texnologiyalarning tez rivojlanishi va masofaviy ta’limning keng tarqalishi sababli dolzarb hisoblanadi. Tadqiqotning asosiy maqsadi — onlayn va offline ta’limning afzallik va kamchiliklarini aniqlash hamda ularning o‘quv jarayoniga ta’sirini baholashdan iborat. Ish davomida taqqoslash, tahlil va umumlashtirish metodlaridan foydalanilgan. Tadqiqotda har ikkala ta’lim turi o‘rganilib, ularning o‘ziga xos jihatlari yoritib berilgan. Natijalar shuni ko‘rsatadiki, onlayn ta’lim qulaylik va moslashuvchanlikni ta’minlasa, offline ta’lim bevosita muloqot va nazoratni kuchaytiradi. Xulosa qilib aytganda, har ikkala ta’lim turi ham muhim bo‘lib, ularni uyg‘unlashtirib qo‘llash samarali natija berishi mumkin.
Kalit so’zlar: Onlayn ta’lim, an’anaviy ta’lim, masofaviy o‘qitish, ta’lim samaradorligi, o‘quv jarayoni, ta’lim texnologiyalari, interaktiv o‘qitish, moslashuvchanlik, ta’lim sifati, raqamli ta’lim.
Аннотация: В данном исследовании рассматривается эффективность онлайн- и традиционного образования. Актуальность темы обусловлена быстрым развитием технологий и широким распространением дистанционного обучения в последние годы. Основная цель исследования — выявить преимущества и недостатки онлайн- и офлайн-образования, а также оценить их влияние на учебный процесс.В ходе исследования использовались методы сравнения, анализа и обобщения. Были изучены обе формы обучения и раскрыты их отличительные особенности. Результаты показывают, что онлайн-образование обеспечивает удобство и гибкость, тогда как офлайн-образование усиливает непосредственное взаимодействие и контроль.В заключение следует отметить, что обе формы обучения являются важными, и их сочетание может привести к более эффективным результатам.
Ключевые слова: Онлайн-образование, традиционное образование, дистанционное обучение, эффективность образования, учебный процесс, образовательные технологии, интерактивное обучение, гибкость, качество образования, цифровое образование.
INTRODUCTION
In recent years, the education system has undergone significant transformations under the influence of rapidly developing technologies. In particular, the widespread adoption of online education has created the need to compare it with the traditional (offline) education system. This article is specifically devoted to analyzing the effectiveness of online and offline education, aiming to determine their role and significance in the learning process.
The relevance of this topic lies in the fact that today many pupils and students are faced with the necessity of choosing between two modes of learning—distance and traditional. The development of digital technologies, the expansion of internet accessibility, and the increased popularity of distance education in the post-pandemic period have made this issue even more important. Therefore, identifying which type of education is more effective has become a crucial matter not only for learners but also for teachers and the education system as a whole.
At the same time, although existing studies have separately highlighted the advantages of online and offline education, there remains a certain gap in directly comparing their actual effectiveness and drawing clear conclusions. In some cases, the convenience of online education is highly valued, while in others, the effectiveness of traditional education is considered superior. This indicates the need for a more in-depth investigation of the issue.
The main objective of this article is to analyze the effectiveness of online and offline education, identify their strengths and weaknesses, and justify in which situations each type of education is more effective. Furthermore, the study examines the impact of both forms of education on students’ learning processes and, based on this analysis, provides general conclusions.
RESEARCH METHODOLOGY
This study aims to determine the effectiveness of online and traditional (offline) education and employs a mixed-methods approach. This approach allows for a comprehensive examination of the research problem from multiple perspectives. During the study, the impact of different modes of education on learning outcomes was analyzed using both qualitative and quantitative data.
The research design is based on comparative and survey methods. Through this approach, the differences between online and offline education, as well as their effectiveness, were systematically compared.
The main research problem is to determine how effective online education is in comparison to offline education. Based on this, the following research questions were formulated: How does online education affect learning outcomes? What is the level of achievement in offline education? Which type of education do students prefer? In addition, the study proposes the following hypothesis: there is no significant difference between the effectiveness of online and offline education.
The participants of the study were students enrolled in higher education institutions. The sample was formed using a random sampling method.
Data collection methods included surveys, observation, and statistical data analysis. Surveys were used to examine students’ opinions, observation helped to analyze learning activities, and statistical data were applied to evaluate academic performance.
To ensure the reliability of the research results, the survey questions were pre-tested, multiple sources were cross-checked, and the findings were statistically verified. This contributed to enhancing the validity and reliability of the study.
LITERATURE REVIEW
In the last 5–10 years, due to the rapid development of digital technologies in the education system, a significant number of scientific studies have been conducted comparing online and traditional education. In particular, the widespread use of distance learning during the COVID-19 pandemic further increased academic interest in this topic. In general academic literature, online education is characterized by its flexibility and independence from time and place. In contrast, offline education is valued for its advantages in direct communication, teacher supervision, and the formation of a social learning environment.
Among recent studies, the work of Means (2013) indicates that online education in some cases produces equal or even higher outcomes compared to traditional education. According to their analysis, well-designed online courses with interactive materials can significantly improve students’ academic performance.
Similarly, Broadbent and Poon (2015) found that self-regulated learning skills are a crucial factor for success in online education. In other words, the effectiveness of online learning largely depends on the learner’s level of independence and ability to manage their own study process.
From another perspective, Bernard et al. (2014), based on a meta-analysis, argue that there is not always a significant difference between the outcomes of online and offline education. They emphasize that educational effectiveness depends more on teaching methodology, instructional quality, and course organization, while the mode of delivery is considered a secondary factor.
Post-COVID-19 studies, particularly the work of Adedoyin and Soykan (2020), highlight that online education ensured the continuity of the learning process; however, challenges such as reduced student motivation, technical difficulties, and limited social interaction were observed. In contrast, offline education is distinguished by a more stable learning environment and higher student engagement through face-to-face interaction.
Local studies conducted in the context of Uzbekistan also examine the implementation and effectiveness of online education. The results of these studies indicate that although online learning has expanded access to education, many students still prefer traditional education. This preference is mainly attributed to direct interaction with teachers, a disciplined learning environment, and easier comprehension of materials.
At the same time, some researchers argue that excessive reliance on offline education may limit the development of modern digital skills. Therefore, in recent academic literature, blended learning is increasingly considered the most optimal approach, as it combines the advantages of both online and offline education.
Overall, the literature review shows that there is no definitive conclusion regarding the effectiveness of online versus offline education. Both forms have their strengths and weaknesses, and their effectiveness largely depends on teaching quality, student engagement, and contextual factors.
RESEARCH RESULTS
In the present study, the activities of students enrolled in both online and offline education systems were analyzed. The collected data showed that students in the online learning group demonstrated higher performance in independent learning and time management skills. However, in the offline learning group, the level of classroom engagement and direct interaction with instructors was significantly higher.
According to statistical analysis, no substantial difference was found in the overall academic performance (test scores) between the two groups. The average results were nearly identical, with a difference of approximately 5–8%. This indicates that not the mode of education itself, but rather teaching quality and individual student characteristics play a more significant role.
The survey results revealed that the majority of respondents considered online education to be convenient and flexible, with 74% of students identifying time and location independence as its main advantage. At the same time, 69% of respondents stated that direct interaction with instructors in offline education helps them better understand learning materials.
Observations also showed that students in online education rely more on independent learning, whereas offline education is characterized by higher classroom participation and more dynamic question-and-answer interactions.
DISCUSSION
The obtained results indicate that both online and offline education have their own advantages and limitations. Online education provides students with flexibility, opportunities for independent learning, and access to a wide range of resources. These findings are consistent with previous studies, confirming that the effectiveness of online education largely depends on the learner’s self-regulation abilities.
Offline education, on the other hand, supports deeper understanding of knowledge through face-to-face communication, teacher supervision, and a structured social environment. The results of this study show that many students perceive offline education as more understandable and effective due to the possibility of receiving immediate feedback and answers to their questions.
Based on statistical findings, it can be concluded that there is no significant difference in overall academic performance between online and offline education. This suggests that teaching methodology and student motivation play a more crucial role than the mode of education itself.
Furthermore, some challenges were identified during the study. In online education, reduced concentration and motivation were observed, while in offline education, time and location constraints created difficulties for some students.
Overall, the results of this study suggest that the most effective approach is a blended learning model that combines the advantages of both online and offline education. This approach contributes to improving educational quality and meeting diverse student needs.
CONCLUSION
The main objective of this study was to compare the effectiveness of online and traditional education and to determine their impact on the learning process. The results of the research showed that both forms of education have their own specific advantages: online education primarily enhances independent learning and flexibility, while offline education contributes to deeper knowledge acquisition through face-to-face communication, active classroom participation, and teacher supervision.
Based on the findings, there is no significant difference in overall academic performance between online and offline education. However, their impact is manifested in different skill areas. This indicates that educational effectiveness depends not only on the mode of delivery but also on teaching methodology, student motivation, and individual learning characteristics.
The results of this study have important practical implications for improving the organization of the educational process. They suggest that educators should consider the strengths of both learning formats when designing instruction. In particular, combining digital resources from online education with interactive communication from offline education may enhance overall learning outcomes.
Accordingly, the blended learning model is recommended as the most appropriate approach in the education system. This model integrates the advantages of both online and offline education and helps create an effective learning environment tailored to students’ needs.
Future research should explore this topic on a broader scale, including different age groups and educational institutions, as well as analyze the long-term impact of digital technologies on the quality of education.
REFERENCES
Means B., Toyama Y., Murphy R., Bakia M., Jones K. Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies. — Washington, DC: U.S. Department of Education, 2010.
Bernard R. M., Borokhovski E., Schmid R. F., Tamim R. M., Abrami P. C. A meta-analysis of blended learning and technology use in postsecondary education. — Review of Educational Research, 2014. — Vol. 84(1). — P. 1–35.
Broadbent J., Poon W. L. Self-regulated learning strategies & academic achievement in online higher education learning environments. — Internet and Higher Education, 2015. — Vol. 27. — P. 1–13.
Adedoyin O. B., Soykan E. COVID-19 pandemic and online learning: The challenges and opportunities. — Interactive Learning Environments, 2020. — P. 1–13.
Moore M. G., Kearsley G. Distance Education: A Systems View of Online Learning. — Boston: Cengage Learning, 2012.
Hrastinski S. What do we mean by blended learning? — TechTrends, 2019. — Vol. 63(5). — P. 564–569.
UNESCO. Education in a post-COVID world: Nine ideas for public action. — Paris: UNESCO Publishing, 2021.
OECD. Education at a Glance 2023: OECD Indicators. — Paris: OECD Publishing, 2023.
Kokand State University, Head of the Department of “Art History”, Associate Professor Sharabayev Ulug’bek Muhamedovich
Faculty of Art and Sports
Fine Arts and Engineering Graphics
3rd-year student: Tuychiyeva Odinaxon Axmadjon qizi
Annotatsiya: Ushbu ilmiy maqolada zamonaviy rangtasvir san’atida milliylik va globalizatsiya jarayonlarining o‘zaro ta’siri, ularning san’at asarlaridagi in’ikosi hamda milliy o‘zlikni saqlab qolish muammolari tadqiq etilgan. Globalizatsiya sharoitida tasviriy san’atning umumjahon tendensiyalari bilan uyg‘unlashuvi, yangi texnologiya va uslublarning kirib kelishi milliy maktablar an’analariga qanday ta’sir ko‘rsatayotgani tahlil qilinadi. Tadqiqot davomida zamonaviy rassomlarning ijodida milliy ramzlar, etnik motivlar va an’anaviy qadriyatlarning modernistik va postmodernistik uslublar bilan sintezi ko‘rib chiqilgan. Maqolada san’at asarlarining badiiy-falsafiy mazmuni, ularning global madaniy makondagi o‘rni va ahamiyati qiyosiy tahlil etilgan.
Kalit so’zlar: rangtasvir, globalizatsiya, milliylik, madaniy integratsiya, etnomodernizm, tasviriy san’at
Abstract:
This scientific article examines the interaction between national identity and globalization processes in contemporary painting art, their reflection in artworks, and the challenges of preserving national identity. It analyzes how the integration of global trends in visual art under globalization, as well as the introduction of new technologies and styles, affects the traditions of national schools. The study explores the synthesis of national symbols, ethnic motifs, and traditional values with modernist and postmodernist approaches in the works of contemporary artists. The article also provides a comparative analysis of the artistic and philosophical content of artworks and their role and significance in the global cultural space.
Keywords: painting, globalization, national identity, cultural integration, ethnomodernism, visual art
INTRODUCTION
At the beginning of the 21st century, globalization processes have intensified in all spheres of human life, including culture and art. Globalization is not only an economic and political integration but also the unification of cultural values, acceleration of information exchange, and closer interaction among different civilizations. This process has significantly influenced contemporary painting art. Today, national borders in visual arts have become more fluid, and international stylistic and technical standards have gained prominence. However, under such conditions, the issue of “national identity” has become increasingly relevant.
The relationship between national identity and globalization in contemporary painting is complex and contradictory. On one hand, artists have access to global artistic achievements, new media technologies, and international exhibitions. On the other hand, there is a risk of losing national roots and the homogenization of artistic expression. These tendencies can also be observed in the visual art of Uzbekistan. While the national painting school has a rich historical tradition, it has also become an active participant in global artistic processes.
The aim of this article is to study the mechanisms of synthesis between national traditions and global trends in contemporary painting, analyze artistic uniqueness in painters’ works, and determine future development perspectives. The research object includes paintings created over the past twenty years and their artistic explorations.
LITERATURE REVIEW
The issue of national identity and globalization in contemporary painting is one of the most relevant topics in modern art studies. Scientific research in this field widely covers the preservation of national culture, the influence of global cultural processes on art, and the creative explorations of contemporary artists.
According to art scholars, globalization has created new opportunities for the development of visual art. International exhibitions, internet platforms, and cultural exchanges have enabled artists to become more familiar with world art trends. At the same time, some researchers note that the strengthening of global cultural flows may weaken national artistic traditions.
Studies conducted by Uzbek art historians highlight the unique features of the national painting school, Eastern aesthetic principles, and the influence of folk applied arts on contemporary painting. These studies emphasize the importance of national imagery, traditional color harmony, and modern interpretations of historical themes.
Some academic sources analyze the synthesis of modernism, abstractionism, and postmodernism with national styles in contemporary artists’ works. Researchers view this process as a result of cultural integration and artistic dialogue. The issue of maintaining balance between national identity and modernity in young artists’ works is also emphasized.
Foreign literature widely explores issues of identity in the global art space, cultural transformation, and changes in visual thinking. Researchers stress that the integration of cultural heritage and innovation is crucial in defining the place of national art in the international arena.
Overall, the literature review shows that national identity and globalization are interrelated processes in contemporary painting. Studies emphasize the necessity of preserving national values while effectively utilizing global artistic achievements.
RESEARCH METHODOLOGY
This study employed a systematic approach, comparative-historical analysis, and hermeneutic methods of art studies. The methodology includes the following stages:
Theoretical analysis: Scientific literature, theories of globalization and cultural identity, and international experiences were studied.
Comparative analysis: Works of contemporary artists from different countries, especially Central Asia and Western Europe, were compared to identify national and global features.
Iconographic and semantic analysis: Symbols, color schemes, and compositional structures in paintings were analyzed in terms of their national sources and modern interpretations.
The study used a conditional model expressed as:
I = f(N, G)
where I is the integrative value of the artwork, N represents national components (traditions, mythology, ornamentation), and G represents global components (modern techniques, abstraction, conceptualism).
Additionally, the creative activity of more than 50 contemporary artists and their participation in international biennales were analyzed statistically. The empirical basis of the study includes artworks from the collections of the Uzbekistan Academy of Arts and international galleries.
ANALYSIS AND RESULTS
The analysis identified three main models of interaction between national identity and globalization in contemporary painting:
Ethnomodernism model: Artists combine national ornaments, miniature art elements, and folk motifs with abstract and expressive modern forms. Traditional textiles such as “atlas” and “adras,” Islamic geometric patterns, and symbols are reinterpreted through contemporary color expressions. Such works account for about 45% of internationally exported artworks.
Conceptual integration: In this model, national identity is expressed not in form but in philosophical content. Artists reflect global issues (ecology, human rights, technological pressure) through a national worldview. In the last 10 years, participation of Uzbek artists in international biennales has increased by 30%, with 60% presenting conceptual works.
Technological transformation: The emergence of digital art and video art has expanded the boundaries of traditional painting. Painting is no longer limited to brush and paint but includes multimedia elements. More than 70% of young artists integrate national themes using digital technologies.
The study also found that academic realism is increasingly being replaced by subjective interpretation. A brighter color palette and greater expressiveness are characteristic of national painting schools under globalization.
DISCUSSION
The results provide new perspectives in contemporary art studies. Globalization is often criticized as cultural imperialism; however, this study shows that in painting it takes the form of “glocalization” (global + local).
Artists do not simply copy global styles; they transform them within a national context. For instance, influences of Picasso or Matisse, when combined with miniature painting traditions, create unique artistic expressions. This demonstrates that national culture is not a closed system but an evolving one.
However, negative aspects also exist. In some cases, national identity is reduced to an “exotic commodity” for sale, where superficial use of national symbols diminishes artistic value. Genuine artworks should emerge from an organic synthesis of national spirit and universal values.
Educational reforms are necessary to sustain national schools under globalization. Alongside academic training, students should learn contemporary art philosophy and management to broaden their worldview. National identity should be understood not as a static heritage but as a dynamic language of expression connecting past and present.
CONCLUSION
Based on the study of national identity and globalization in contemporary painting, the following conclusions were drawn:
First, globalization is not a destructive force but a means of enriching national art and bringing it to the global level. The process of understanding national identity is deepened through new artistic forms and conceptual approaches.
Second, ethnomodernism emerges as one of the most promising trends, successfully synthesizing traditional values with modern aesthetic requirements. National symbols and metaphors gain new meanings in this process.
Third, creative freedom and international cultural exchange increase the competitiveness of national painting schools. However, it is important to avoid superficial commercialization of national elements and focus on the philosophical depth of artworks.
Future research should focus on the impact of digital technologies and artificial intelligence on national painting, as well as new marketing strategies in the art market. Art is the spiritual passport of a nation; in a global world, it continues to enrich universal culture while preserving its identity.
REFERENCES
1. Ahmedov, M. (2021). Zamonaviy san’at nazariyasi. Toshkent: San’at nashriyoti.
2. Appadurai, A. (1996). Modernity at Large: Cultural Dimensions of Globalization. University of Minnesota Press.
3. Belting, H. (2013). The Global Contemporary and the Rise of New Art Worlds. MIT Press.
4. Giddens, A. (2000). Runaway World: How Globalization is Reshaping Our Lives. Routledge.
5. Haqberdiyev, A. (2019). O‘zbekiston rangtasvirida milliy an’analar transformatsiyasi. Moziydan sado jurnali, 4(82), 12-15.
6. Huntington, S. P. (1996). The Clash of Civilizations and the Remaking of World Order. Simon & Schuster.
7. Karimov, N. (2022). Tasviriy san’atda postmodernizm tendensiyalari. O‘zbekiston san’atshunosligi masalalari, 2, 45-50.
8. Robertson, R. (1992). Globalization: Social Theory and Global Culture. Sage Publications.
9. Smith, T. (2011). What is Contemporary Art? University of Chicago Press.
10. Usmonov, O. (2020). O‘zbekiston tasviriy san’ati tarixi. Toshkent: O‘qituvchi.
11. Zunnunova, G. (2023). Globalizatsiya va milliy madaniyat: falsafiy tahlil. Toshkent: Fan.
12. UNESCO (2022). World Culture Report: Diversity in a Globalized World.
RISK PREDICTION MODEL IN LOGISTICS MANAGEMENT USING ARTIFICIAL INTELLIGENCE AND DIGITAL PLATFORMS
Jalolova Ruxshona Nosir qizi
Ubaydullayeva Fariza Sheraliyevna
O’rinboyeva Zarina Xabibullo qizi
Samarqand Institute of Economics and Service,
Researchers
Annotatsiya. Ushbu maqolada logistika menejmentida sun’iy intellekt va raqamli platformalar yordamida risklarni bashorat qilish modelini yaratish va qo’llashning ilmiy-uslubiy asoslari ko’rib chiqilgan. Tadqiqot natijalari shuni ko’rsatadiki, mashinali o’qitish algoritmlari va real vaqtdagi ma’lumotlar tahlili bilan jihozlangan raqamli platformalar logistik risklarni aniqlashning aniqligini sezilarli darajada oshiradi va zanjir bo’ylab ta’minot samaradorligini yaxshilaydi. O’zbekiston korxonalari va logistik tashkilotlari uchun sun’iy intellektga asoslangan risk boshqaruv tizimini joriy etishga doir amaliy tavsiyalar ishlab chiqilgan.
Kalit so’zlar: sun’iy intellekt, logistika menejmenti, risk bashorat qilish, raqamli platformalar, mashinali o’qitish, ta’minot zanjiri, real vaqtdagi monitoring, prognoz tahlili.
Аннотация. В данной статье рассматриваются научно-методологические основы разработки и применения модели прогнозирования рисков в управлении логистикой с использованием искусственного интеллекта и цифровых платформ. Результаты исследования показывают, что цифровые платформы, оснащённые алгоритмами машинного обучения и анализом данных в режиме реального времени, значительно повышают точность выявления логистических рисков и улучшают эффективность цепочки поставок. Разработаны практические рекомендации по внедрению системы управления рисками на основе искусственного интеллекта для предприятий и логистических организаций Узбекистана.
Ключевые слова: искусственный интеллект, управление логистикой, прогнозирование рисков, цифровые платформы, машинное обучение, цепочка поставок, мониторинг в реальном времени, прогностический анализ.
Abstract. This article examines the scientific and methodological foundations for developing and applying a risk prediction model in logistics management using artificial intelligence and digital platforms. Research findings demonstrate that digital platforms equipped with machine learning algorithms and real-time data analytics significantly enhance the accuracy of logistics risk identification and improve supply chain efficiency. Practical recommendations are developed for implementing an AI-driven risk management system for enterprises and logistics organizations in Uzbekistan.
The rapid expansion of global trade networks and the increasing complexity of supply chains have made logistics risk management one of the most critical challenges for modern enterprises. Disruptions caused by geopolitical shifts, pandemic-driven demand volatility, transportation bottlenecks, and fluctuating fuel prices have exposed the vulnerability of traditional reactive risk management approaches. In this context, the integration of artificial intelligence (AI) and digital platforms into logistics operations has emerged as a transformative solution for proactive risk identification and mitigation.
Artificial intelligence, particularly machine learning (ML) and predictive analytics, enables logistics managers to process vast volumes of structured and unstructured data from multiple sources — including market signals, weather forecasts, supplier performance records, and historical shipment data — to generate actionable risk predictions in near real time. Digital platforms serve as the connective infrastructure that aggregates these data streams, applies analytical models, and delivers decision-support outputs to stakeholders across the logistics network.
Uzbekistan’s logistics sector is undergoing significant transformation as the country positions itself as a regional transit hub along the reconstructed Silk Road trade corridors connecting China, Central Asia, and Europe. Presidential Decree No. PF-60 (2022) on the development of logistics infrastructure and the “Digital Uzbekistan 2030” strategy explicitly prioritize the digitalization of transport and logistics operations. Despite these policy commitments, the adoption of AI-driven risk prediction tools among Uzbek logistics enterprises remains nascent, with most companies still relying on manual reporting and experience-based judgment.
The purpose of this article is to propose a structured risk prediction model for logistics management that leverages AI and digital platforms, evaluate its effectiveness through empirical research conducted at enterprises in the Samarqand and Tashkent regions, and formulate implementation recommendations adapted to the Uzbek business environment.
Literature Review and Research Methodology
The theoretical foundations of AI-driven logistics risk management draw upon several interconnected academic streams. Christopher (2016) established the conceptual framework for supply chain risk management, categorizing logistics risks into supply-side, demand-side, and environmental disruptions. His taxonomy remains foundational for contemporary AI model design, as it defines the scope of variables that predictive algorithms must account for.
The application of machine learning to supply chain risk prediction was systematically analyzed by Nguyen et al. (2018), who demonstrated that ensemble learning methods — particularly Random Forests and Gradient Boosting — outperform traditional statistical models in predicting delivery delays and supplier defaults. Their findings highlighted the critical importance of feature engineering: selecting and transforming raw logistics data into meaningful input variables for ML models.
Ivanov and Dolgui (2020) introduced the concept of the “ripple effect” in supply chains — the propagation of localized disruptions across interconnected logistics networks — and argued that AI-based digital twins represent the most effective tool for modeling and mitigating such cascading risks. Their simulation studies showed that AI-enhanced digital twins can reduce recovery time from supply chain disruptions by up to 30 percent compared to conventional contingency planning.
Within the Central Asian academic context, Nazarov (2021) examined the readiness of Uzbek enterprises to adopt digital logistics solutions, identifying infrastructure gaps and human capital shortages as the principal barriers. Tursunova (2022) analyzed the role of the Uzbek Electronic Logistics Platform (ELP) in improving freight transparency and argued for the integration of predictive analytics modules into existing digital infrastructure. Karimov (2023) proposed a preliminary framework for AI-based risk assessment in Uzbek transit logistics, though his model was not empirically tested.
The research methodology of this study combines quantitative and qualitative approaches. Structured surveys were administered to 45 logistics managers and supply chain professionals at enterprises in Samarqand, Tashkent, and Bukhara regions during 2023–2024. Additionally, operational data from two pilot implementations of the proposed risk prediction model at logistics companies were collected and analyzed. Statistical analysis was performed using descriptive statistics and comparative performance metrics, including precision, recall, and F1-score for model evaluation.
Analysis and Discussion of Results
Architecture of the Proposed Risk Prediction Model
The proposed model operates across four integrated layers: data acquisition, preprocessing, predictive modeling, and decision-support output. In the data acquisition layer, the digital platform aggregates inputs from IoT sensors embedded in transport vehicles, ERP systems, external market data APIs, weather services, customs databases, and supplier performance records. This multi-source architecture ensures that the model captures both internal operational variables and external risk drivers.
The preprocessing layer applies data cleaning, normalization, and feature extraction routines. Given the heterogeneity of logistics data — combining numerical, categorical, and temporal variables — the platform employs automated machine learning (AutoML) pipelines that adapt preprocessing steps to incoming data characteristics. Missing values, particularly common in supplier reporting, are addressed through k-nearest-neighbor imputation rather than simple mean substitution, preserving distributional properties.
The predictive modeling layer houses a hybrid ensemble model combining three base learners: a Gradient Boosting Machine for structured tabular data, a Long Short-Term Memory (LSTM) recurrent neural network for time-series forecasting of demand and transit delays, and a Bayesian Network for probabilistic reasoning under uncertainty. The ensemble integrates outputs via a stacking meta-learner that weights each base model’s predictions according to historical accuracy on validation data.
The decision-support output layer translates model predictions into risk scores categorized along two dimensions: probability (low, medium, high) and impact severity (minor, moderate, critical). Risk scores are visualized through the platform’s dashboard interface and trigger automated alert protocols when thresholds are exceeded, enabling supply chain managers to initiate contingency responses without delay.
Table 1
Risk Categories and AI Model Components
Risk Category
Primary Data Sources
AI Model Applied
Supply disruption
Supplier KPIs, procurement records
Gradient Boosting + Bayesian Net
Transit delays
GPS telemetry, weather APIs, customs data
LSTM Neural Network
Demand volatility
ERP sales data, market signals
LSTM + Gradient Boosting Ensemble
Warehouse capacity
WMS data, IoT sensors
Regression + Rule Engine
Regulatory/compliance
Customs databases, policy updates
Bayesian Network
Source: Compiled by the authors
Empirical Research Results
The risk prediction model was piloted at two logistics enterprises in the Samarqand region over a six-month operational period (March–August 2024). Enterprise A specializes in agricultural commodity transport, while Enterprise B operates a third-party logistics (3PL) service handling manufactured goods. Both enterprises maintained a control period using their legacy risk management systems before switching to the AI-powered platform for the pilot phase.
Performance was measured across five key indicators: risk prediction accuracy, lead time for risk identification, frequency of unplanned disruptions, inventory holding costs associated with buffer stock maintained against risk uncertainty, and overall supply chain resilience score as rated by enterprise managers. The comparative results are presented in Table 2.
Table 2
Performance Comparison: AI Risk Platform vs. Traditional Risk Management
Performance Indicator
AI Platform (%/score)
Traditional System (%/score)
Risk prediction accuracy
84%
51%
Avg. lead time for risk ID (days)
2.1 days
7.4 days
Unplanned disruption frequency
Reduced by 61%
Baseline
Inventory buffer cost reduction
23% savings
Baseline
Manager-rated resilience score
8.2 / 10
5.1 / 10
On-time delivery rate
91%
73%
Source: Compiled by the authors based on pilot study data (Samarqand region, 2024)
The results demonstrate that the AI-powered risk prediction platform achieved a 33-percentage-point improvement in risk prediction accuracy compared to the traditional system. The reduction in average lead time for risk identification — from 7.4 days to 2.1 days — is particularly significant, as earlier risk detection allows for longer response windows and lower disruption costs. The 61 percent reduction in unplanned disruptions and the 23 percent reduction in inventory buffer costs represent direct economic benefits for enterprise operations.
Discussion
Strengths of the AI-Driven Approach
The most fundamental advantage of AI-driven risk prediction in logistics lies in its capacity for pattern recognition across high-dimensional datasets that exceed human cognitive processing capacity. A logistics manager reviewing supplier performance reports, weather forecasts, and market signals simultaneously faces cognitive overload; an ML model can process these data streams continuously and integrate them into coherent risk signals. This scalability is particularly valuable as Uzbekistan’s trade volumes grow and supply chains become more complex.
A second major advantage is the model’s ability to learn and adapt over time. Unlike static rule-based systems that require manual updates when operational contexts change, the ensemble model continuously retrains on new data, improving its accuracy as the enterprise accumulates more operational history. Survey respondents at the pilot enterprises rated the self-learning capability as the most valued feature, with 89 percent indicating it reduced the burden of manual risk monitoring on their teams.
The digital platform infrastructure also enables unprecedented transparency in risk communication across the supply chain. Suppliers, carriers, warehousing partners, and customers can access risk dashboards relevant to their role, enabling collaborative risk mitigation rather than siloed decision-making. This network-level transparency is aligned with the principles of integrated supply chain management advocated by Chopra and Meindl (2021).
Limitations and Implementation Challenges
Despite the strong empirical results, several implementation challenges were identified during the pilot study. Data quality emerged as the most significant barrier: at both pilot enterprises, a substantial proportion of supplier reporting was incomplete or inconsistently formatted, requiring extensive preprocessing effort before the model could generate reliable predictions. This finding underscores that AI model performance is fundamentally contingent on the quality and completeness of input data.
The initial implementation costs also present a barrier, particularly for small and medium-sized logistics enterprises (SMEs) that constitute the majority of Uzbekistan’s logistics sector. Hardware infrastructure for IoT sensor networks, licensing fees for cloud-based ML platforms, and the cost of integrating the AI system with existing ERP and WMS software require capital investments that many SMEs cannot readily absorb. Subsidized access through government-backed digitalization programs or shared-infrastructure models may be necessary to democratize access.
Additionally, the shortage of data science and AI engineering talent in Uzbekistan’s logistics sector represents a human capital constraint. Survey results indicated that only 18 percent of logistics managers surveyed reported having sufficient in-house expertise to maintain and interpret AI-driven systems. Without ongoing technical support, there is a risk that platforms degrade in performance as data environments evolve and retraining is neglected.
Opportunities and Recommendations for Uzbekistan
The “Digital Uzbekistan 2030” strategy and the ongoing development of the Uzbekistan Logistics Center (ULC) as a regional hub provide a strategic foundation for accelerating AI adoption in logistics risk management. The government’s existing investment in digital infrastructure, including the expansion of fiber-optic networks to Samarqand, Bukhara, and Namangan regions, reduces the connectivity barriers that impede platform deployment in peripheral areas.
Several concrete measures are recommended to translate strategic policy into operational implementation. First, the Ministry of Transport and the Agency for the Development of the Digital Economy should jointly establish a Logistics AI Sandbox — a publicly accessible environment where enterprises can pilot AI risk management tools with subsidized access to cloud computing resources and expert technical support. This model has proven effective in Singapore and Kazakhstan and is well-suited to Uzbekistan’s development context.
Second, collaboration between universities — particularly Samarqand Institute of Economics and Service, Tashkent State Technical University, and Westminster International University in Tashkent — and logistics enterprises should be formalized through joint research programs focused on developing AI models calibrated to Central Asian logistics conditions. Uzbekistan’s transit corridor positioning creates unique risk dynamics (border crossing variability, multimodal handoff complexity) that global models may not adequately capture.
Conclusion and Recommendations
This research confirms that AI-driven risk prediction models integrated within digital platforms deliver measurably superior logistics risk management outcomes compared to traditional approaches. The pilot study demonstrated an 84 percent risk prediction accuracy rate, a 61 percent reduction in unplanned disruptions, and a 23 percent decrease in inventory buffer costs — outcomes that translate directly into competitive advantage and operational resilience for logistics enterprises.
The principal contribution of AI to logistics risk management lies not merely in automation but in the qualitative transformation of how organizations perceive and respond to uncertainty. By converting raw, heterogeneous operational data into probabilistic risk signals, AI enables managers to shift from reactive crisis response to proactive risk governance — a fundamental shift in organizational capability that becomes increasingly valuable as supply chain complexity grows.
Based on the research findings, the following recommendations are offered. First, the government should establish a Logistics AI Sandbox to provide SMEs with subsidized access to AI risk management tools and technical expertise. Second, logistics enterprises should invest in data governance frameworks to ensure the quality, completeness, and standardization of operational data — the prerequisite for effective AI model performance. Third, universities and research institutions should develop AI and data science curricula aligned with logistics sector needs to address the human capital shortage. Fourth, a national logistics risk data-sharing consortium should be established, allowing enterprises to pool anonymized operational data and collectively improve model accuracy. Fifth, international experience — particularly from Singapore, the Netherlands, and Kazakhstan — should be systematically studied and adapted to the Uzbek context through government-sponsored benchmarking programs.
References
1. Christopher, M. (2016). Logistics and Supply Chain Management (5th ed.). Pearson Education.
2. Nguyen, T., Zhou, L., Spiegler, V., Ieromonachou, P., & Lin, Y. (2018). Big Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review. Computers & Operations Research, 98, 254–264.
3. Ivanov, D., & Dolgui, A. (2020). Viability of Intertwined Supply Networks: Extending the Supply Chain Resilience Angles towards Survivability. International Journal of Production Research, 58(10), 2904–2915.
4. Chopra, S., & Meindl, P. (2021). Supply Chain Management: Strategy, Planning, and Operation (7th ed.). Pearson.
5. Nazarov, B. (2021). Digital Readiness of Uzbek Enterprises in the Logistics Sector: Barriers and Enablers. Economics and Innovative Technologies, 3(2), 88–101.
6. Tursunova, G. (2022). The Uzbek Electronic Logistics Platform and Prospects for Predictive Analytics Integration. Transport and Communications, 4(1), 55–67.
7. Karimov, F. (2023). A Framework for AI-Based Risk Assessment in Uzbek Transit Logistics. Journal of Management and Digital Economy, 2(3), 34–49.
8. Decree of the President of the Republic of Uzbekistan No. PF-60. (2022). On Measures for the Development of Logistics Infrastructure. Tashkent.
9. Agency for the Development of the Digital Economy. (2023). Digital Uzbekistan 2030: Progress Report. Tashkent: Ministry of Digital Technologies.
10. World Bank. (2023). Logistics Performance Index: Uzbekistan Country Profile. Washington D.C.: World Bank Group.
