«Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025

December , 2025 № 12 Oslo, Norway 18-19.12.2025 Proceedings of the 12th International Scientific Conference 2 UDC 001.1 P 97 Publisher.agency: Proceedings of the 12th International Scientific Conference «Theoretical Hypotheses and Empirical results» (December 18 - 19, 2025). Oslo, Norway, 2025. 346p ISBN 978-9-4641-5779-6 DOI 10.5281/zenodo.18013994 Editor: Mary Olafsen, Professor, Nord University International Editorial Board: Anja Kazemi Professor, University of South-Eastern Norway Ståle Shokri Professor, Universitetslektor - Universitetet i Sørøst-Norge Karen Foray Professor, University of Oslo Hosein Nilsen Professor, USN School of Business Aida Drake Professor, Buskerud University College Gjerdalen Rolfson Professor, University College Southeast Norway Etty Allern Professor, Norwegian Business School Dr. Irmelin Kujanpää Professor, University of South-Eastern Norway Terje Øivind Madsen Professor, Bergen National Academy of the Arts Sigurd Sunagic Professor, Inland Norway University of Applied Sciences Miika Vesin Professor, Norwegian Naval Academy Dag Anderson-Glenna Professor, Norwegian School of Economics Mirha Seierstad Professor, Norwegian University of Life Sciences Boban Tavassoli Professor, Norwegian University of Science and Technology Cathrine Vikhagen Professor, OsloMet - Oslo Metropolitan University Sara Stendal Professor, University of Agder [email protected] https://publisher.agency/ «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 3 Table of Contents Pedagogical Sciences ACHIEVEMENT MOTIVATION IN LATE ADOLESCENCE: A SYSTEMATIC REVIEW OF EDUCATIONAL AND SOCIAL DETERMINANTS ..... 6 SANIYA NURMUKHAMBETOVA TEACHER MOTIVATION, WORKLOAD, AND RETENTION IN AZERBAIJAN’S GENERAL EDUCATION SYSTEM: A HUMAN RESOURCE MANAGEMENT PERSPECTIVE .................................................................................................................................................................... 9 MAHAMMAD AZIZI-MESHKIN ORGANIZATIONAL AND EDUCATIONAL CONDITIONS FOR THE FORMATION OF EFFECTIVE MANAGEMENT IN PROFESSIONAL BOXING ...................................................................................................................................................................................................... 19 KAZANKAPOV AMAN TELAKHYNOV YERKIN UMIRZAKOV YEDIGE THE TYPOLOGICAL STRUCTURES OF AZERBAIJANI AND ENGLISH .......................................................................................................... 26 NURAN MURSHUDZADA ARTIFICIAL INTELLIGENCE AS A TOOL FOR FORMING INTEREST IN MATHEMATICS AMONG STUDENTS WITH LOW MOTIVATION TO LEARN ........................................................................................................................................................................................................ 30 Y.S. KARYAKIN EXPLORING THE IMPACT OF DIGITAL MEDIA ON READING SKILLS ........................................................................................................ 34 A.S.OSPAN ЦИФРОВЫЕ ИНСТРУМЕНТЫ КАК СРЕДСТВО ПРОВЕРКИ ТЕОРЕТИЧЕСКИХ ГИПОТЕЗ В ОБУЧЕНИИ МАТЕМАТИКЕ ..................... 39 АЛДИБАЕВА ТУРАГАЛДЫ АБИЛАКИМОВНА INTERACTIVE METHODS FOR IMPROVING LEARNERS’ READING SKILLS AT THE ENGLISH LESSONS ................................................... 44 HALIMA MAMMADOVA ELMAR PEDAGOGICAL POTENTIAL OF 3D MODELING FOR DEVELOPING VISUAL LITERACY IN ART EDUCATION ........................................... 52 YESMAGAMBETOVA L.O. KRYKBAYEVA S.M. THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE TRANSFORMATION OF THE TEACHER'S ROLE .................................................... 62 ISKANDIROVA AIZHAN BAURZHANOVNA БИОЛОГИЯЛЫҚ BIG DATA ТАЛДАУДА ЖАСАНДЫ ИНТЕЛЛЕКТІНІҢ ИННОВАЦИЯЛЫҚ МҮМКІНДІКТЕРІ ...................................... 67 КЫДЫРБАЕВА Н.Х. МУКАШЕВА Д.М. АУЕЛБЕК М.А. FROM CLASSICAL TEXTS TO DIGITAL PLATFORMS .................................................................................................................................. 74 SEVINJ ALIYEVA DEVOLOPİNG İNCLUSİON İN EDUCATION ............................................................................................................................................... 77 QOCAYEVA NURANA MAMMAD MÜƏLLIMIN PEŞƏ USTALIĞI VƏ NÜFUZU ................................................................................................................................................ 81 RƏFIYEVA XURAMAN ƏLI QIZI Economic Sciences РОЛЬ КАЧЕСТВЕННОГО ЧЕЛОВЕЧЕСКОГО КАПИТАЛА В РАЗВИТИИ ДЕЛОВОГО ТУРИЗМА В КАЗАХСТАНЕ ................................. 85 ТЕМИРБЕКОВ А.Г. БЕРДИБЕКОВА А.Ш. МАЙДЫРОВА А.Б. INTERNATIONAL EXPERIENCE IN DENTAL BUSINESS MARKETING ......................................................................................................... 89 TAMAR ORJONIKIDZE МЕТОДОЛОГИЧЕСКИЕ ПОДХОДЫ К ОЦЕНКЕ ЭФФЕКТИВНОСТИ ИНТЕГРАЦИИ БИЗНЕСА: СРАВНИТЕЛЬНЫЙ АНАЛИЗ МОДЕЛЕЙ ................................................................................................................................................................................................. 97 А. ЦОЙ Н. НИКИФОРОВА REASSESSING GLOBAL INNOVATION METRICS: CRITICAL ANALYSIS OF GII INDICATORS AND INTANGIBLE INVESTMENT TRENDS 106 ARCHIL CHIRAKADZE NODAR MITAGVARIA NELI MAKHVILADZE TEIMURAZ CHUBINISHVILI NODAR SULASHVILI DEVA HARSHA UDAY GUNDLURU GIORGI PALAVANDISHVILI KHTUNA TSERODZE KAKHA GORGADZE NANA KHUSKIVADZE DAVID APHKHAZAVA Technical Sciences APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN AUTOMATION SYSTEMS .................................................................................. 138 BEKTEMІR ALMAS TANATULY JULAYEVA ZHAZIRA TULEGENOVNA KARATAYEVA ZHANBUBI YERZHANOVNA Proceedings of the 12th International Scientific Conference 4 РАЗРАБОТКА ПРОГРАММНОГО СРЕДСТВА `SME-PROMOOPTIMIZER` ДЛЯ ИНТЕЛЛЕКТУАЛЬНОГО АНАЛИЗА И СЕГМЕНТАЦИИ КЛИЕНТОВ МСБ ..................................................................................................................................................................................... 142 АХМЕТОВ ТИМУР ХАФИЗОВИЧ КИМ Е. Р. JUSTIFICATION OF THE PARAMETERS AND OPERATING MODES OF A BIOGAS PLANT FOR SMALL FARMS ..................................... 146 TELMANOV IMRAN SARKYNOV YERBOL ZHAKUPOVA ZHANAR ABILDA ZHANSAIYA АҚПАРАТТЫҚ ЖҮЙЕЛЕРДЕ ҚОРҒАЛҒАН АУТЕНТИФИКАЦИЯ: МОДЕЛЬДЕУ ЖӘНЕ ТАЛДАУ ......................................................... 151 ЕРГЕШ НҰРСҰЛТАН САГИТЯНҰЛЫ КАПАЛОВА НУРСУЛУ АЛДАЖАРОВНА РАЗРАБОТКА МОДЕЛИ ЛОГИСТИЧЕСКОЙ СИСТЕМЫ ПОСТАВКИ ПЛОДООВОЩНЫХ ГРУЗОВ, ПЕРЕВОЗИМЫХ ЖЕЛЕЗНОДОРОЖНЫМ ТРАНСПОРТОМ ............................................................................................................................................. 158 ЖАТКАНБАЕВА Э.А. БАҚЫТБЕК ҚҰШТАР RƏQƏMSAL İNFRASTRUKTURUN DİSTANT TƏHSİLİN KEYFİYYƏTİNƏ TƏSİRİ ÜZRƏ ANALİTİK ARAŞDIRMA ...................................... 162 PƏNAHOVA İLKANƏ MÜBARIZ QIZI ОБЗОР И СИСТЕМАТИЧЕСКОЕ ИССЛЕДОВАНИЕ СОВРЕМЕННЫХ ВЕБ‑АТАК, ЭКСПЛОЙТОВ И КИБЕРУГРОЗ 2025 ГОДА.......... 168 ТАНАТАР НУРГАЛИ Physical and Mathematical Sciences КОГНИТИВТІ КОНФЛИКТ ӘДІСІНІҢ МӘНІ ЖӘНЕ ОНЫҢ ФИЗИКА САБАҒЫНДАҒЫ РӨЛІ ................................................................ 178 СУЛТАНОВА ИНАРА АСКАРОВНА КАЗАХБАЕВА ДАНАКУЛЬ МУКАЖАНОВНА Legal Sciences INTERNATIONAL LEGAL REGULATION OF ARTIFICIAL INTELLIGENCE AND GEORGIA ......................................................................... 185 NINO BOTCHORISHVILI THE CRITERION OF ORIGINALITY IN WORKS CREATED BY ARTIFICIAL INTELLIGENCE ........................................................................ 194 DR. LIKA SAJAIA DIGITAL TRANSFORMATION OF JUSTICE IN THE AGE OF ARTIFICIAL INTELLIGENCE .......................................................................... 201 GIORGI SVIANADZE Philological Sciences A THEORETICAL STUDY OF INTERTEXTUALITY AND CULTURAL CODE IN AMERICAN CINEMA .......................................................... 207 AIMYSHEVA TOMIRIS SULEIMENKYZY NEUROBIOLOGICAL PATHWAYS OF FIRST WORD ACQUISITION .......................................................................................................... 214 ASMAR KARIMLI COMPARATİVE ELLİPSİS İN BRİTİSH AND AMERİCAN ENGLİSH ............................................................................................................ 218 GONCHA AHMADOVA Culturology FRANSIZ DILINI NIYƏ ÖYRƏNIRIK? .......................................................................................................................................................... 223 ƏKBƏROVA ƏSMAYƏ BƏXTIYAR QIZI Psychological Sciences ОСОБЕННОСТИ ЛОКУСА КОНТРОЛЯ И КОПИНГ-СТРАТЕГИЙ У МУЖЧИН СРЕДНЕГО ВОЗРАСТА С РАЗЛИЧНЫМИ ФОРМАМИ АДДИКТИВНОГО ПОВЕДЕНИЯ ............................................................................................................................................................. 227 ТОЛЕУХАНОВА ТОМИРИС ЕРЛАНОВНА ШИХОВА ОЛЬГА МИХАЙЛОВНА МЕХАНИЗМЫ ПСИХОЛОГИЧЕСКОЙ ЗАЩИТЫ: ПСИХИКА ЧЕЛОВЕКА И СТРЕСС ............................................................................ 247 ТУЛЕКОВА Г. Х БАЗАРБАЙ АНЕЛЬ ЕЛЕМЕСКЫЗЫ ВЛИЯНИЕ СОЦИАЛЬНЫХ СЕТЕЙ НА САМОВОСПРИЯТИЕ ЧЕЛОВЕКА .............................................................................................. 251 ТУЛЕКОВА Г. Х. ЫНТЫМАККЫЗЫ СЫМБАТ Sociological Sciences SOCIAL, CULTURAL, ECONOMIC AND DEMOGRAPHIC FACTORS INFLUENCING AI REGULATION IN KAZAKHSTAN .......................... 255 TOKHTAKHOUNOV B.N. NEGOTIATING SOCIAL IDENTITY IN URBAN SOCIAL SPACES ................................................................................................................ 266 SAGIDULLA G. S. «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 5 THE RELIGIOUS, SCIENTIFIC, CULTURAL, AND SOCIAL SIGNIFICANCE OF STUDYING BIOETHICS ....................................................... 274 YERIMBET RAMZAN YEDILBEKULY. E. AMANKUL TEMUR ORALBAIULY ERIMBET RIZUAN EDILBEKOVICH HIGHER EDUCATION AND RURAL YOUTH: A SYSTEMATIC BIBLIOGRAPHIC REVIEW .......................................................................... 278 SHAKENOV D. S. SARYBAYEVA I. S. Medical Sciences CLINICAL FEATURES OF EARLY DIAGNOSIS OF BREAST PATHOLOGY: THE ROLE OF ULTRASOUND IN THE DETECTION OF INFLAMMATORY, PRECANCEROUS DISEASES AND BREAST CANCER .................................................................................................. 289 ARMAN KHOZHAYEV MARAT MAULETBAYEV AIGERIM AUBAKIROVA ASSEM GARIPOVA DAMIRA BAICHAPANOVA AIDA MANGUTOVA ALEXANDR KHALMETOV Historical Sciences ТАРИХ САБАҒЫНДА СЕМЕЙ ПОЛИГОНЫН ТАРИХЫН ОҚЫТУДА ЖИ ҚОЛДАНУ ............................................................................. 302 Н.Ж. ТОКТАГАНОВ А.М. САДЫКОВА Political Studies THE ROLE OF SMALL AND MEDIUM-SIZED STATES IN THE NEW WORLD ORDER: THE EXAMPLE OF AZERBAIJAN .......................... 313 GÜLGÜN MÜBARIZ QULIYEVA PROBLEMS AND PROSPECTS OF DEVELOPING KAZAKHSTAN'S POLITICAL SCIENCE AT THE CURRENT STAGE ................................. 329 V.A. LUZANOV N.G. KABDII A.M. KENZHEBULATOVA Geographic Sciences İQLIM DƏYIŞIKLIYI VƏ YENI PANDEMIYALAR: XƏSTƏLIKLƏRIN COĞRAFI XƏRITƏSI NECƏ DƏYIŞIR? .................................................. 334 ƏLIYEVA ŞƏFƏQ MƏMMƏD QIZI Journalism DIGITAL COMMUNICATION TECHNOLOGIES AS MEANS OF EFFECTIVE COMMUNICATION IN ORGANISATIONS ............................ 338 ĽUDOVÍT HAJDUK STANISLAV BENČIČ Proceedings of the 12th International Scientific Conference 6 Pedagogical Sciences Achievement Motivation in Late Adolescence: A Systematic Review of Educational and Social Determinants Saniya Nurmukhambetova Master’s degree student (1st year) in Pedagogy and Psychology, A. Baitursynov Kostanay Regional University Abstract: This review synthesizes contemporary research (2010–2023) on the factors influencing the development of achievement motivation in adolescents aged 15–18. The focus is on the interplay between individual psychological characteristics, the educational environment, and the broader social context. The methodology involved a systematic search and analysis of articles in the Scopus, Web of Science, and PsycINFO databases using key term combinations. The review's findings are structured around three primary determinants: 1) the role of school and pedagogical practices, 2) the influence of family and peers, and 3) cognitive-personality factors (growth mindset, self-regulation). The analysis reveals that a supportive educational environment combining high expectations with autonomy is a key condition. Parental influence transforms from direct control to a consultative role, while peer influence becomes ambivalent, serving as both a source of support and social comparison. Special attention is paid to gender differences and the impact of the digital environment. The review identifies methodological gaps, including a lack of longitudinal and cross-cultural studies, and suggests directions for future research aimed at creating ecosystemic models of motivation support. Keywords: achievement motivation, older adolescents, educational environment, social factors, self-efficacy, review. Introduction The period of late adolescence (ages 15–18) is a critical stage in shaping life trajectories, where academic and career decisions begin to define an individual's future. Achievement motivation, understood as the drive to improve mastery, meet high standards, and overcome challenges (Dweck, 2006; Elliot & McGregor, 2001), becomes a central psychological resource during this transition. However, its manifestations and sustainability are not static; they are formed through a complex interaction between the developing individual and their environment. Despite an extensive body of research scattered across disciplines (educational psychology, sociology of education, developmental psychology), there is a need for consolidation. The aim of this review is to systematize contemporary empirical and theoretical data to build a comprehensive picture of the key determinants of achievement motivation in older adolescents, giving equal attention to internal and external factors. The central thesis of this review is that adaptive achievement motivation in late adolescence is the product of a dynamic ecosystem where personal beliefs (such as mindset) are activated and supported (or suppressed) by specific characteristics of the educational environment and the quality of social relationships. «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 7 Methodology This review follows the principles of a narrative systematic review (Snyder, 2019). A literature search was conducted in the Scopus, Web of Science, and PsycINFO databases for the period 2010–2023. Combinations of the following keywords were used: ["achievement motivation" OR "academic motivation"] AND ["late adolescence" OR "older adolescents" OR "high school students"] AND ["educational environment" OR "social factors" OR "parental influence" OR "peer influence"]. Inclusion criteria were met by empirical studies and meta-analyses published in peerreviewed English-language journals, focusing on the 15–18 age group. Studies concerning clinical populations or highly specific types of motivation (e.g., solely in sports) were excluded. The initial search yielded 482 articles. After screening titles, abstracts, and full texts for relevance, 78 works were included in the final review. Results and Discussion 1. The Educational Environment as a Key Context The topic sentence of this section asserts that school characteristics and teaching practices play a decisive role in translating an adolescent's potential motivation into sustainable behavioral patterns. Contemporary research is shifting focus from formal school indicators (e.g., size) to the psychological classroom climate (Ruzek et al., 2016). A supportive climate characterized by mutual respect, trust, and a sense of belonging serves as the foundation. More specifically, pedagogical practices that support autonomy (autonomy-supportive teaching) show the most robust association with intrinsic achievement motivation. These practices include providing choice within tasks, explaining the relevance of learning material, using non-controlling language, and acknowledging the student's perspective (Jang et al., 2010). For example, a study by Patall et al. (2018) showed that even minimal choice in homework completion significantly increased high school students' engagement and striving for quality. In contrast, a controlling environment emphasizing external rewards, punishments, and rigid social comparison fosters learned helplessness or fragile extrinsic motivation. 2. Social Factors: The Transforming Influence of Parents and Peers An adolescent's social context undergoes significant changes: while family influence remains important, its nature transforms, and the role of peers increases sharply. Parenting style evolves from direct control to a complex system of expectations, goal discussions, and emotional support (Pomerantz & Moorman, 2010). Parental expectations, when perceived as realistic and conveyed with belief in the child's competence (providing structure), correlate with high motivation and persistence. Conversely, excessively high, perfectionistic expectations or psychological control ("I will love you only if you become a straight-A student") lead to fear of failure and challenge avoidance (Kouros et al., 2020). Peer influence is paradoxical. On one hand, friendships based on mutual academic support create a "buffer zone" against stress and enhance self-efficacy. On the other hand, social comparison in the academic sphere, intensified by grade transparency and rankings, can lead to burnout and maladaptive perfectionism (Ryan & Shim, 2012). A new dimension of this comparison is the digital environment, where the showcasing of academic and extracurricular successes on social media creates additional pressure. 3. Cognitive-Personality Factors: Mindsets and Beliefs Internal filters through which an adolescent perceives educational and social influences are the ultimate mediators of motivation. Central here is the concept of growth mindset (Dweck, 2006). Adolescents who believe in the malleability of intelligence and abilities (growth mindset) are more likely to interpret difficulties as learning opportunities, demonstrate perseverance, and choose challenging tasks. Conversely, a fixed mindset leads to risk avoidance, a desire to "look smart" rather than "become smarter," and a sharp decline in motivation after failure. Inseparable from this is self-efficacy—the belief in one's ability to organize and execute the actions required to achieve a goal (Bandura, 1997). For adolescents, sources of self-efficacy include mastery Proceedings of the 12th International Scientific Conference 8 experiences (e.g., successfully completing a complex project), vicarious experience (observing peer successes), verbal persuasion from significant adults, and emotional states. The development of self-regulation skills, including goal setting, time management, and distraction control, serves as the operational tool that transforms motivation into concrete results (Zimmerman, 2013). Conclusion This review concludes that the achievement motivation of an older adolescent is neither a stable personality trait nor a simple sum of external incentives. It is a dynamic state arising at the intersection of a developing personality (with its mindsets, self-efficacy, and self-regulation skills) and a multi-layered context. Key elements of this context are: 1) an educational environment built on the principles of autonomy and mastery support rather than control and comparison; 2) a social network providing realistic expectations, emotional support, and models of adaptive behavior. This review also identifies significant research gaps. Primarily, there is a lack of cross-cultural studies accounting for the specifics of collectivistic and individualistic societies. Secondly, longitudinal studies tracking motivation trajectories during critical transitions (e.g., from high school to university) are needed. Thirdly, the rapidly changing digital environment requires new conceptualization both as a source of new challenges (social comparison, digital distraction) and new opportunities (online mastery courses, educational communities). A practical implication of this review is the necessity for developing ecosystemic intervention programs that simultaneously address adolescent beliefs, enhance teacher competency in motivational teaching, and engage parents as partners in supporting autonomy. References (Illustrative Fragment) 1. Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman. 2. Dweck, C. S. (2006). Mindset: The new psychology of success. Random House. 3. Elliot, A. J., & McGregor, H. A. (2001). A 2×2 achievement goal framework. Journal of Personality and Social Psychology, 80(3), 501–519. 4. Jang, H., Reeve, J., & Deci, E. L. (2010). Engaging students in learning activities: It is not autonomy support or structure but autonomy support and structure. Journal of Educational Psychology, 102(3), 588–600. 5. Kouros, C. D., et al. (2020). Helicopter parenting, autonomy support, and college students’ mental health and well-being. Emerging Adulthood, 8(3), 234–245. 6. Patall, E. A., et al. (2018). Daily autonomy supporting or thwarting and students’ motivation and engagement in the high school science classroom. Journal of Educational Psychology, 110(2), 269–288. 7. Pomerantz, E. M., & Moorman, E. A. (2010). Parents’ involvement in children’s schooling. In J. L. Meece & J. S. Eccles (Eds.), Handbook of research on schools, schooling, and human development (pp. 398–416). Routledge. 8. Ruzek, E. A., et al. (2016). How teacher emotional support motivates students: The mediating roles of perceived peer relatedness, autonomy support, and competence. Learning and Instruction, 42, 95–103. 9. Ryan, A. M., & Shim, S. S. (2012). Changes in help seeking from peers during early adolescence: Associations with changes in achievement and perceptions of teachers. Journal of Educational Psychology, 104(4), 1122–1134. 10. Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. 11. Zimmerman, B. J. (2013). From cognitive modeling to self-regulation: A social cognitive career path. Educational Psychologist, 48(3), 135–147. «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 9 Teacher Motivation, Workload, and Retention in Azerbaijan’s General Education System: A Human Resource Management Perspective Mahammad Azizi-Meshkin PhD student, University: ISTU Abstract Teacher motivation, workload, and retention are increasingly recognized as central determinants of education system quality and sustainability. While Azerbaijan has implemented substantial reforms in curriculum modernization, assessment mechanisms, and digital infrastructure within general education, comparatively limited attention has been paid to the human resource management (HRM) dimensions shaping teachers’ professional experiences. This article examines teacher motivation, workload, and retention in Azerbaijan’s general education system through an HRM perspective, drawing on international theoretical frameworks and secondary statistical evidence from OECD, UNESCO, the World Bank, and the International Labour Organization. The analysis reveals that excessive administrative workload, compliance-oriented performance management, limited career progression pathways, and uneven incentive structures undermine teacher motivation and increase retention risks, particularly among early-career and rural teachers. By reframing these challenges as systemic HR governance issues rather than individual shortcomings, the article proposes evidence-based policy and HRM reforms aimed at strengthening workforce sustainability, improving teacher motivation, and enhancing education quality in Azerbaijan. Keywords: Human Resource Management, Teacher Motivation, Workload, Retention, General Education, Azerbaijan, Public Sector HRM 1. Introduction Teachers constitute the most critical human resource in any education system, directly influencing student learning outcomes, institutional effectiveness, and long-term socioeconomic development. International research consistently demonstrates that motivated, well-supported, and professionally satisfied teachers are essential for achieving quality education and sustaining reform outcomes (OECD, 2021; UNESCO, 2023). Conversely, excessive workload, declining motivation, and high attrition rates undermine system performance and generate significant fiscal and social costs. Over the past decade, Azerbaijan has undertaken a series of reforms aimed at modernizing its general education system. These reforms have focused on curriculum renewal, digital learning platforms, centralized assessment mechanisms, and governance restructuring. While these initiatives have strengthened institutional capacity, they have also intensified expectations placed on teachers without proportionate adaptation of human resource management practices. As a result, concerns related to teacher workload, motivation, and retention have become increasingly salient. Despite their importance, these issues remain underexplored in the Azerbaijani academic literature, particularly from an HRM perspective. Teachers are often examined primarily as pedagogical actors rather than as public-sector employees embedded within complex HR Proceedings of the 12th International Scientific Conference 16 9.1 Strategic Workforce Planning and Forecasting A core priority for education governance should be the development of systematic workforce planning mechanisms. Currently, teacher recruitment and deployment processes are largely reactive, responding to immediate staffing needs rather than long-term demographic and regional trends. Strategic workforce planning would enable education authorities to anticipate teacher supply and demand across regions, subjects, and experience levels. By integrating demographic data, retirement projections, and regional enrollment trends, policymakers could proactively address emerging shortages, particularly in rural areas and STEM subjects. International experience demonstrates that data-driven workforce planning significantly improves retention outcomes by reducing job insecurity, preventing overburdening of existing staff, and ensuring more equitable teacher distribution (World Bank, 2022). For Azerbaijan, embedding workforce planning within national education strategies would strengthen institutional resilience and support sustainable reform implementation. 9.2 Workload Rationalization and Job Redesign Reducing excessive workload is essential for improving teacher motivation and preventing burnout. Workload rationalization should begin with a comprehensive audit of teachers’ noninstructional duties, including administrative reporting, data entry, and extracurricular supervision. International evidence indicates that many such tasks can be streamlined, automated, or reassigned without compromising accountability (OECD, 2021). In Azerbaijan, the expansion of digital education platforms has improved transparency but has also increased reporting requirements. HRM reforms should therefore focus on simplifying reporting processes, integrating digital systems, and clearly distinguishing between pedagogical and administrative responsibilities. Introducing school-level administrative support staff could significantly reduce teachers’ non-teaching workload, allowing them to concentrate on instructional quality and professional development. Effective job redesign not only improves efficiency but also enhances professional satisfaction by reinforcing teachers’ core identity as educators rather than administrative functionaries. 9.3 Motivation-Oriented Incentive Systems Motivation-enhancing HRM practices should balance financial and non-financial incentives. While salary improvements are necessary, international research consistently shows that recognition, professional autonomy, and career acknowledgment play equally important roles in sustaining motivation (Deci & Ryan, 2000; OECD, 2019). In the Azerbaijani context, performance-based recognition systems could be introduced to acknowledge pedagogical innovation, mentoring contributions, and sustained professional commitment. Such systems should prioritize transparency and developmental feedback rather than punitive evaluation. Public recognition, professional awards, and opportunities to participate in national education initiatives can strengthen intrinsic motivation and reinforce teachers’ sense of professional value. Importantly, incentive systems should avoid excessive competition and instead promote collaboration and collective improvement within schools. 9.4 Career Development and Professional Pathways Expanding career development opportunities is critical for long-term teacher retention. Azerbaijan’s education system would benefit from the introduction of multi-track career pathways that allow teachers to advance professionally without leaving classroom practice. These pathways could include roles such as mentor teacher, instructional coach, curriculum specialist, or professional development trainer. Clear and transparent criteria for progression would enhance fairness and predictability, addressing one of the major sources of dissatisfaction among mid-career teachers. International «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 17 evidence suggests that diversified career structures increase retention by aligning individual aspirations with organizational needs (Johnson et al., 2012). Moreover, linking career advancement to continuous professional development would strengthen the connection between performance management and capacity building. 9.5 Retention-Focused and Location-Sensitive Policies Given persistent urban–rural disparities, retention strategies must be geographically sensitive. Financial incentives such as rural hardship allowances, housing support, and transport subsidies have proven effective in comparable contexts (ILO, 2021; UNESCO, 2023). However, such measures should be complemented by professional incentives, including priority access to training, accelerated career progression, and enhanced mentoring support. Early-career retention deserves particular attention. Structured induction programs, reduced teaching loads during initial years, and formal mentoring arrangements can significantly improve retention outcomes. These measures signal institutional commitment to teacher development and reduce the likelihood of early exit due to professional overload or unmet expectations. 10. Conclusion Teacher motivation, workload, and retention in Azerbaijan’s general education system constitute interrelated and systemic human resource management challenges with far-reaching implications for education quality, workforce sustainability, and the long-term success of education reforms. While recent policy initiatives have modernized curricular frameworks, assessment mechanisms, and digital infrastructure, this study demonstrates that insufficient attention to HRM dimensions has limited the effectiveness of these reforms at the level of implementation. By applying an HRM lens and drawing on internationally recognized secondary data, the article shows that excessive administrative workload, compliance-oriented performance management, constrained professional autonomy, and limited career progression pathways collectively undermine teacher motivation and increase retention risks. These challenges are particularly acute among early-career teachers and those working in rural or disadvantaged contexts, where institutional support mechanisms remain weaker and professional isolation is more pronounced. The findings underscore that teacher-related challenges in Azerbaijan should not be interpreted as individual performance deficiencies or short-term adjustment issues. Rather, they reflect structural features of education governance and human resource systems that prioritize control and standardization over professional development and workforce sustainability. Without addressing these underlying HRM constraints, further reforms risk intensifying workload pressures and accelerating attrition, thereby weakening the very human capital on which education quality depends. The study argues for a strategic shift from administrative personnel management toward strategic human resource management, in which teachers are recognized as central assets rather than passive implementers of policy. Such a shift requires integrating workforce planning, workload rationalization, motivation-oriented incentive structures, diversified career pathways, and retention-focused support mechanisms into the core of education governance. International experience suggests that education systems adopting this approach are better positioned to sustain reform outcomes, improve instructional quality, and retain skilled teaching personnel. From a policy perspective, strengthening HRM practices offers a cost-effective and sustainable pathway to improving education outcomes. Investments in teacher well-being, professional development, and retention reduce turnover costs, enhance institutional memory, and foster a more stable and motivated workforce. For Azerbaijan, embedding HRM considerations into education reform agendas is therefore not supplementary but essential to achieving long-term system resilience and equity. Proceedings of the 12th International Scientific Conference 18 Finally, this study contributes to the broader international literature by illustrating how HRM frameworks can be applied to analyze teacher motivation, workload, and retention in centralized education systems. Future research could build on this analysis by incorporating primary survey data, longitudinal workforce statistics, or comparative studies across similar governance contexts. Nonetheless, the evidence presented here clearly indicates that addressing teacher-related challenges through strategic HRM reform is a prerequisite for sustainable education development in Azerbaijan. References Boxall, P., & Purcell, J. (2016). Strategy and human resource management (4th ed.). Palgrave. Deci, E. L., & Ryan, R. M. (2000). Intrinsic and extrinsic motivation. Contemporary Educational Psychology, 25(1), 54–67. Ingersoll, R. (2001). Teacher turnover and shortages. American Educational Research Journal, 38(3), 499–534. International Labour Organization. (2021). Working conditions of teachers in public education. Johnson, S. M., et al. (2012). Teaching careers and retention. Harvard University Press. OECD. (2019). TALIS 2018 results. OECD. (2020). Education policy outlook. OECD. (2021). Teachers at the centre of education recovery. UNESCO. (2023). Global education monitoring report. World Bank. (2020). Teacher policy and management in Europe and Central Asia. World Bank. (2022). Improving human capital through education workforce reform. «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 19 ORGANIZATIONAL AND EDUCATIONAL CONDITIONS FOR THE FORMATION OF EFFECTIVE MANAGEMENT IN PROFESSIONAL BOXING Kazankapov Aman Abai Kazakh National Pedagogical University, Almaty, Kazakhstan Telakhynov Yerkin Abai Kazakh National Pedagogical University, Almaty, Kazakhstan Umirzakov Yedige Abai Kazakh National Pedagogical University, Almaty, Kazakhstan Abstract. The article analyzes the organizational and pedagogical conditions for the development of management in professional boxing. Professional boxing is considered as a specific segment of the modern sports industry characterized by commercialization, high media involvement, and complex contractual relations. The structure of management in professional boxing, the role of the manager, and the content of managerial activity are examined. Special attention is paid to the functions of a professional boxing manager, including goal setting, planning, organization of bouts, motivation, control, and information support. The study substantiates key organizational and pedagogical conditions that ensure effective management development, as well as the importance of specialized education and practical training of sports managers. The findings may be applied in sports management education and professional boxing practice. Keywords: professional boxing, sports management, organizational and pedagogical conditions, managerial activity. Introduction. Professional boxing occupies a significant place in the contemporary sports industry, functioning not only as a competitive sport but also as an integral component of the global entertainment and commercial market. Over the past decades, professional boxing has undergone substantial transformation, becoming closely intertwined with mass media, sponsorship networks, broadcasting corporations, and promotional organizations. This commercialization has significantly expanded the economic scale of professional boxing, while simultaneously increasing the complexity of its organizational and managerial processes (Smith & Stewart, 2015). In contrast to amateur sport, where institutional regulation and state-supported structures prevail, professional boxing is predominantly governed by market mechanisms and contractual labor relations. Boxers, promoters, managers, trainers, and sponsors interact within a competitive environment based on contractual obligations, financial interests, and performance outcomes. As noted by Houlihan and Green (2011), such conditions require a fundamentally different management approach, emphasizing strategic planning, legal literacy, communication skills, and long-term athlete development. The effectiveness of professional boxing largely depends on the quality of management, which encompasses organizational coordination, financial planning, athlete career management, marketing, and media relations. At the same time, modern sport management theory increasingly Proceedings of the 12th International Scientific Conference 20 recognizes the importance of pedagogical competence in the professional preparation and support of athletes. Managers in professional boxing are not merely administrators but also key actors influencing athletes’ motivation, ethical behavior, psychological stability, and professional longevity (Chelladurai, 2014). Pedagogical aspects of management become especially relevant in professional boxing due to the high physical and psychological demands placed on athletes, the risks associated with competitive performance, and the necessity of continuous professional development. Effective managers must be capable of creating educational and developmental environments that foster athletes’ tactical thinking, self-regulation, and responsible decision-making both inside and outside the ring (Lyle, 2018). This highlights the need to integrate pedagogical principles into the organizational structure of professional boxing management. Despite the growing body of research on sport management, the organizational and pedagogical conditions for the formation of effective management in professional boxing remain insufficiently explored. Existing studies often focus on general sport management models without considering the specific characteristics of professional boxing, such as individualized career trajectories, promoter-manager dynamics, and the dual commercial-pedagogical nature of managerial activity (Slack & Parent, 2006). Therefore, the problem of developing management in professional boxing through scientifically grounded organizational and pedagogical conditions is particularly relevant. Addressing this issue contributes not only to improving management efficiency but also to ensuring sustainable athlete development, ethical standards, and the long-term stability of professional boxing as a socio-cultural and economic phenomenon. Materials and Methods. The purpose of this study is to substantiate the organizational and pedagogical conditions that ensure the effective formation and development of management in professional boxing under contemporary socio-economic and sports-industry conditions. The research aims to identify key organizational mechanisms and pedagogical principles that contribute to improving managerial efficiency, optimizing athlete development, and enhancing the sustainability of professional boxing organizations. In achieving this purpose, the study seeks to address several interrelated objectives: (1) to analyze the theoretical foundations of sports management and pedagogical support in professional sport; (2) to examine the specific characteristics of managerial activity in professional boxing, including its contractual, commercial, and educational dimensions; (3) to systematize organizational and pedagogical conditions that influence the quality of management; and (4) to develop a conceptual framework that integrates management theory with pedagogical approaches relevant to professional boxing. To achieve the stated purpose, a комплекс of general scientific and special research methods was employed. The methodological basis of the study is grounded in systems theory, competence-based approach, and pedagogical modeling, which allow management in professional boxing to be considered as a multidimensional phenomenon combining organizational, economic, and educational components. Theoretical analysis, synthesis, and generalization of scientific literature were used to examine classical and contemporary studies in sports management, professional sport, and pedagogical theory. This method enabled the identification of conceptual approaches to management effectiveness, leadership, and educational support in elite and professional sports. Comparative analysis was applied to distinguish between management models in amateur and professional sport and to highlight the specific managerial requirements of professional boxing. Document analysis was employed to examine regulatory and normative materials governing professional boxing, including international federation regulations, contractual frameworks, ethical codes, and professional standards related to managerial and promotional activities. This «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 21 method allowed for the identification of structural and organizational constraints affecting management practices in professional boxing. Additionally, a structural-functional analysis was used to examine the roles and responsibilities of key stakeholders in professional boxing management, such as managers, promoters, trainers, and athletes. This approach facilitated the identification of pedagogical interactions and communication mechanisms that influence athlete development and career management. The method of conceptual modeling was applied to develop a generalized model of organizational and pedagogical conditions for effective management in professional boxing. This model integrates organizational structures, pedagogical support mechanisms, and managerial competencies, providing a holistic framework for understanding management development in this sport. The use of these methods ensured the validity and reliability of the study’s conclusions and allowed for a comprehensive examination of management development in professional boxing from an organizational and pedagogical perspective. Results. The results of the study are based on a comprehensive analysis of scientific literature, regulatory documents, and conceptual approaches to sports management. The findings allowed for the identification and systematization of key organizational and pedagogical conditions that ensure the formation of effective management in professional boxing. The obtained results confirm that management effectiveness in professional boxing is determined by the interaction of organizational structures, managerial competencies, and pedagogical support mechanisms. Organizational Conditions for Effective Management in Professional Boxing The analysis revealed that professional boxing management operates within a complex and multi-actor organizational environment. Effective management requires clear structural coordination between managers, promoters, coaches, medical staff, and media representatives. One of the main results of the study is the identification of core organizational conditions that directly influence management quality. Table 1 presents the main organizational conditions and their functional significance in professional boxing management. Table 1. Key Organizational Conditions for Effective Management in Professional Boxing Organizational condition Functional significance Clearly defined management structure Ensures role distribution, accountability, and coordination Strategic career planning Supports long-term athlete development and performance stability Legal and regulatory competence Prevents contractual conflicts and protects athletes’ rights Financial and resource management Optimizes sponsorship, budgeting, and economic sustainability Communication and coordination mechanisms Enhances interaction among stakeholders The results indicate that the absence of a clearly structured management system leads to fragmented decision-making and reduced organizational efficiency. Strategic planning was identified as a critical condition, allowing managers to balance sporting objectives with commercial interests while reducing physical and psychological risks for athletes. Proceedings of the 12th International Scientific Conference 22 Pedagogical Conditions in Professional Boxing Management In addition to organizational factors, the study revealed that pedagogical conditions play a decisive role in management effectiveness. Professional boxing management involves continuous interaction with athletes, which requires pedagogical competence, ethical awareness, and communication skills. Table 2. Pedagogical Conditions for the Formation of Effective Management in Professional Boxing Pedagogical condition Educational and managerial impact Developmental educational environment Promotes motivation, discipline, and self-regulation Competence-based approach to management Integrates organizational, pedagogical, and psychological skills Pedagogical communication Strengthens trust and cooperation between managers and athletes Reflective managerial practice Enhances adaptability and decision-making quality Ethical and value-oriented education Supports fair play and responsible behavior The results show that managers who demonstrate pedagogical awareness are more effective in supporting athletes’ professional growth and psychological stability. Pedagogical communication and reflective practices were found to be especially important in managing highperformance athletes under competitive pressure. Integration of Organizational and Pedagogical Conditions A key result of the study is the substantiation of the integrated nature of organizational and pedagogical conditions in professional boxing management. The findings indicate that organizational mechanisms alone are insufficient without pedagogical support, and vice versa. Table 3 illustrates the integrative relationship between organizational and pedagogical conditions. Table 3. Integration of Organizational and Pedagogical Conditions in Professional Boxing Management Organizational component Pedagogical component Resulting effect Management structure Educational interaction Coordinated and athlete-centered management Strategic planning Career guidance and mentoring Sustainable athlete development Legal regulation Ethical education Protection of rights and professional integrity Resource management Motivation and support Increased performance efficiency The integration of these components contributes to the formation of a holistic management model that emphasizes both efficiency and human-centered development. The results confirm that the highest level of management effectiveness is achieved when organizational regulation is complemented by pedagogical interaction. «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 23 Overall, the findings demonstrate that effective management in professional boxing is a multidimensional phenomenon that requires the simultaneous implementation of organizational and pedagogical conditions. The results substantiate the necessity of a systematic approach to management development, aimed at improving managerial quality, supporting athlete well-being, and ensuring the long-term sustainability of professional boxing organizations. Discussion. The findings of this study confirm that the development of effective management in professional boxing is a complex process determined by the interaction of organizational and pedagogical conditions. The results align with contemporary sport management theories, which emphasize that management effectiveness in professional sport cannot be reduced solely to administrative or economic functions but must incorporate educational, psychological, and ethical dimensions. The identified organizational conditions correspond with the structural-functional models of sport organizations described by Slack and Parent (2006), who argue that clearly defined management structures and role differentiation are fundamental to organizational efficiency. In professional boxing, however, this requirement is complicated by the decentralized nature of management and the presence of multiple independent stakeholders. The present study expands existing theoretical models by demonstrating that coordination mechanisms in professional boxing must be more flexible and adaptive, reflecting the individualized career paths of professional athletes. The importance of strategic planning identified in the Results section supports the conclusions of Chelladurai (2014), who emphasizes long-term planning as a key determinant of sustainable performance in elite sport. In professional boxing, strategic planning extends beyond competition scheduling to include career longevity, health preservation, and post-career transition. This finding highlights a distinctive feature of professional boxing management, where sporting and commercial objectives are inseparable and must be balanced through informed managerial decisions. Legal and regulatory competence emerged as a critical organizational condition in this study, reinforcing previous research on professional sport management that underscores the role of legal literacy in contract-based sports systems (Houlihan & Green, 2011). Unlike team sports with centralized governance, professional boxing relies heavily on individual contracts, making managers’ legal awareness essential for protecting athletes’ rights and ensuring ethical conduct. The discussion of this aspect contributes to the literature by emphasizing the pedagogical role of managers in educating athletes about contractual responsibilities and professional ethics. From a pedagogical perspective, the study’s findings are consistent with Lyle’s (2018) assertion that coaching and management in high-performance sport involve continuous educational interaction. The identified pedagogical conditions—such as the creation of a developmental educational environment, competence-based management, and reflective practice—underscore the human-centered nature of effective management in professional boxing. These results extend existing pedagogical theories by applying them to the specific context of professional boxing, where athletes are exposed to high levels of physical, psychological, and social pressure. The integration of organizational and pedagogical conditions represents a key theoretical contribution of this study. While previous research often treats management and pedagogy as separate domains, the present findings demonstrate their interdependence in professional boxing management. This integrative perspective supports the systemic approach advocated in modern sport pedagogy, which views athlete development as a holistic process involving organizational support, educational guidance, and ethical regulation. Furthermore, the emphasis on reflective managerial practice aligns with contemporary professional development models, which highlight lifelong learning and self-assessment as Proceedings of the 12th International Scientific Conference 24 essential components of managerial competence. In professional boxing, reflective management enables adaptation to dynamic competitive environments and individual athlete needs, thereby enhancing both performance outcomes and athlete well-being. The discussion also reveals that insufficient pedagogical competence among managers may undermine organizational effectiveness, even when structural and financial resources are adequate. This finding suggests that management education programs in professional sport should incorporate pedagogical training alongside organizational and legal instruction. Such an approach would contribute to the formation of a new generation of sport managers capable of addressing both performance and developmental objectives. Overall, the results of this study support the view that effective management in professional boxing requires a balanced integration of organizational efficiency and pedagogical responsibility. This conclusion not only enriches theoretical discourse in sport management and pedagogy but also provides practical implications for the design of management training programs and organizational policies in professional boxing. Conclusion. The present study substantiates the significance of organizational and pedagogical conditions as key determinants of effective management development in professional boxing. The findings confirm that professional boxing, as a highly commercialized and contract-based sport, requires a management model that goes beyond traditional administrative functions and incorporates pedagogical, ethical, and developmental components. The results demonstrate that organizational conditions such as clearly defined management structures, strategic career planning, legal and regulatory competence, and effective communication mechanisms form the structural foundation of professional boxing management. These conditions ensure coordination among stakeholders, protect athletes’ rights, and support the economic sustainability of professional boxing organizations. However, the study reveals that organizational mechanisms alone are insufficient to guarantee management effectiveness without corresponding pedagogical support. Pedagogical conditions, including the creation of a developmental educational environment, competence-based managerial training, pedagogical communication, and reflective practice, play a decisive role in enhancing management quality. The integration of pedagogical principles into managerial activity enables managers to support athletes’ motivation, psychological stability, ethical behavior, and long-term professional development. This human-centered approach contributes to improved performance outcomes and career longevity in professional boxing. One of the key conclusions of the study is that the highest level of management effectiveness is achieved through the integration of organizational and pedagogical conditions. The proposed integrative framework highlights the interdependence of structural regulation and educational interaction, emphasizing the necessity of a systematic approach to management development in professional boxing. This approach aligns with contemporary theories of sport management and pedagogy, which view athlete development as a holistic and multi-dimensional process. The theoretical significance of this study lies in expanding the understanding of professional boxing management as a socio-pedagogical phenomenon that combines organizational efficiency with educational responsibility. Practically, the findings provide a basis for improving management training programs, developing professional standards for boxing managers, and designing organizational policies that prioritize athlete well-being and ethical conduct. In conclusion, the study confirms that the development of effective management in professional boxing requires scientifically grounded organizational and pedagogical conditions. The implementation of these conditions contributes to the sustainable development of professional boxing, the protection of athletes’ professional interests, and the long-term stability of the sport within the global sports industry. Future research may focus on empirical validation «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 25 of the proposed framework and the development of applied models for management education in professional boxing. References 1. Chelladurai P. Managing organizations for sport and physical activity. – London: Routledge, 2014. – 360 p. 2. Houlihan B., Green M. Routledge handbook of sports development. – London: Routledge, 2011. – 624 p. 3. Lyle J. Sports coaching concepts: A framework for coaching practice. – London: Routledge, 2018. – 312 p. 4. Slack T., Parent M.M. Understanding sport organizations: The application of organization theory. – Champaign: Human Kinetics, 2006. – 296 p. 5. Smith A., Stewart B. Introduction to sport marketing. – London: Routledge, 2015. – 356 p. 6. Chelladurai P. Managing organizations for sport and physical activity: A systems perspective. – London: Routledge, 2014. – 380 p. 7. Houlihan B., Green M. Routledge handbook of sports development. – London: Routledge, 2011. – 624 p. 8. Lyle J. Sports coaching concepts: A framework for coaching practice. – London: Routledge, 2018. – 312 p. 9. Slack T., Parent M.M. Understanding sport organizations: The application of organization theory. – 2nd ed. – Champaign: Human Kinetics, 2006. – 310 p. 10. Smith A., Stewart B. Introduction to sport marketing. – London: Routledge, 2015. – 356 p. 11. Taylor T., Doherty A., McGraw P. Managing people in sport organizations: A strategic human resource management perspective. – Oxford: Butterworth-Heinemann, 2008. – 295 p. 12. Shilbury D., Westerbeek H., Quick S., Funk D. Strategic sport marketing. – 4th ed. – Crows Nest: Allen & Unwin, 2014. – 410 p. 13. Green M., Houlihan B. Elite sport development: Policy learning and political priorities. – London: Routledge, 2005. – 246 p. 14. Hoye R., Smith A., Nicholson M., Stewart B. Sport management: Principles and applications. – 5th ed. – London: Routledge, 2018. – 432 p. 15. International Boxing Association (IBA). Technical and competition rules. – Lausanne: IBA, 2023. – 198 p. 16. UNESCO. Quality physical education guidelines for policy-makers. – Paris: UNESCO, 2015. – 120 p. Proceedings of the 12th International Scientific Conference 32 − Pair work with chatbot support. − Reflection through digital forms. The following platforms were used: ChatGPT, MathGPT, Photomath, Kampus, Desmos, Symbolab. Criteria for assessing effectiveness in EG Criteria Indicators of effectiveness in EG Cognitive interest Growth in the proportion of students with high interest (≥25%) Learning motivation Increase in internal motivation, decrease in demotivation Behavioral engagement Growth in initiative, focus time, activity in dialogue Subject results Improvement in success, reduction of typical errors (≥30%) Reflective component Growth in conscious use of AI as an educational tool Results and Discussion Based on the results of the experiment in the experimental group (EG), the following changes were observed: − The average increase in interest in mathematics was Δ≈+0.7. − The proportion of students voluntarily participating in tasks reached 60%. − The number of queries to AI for explanations, rather than just receiving ready-made answers, increased. − Subject results improved, particularly in algebraic transformations. − The effect was partially retained 4–6 weeks after the experiment. Conclusion The conducted research confirmed that artificial intelligence technologies have high potential in fostering interest in mathematics among students with low learning motivation. The implementation of AI tools—such as chatbots, adaptive platforms, and visualizers—enabled a shift from traditional reproductive learning to a more flexible, interactive, and personalized model, which contributed to an increase in cognitive activity, engagement, and academic performance. The developed methodology, based on the use of AI for creating motivational tasks, instant feedback, visualization, and reflection, demonstrated its effectiveness in school practice. Experimental data indicate significant improvements in academic performance, an increase in interest in the subject, and a conscious use of digital tools in learning. Thus, AI can serve not only as a technological novelty but also as a full-fledged pedagogical resource capable of transforming the approach to teaching mathematics. It is important to emphasize that successful AI integration requires methodological planning, pedagogical support, and ethical responsibility. Perspectives for further research include: − Expanding the age and motivation groups of students. − Developing specialized AI modules for school mathematics. − Studying the long-term impact of AI on learning motivation and cognitive development. − Creating methodological recommendations for teachers on integrating AI into the educational process. The integration of AI into school mathematics education opens new horizons for improving the quality of learning, especially for students facing difficulties in mastering the subject. This makes further development of the scientific-methodological base and practical solutions in this area particularly relevant. «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 33 References 1. Zhou, Y., & Li, H. (2024). AI-enhanced visualization in mathematics education. Journal of Digital Pedagogy. 2. Kasymova, G.K., & Xu, W. (2024). Adaptive learning technologies in secondary education. International Review of Education. 3. Shlykova, S.S. (2025). Dialogic interaction with AI in the classroom. Pedagogical Innovations. 4. Kampylis, P., et al. (2023). Personalization through AI: A framework for schools. European Journal of Educational Technology Proceedings of the 12th International Scientific Conference 34 EXPLORING THE IMPACT OF DIGITAL MEDIA ON READING SKILLS A.S.Ospan Master degree student of Kazakh National Women’s Teacher Training University (Almaty, Kazakhstan) Аңдатпа Бұл мақалада ағылшын тілін оқытуда сандық технологияның оқу дағдыларына әсері қарастырылады. Қазіргі таңда осы тақырыптың ғылыми жаңалығы қазіргі қоғамда ағылшын тілін үйрену қажеттілігін анықтауында. Бұл зерттеуде ағылшын тілін оқытуда цифрлық технологияны қолданудың ең тиімді әдістері мен тәсілдері талданды, соның ішінде теледидар, радио, интернет ресурстары, ауызша коммуникация, тыңдау, оқу және жазу сияқты сөйлеу әрекеттерінің әртүрлі түрлерінде дағдыларды дамытуға арналған арнайы білім беру бағдарламалары, сондай-ақ ағылшын тілінің әртүрлі оқу мәнерлеріне егжейтегжейлі талдау жасалды. Және де мақалада бастауыш сыныптағы ағылшын тілі сабақтарында медиа-технологияларды қолдану арқылы эксперименттік енгізуді қарастыратын практикалық бөлім қарастырылады. Кілт сөздер: ағылшын тілін оқыту, түпнұсқалық материалдар, цифрлық технологиялар, интернет ресурстары, оқу стильдері, медиа технологиялар. Аннотация В данной статье рассматривается влияние использования цифровых технологий на навыки чтения в процессе обучения английскому языку. Научная новизна заключается в раскрытии необходимости изучения английского языка в современном обществе. В ходе изучения данной темы были проанализированы наиболее эффективные методы и подходы использования средств цифровых технологий в обучении английскому языку, такие как телевидение, радио, интернет-ресурсы, специализированные образовательные программы, предназначенные для развития навыков различных видов речевой деятельности таких как устного общения, аудирования, чтения, письма, а также навыков подробный анализ различных стилей чтения английского языка.Далее в статье рассматривается практическая часть, в которой проводится экспериментальное внедрение медиа технологий на уроках английского языка в младшей школе. Ключевые слова: обучение английскому языку, аутентичные материалы, цифровые технологии, интернет-ресурсы, стили чтения, медиа технологии. Annotation This article examines the impact of digital technology on reading skills in English language learning. Its novelty lies in its identification of the need for English language learning in modern society.This study analyzed the most effective methods and approaches for using digital technology in English language teaching, including television, radio, internet resources, specialized educational programs designed to develop skills in various types of language activities such as oral communication, listening, reading, and writing, as well as a detailed analysis of various English reading styles. The article then discusses the practical part, which involves the experimental implementation of media technologies in English language lessons in primary school. «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 35 Keywords: English language teaching, authentic materials, digital technology, internet resources, reading styles, media technologies. Text In the modern world, learning English is essential because it is the international mode of communication for all of society. International languages allow for the exchange of information, regardless of nationality or place of residence. English is designated as the official working language of the United Nations (UN), and therefore, most international meetings are conducted in this language. The development of innovative digital learning technologies has led to increased interest among researchers in adaptive approaches to the educational process in schools. This includes not only the emergence of training courses, but also intelligent systems and personalization of learning, as well as issues of increasing the effectiveness of the educational process [1, p. 115], [2, p. 710]. English also allows us to consume a vast amount of information: read the most outstanding works in the original, listen to world-famous musical compositions, and understand their meaning. It's difficult to imagine the modern world without English. It's firmly ingrained in our daily lives and completely changes our perspective on communication. Digital technologies create a unique opportunity for foreign language learners to use authentic tools to put their acquired knowledge into practice through interaction with native speakers. The issue of developing students' reading skills in senior secondary schools is becoming increasingly important, as reading, as a learning goal, serves as a means of understanding the subject matter. Reading, as a productive process, takes a significant amount of time and effort from students, as it also requires the integration of linguistic, speech, and cognitive competencies. As a form of communicative activity, it should be an integral part of every lesson. To facilitate students' reading, it is necessary to consider the specific characteristics of this type of cognitive activity, such as motivation, determination, students' mental abilities, independence, and perseverance. The practical significance of reading lies in the fact that through it, students become familiar with the culture of the people speaking the target language, develop information retrieval skills, and instill respect for that foreign culture. Furthermore, reading success fully addresses the primary goal of foreign language learners: developing communicative competence. Let's consider the problem of improving media literacy in English lessons using digital systems (tools) and their application to analyzing fake news. Modern society, the media, and media networks are inseparable. Media literacy is the ability to critically consume and comprehend information, filter it appropriately, and prevent its uncontrolled dissemination and disinformation. It is important to be able to distinguish and filter web resources, fraudulent schemes, and malicious content. Media literacy is the ability to identify and classify media resources and media messages, to analyze and to evaluate them, preventing their negative impact on users. This addresses a variety of tasks, such as: 1. Genre assessment; 2. Familiarization with structure and capabilities; 3. Creating a scenario or situation; 4. Argumentation and justification of what was observed; 5. Risk and threat assessment. Threats – real or virtual, with sources in the real or virtual world. Proceedings of the 12th International Scientific Conference 36 Example. Examples of threats to students may include: 1. Fakes, fake pages (threats, for example,a publicly accessible photo posted on a social network or account); 2. Disinformation (deliberate misleading); 3. Cyberbullying (usually prolonged, intentional slander, threats, and compromise via SMS, email, or instant messaging); 4. Group pressure, trolling, and bullying (often involving insults, belittling the student in the eyes of others, including promoting suicide, romanticizing crime, etc.); 5. Extremism (involving a victim – a group member – in an extremist organization). Students at school need to improve their media literacy, in particular, byimplementing: 1. Personalsafetymanagement (self-awarenessonline, protectionfrommediatricks, abilitytonavigatemedianetworks); 2. Informationretrieval (relevantselectionofinformation); 3. Informationperception (recognizingcontext, hiddenmeaning, forming a wellreasonedpersonalopinion); 4. Contentcreation (abilitytocreatevaluablecontent); 5. Mediaethics (correctonlinecommunication). Media education is based on the concept of encouraging people to engage in purposeful dialogue. The benefits of media literacy for students are obvious, for example, critical thinking, recognizing hidden thoughts and goals ("the devil is in the details"), cultivating resilience to tricks, etc. Media literacy also means self-education in the media sphere, reaching school children and their parents. Media literacy reflects literacy in obtaining, analyzing, evaluating, and transmitting information through various media channels and resources – from traditional to mainstream media. Media literacy enables one to analyze media, identify fakes and propaganda, censor news and public programs, and identify key structural elements, such as the true source of a fake, its purpose, etc. The goal of media literacy is not only to develop media competencies but also heuristic procedures, identifying their advantages and limitations, stakeholders, and formulating effective opinions and decisions. Example: If, for example, a user is asked to vote for "their" opinion or product, this is not mandatory; a media-literate user will critically evaluate such a proposal. Media literacy offers additional and powerful opportunities for developing critical, comparative, and systemic thinking, creating and evolving one's own media resources and messages, assessing one's own and competitors' behavior, and using heuristics. Media learning is a form of information education, integrated with biblioliteracy, reading-writing-listening culture, and the use of relevant IT infrastructures (ecosystems). English lessons have their own specific characteristics. Along with the study of language and the country's history, basic speech and grammar skills are developed. The practical acquisition of this knowledge depends on the teacher's understanding, the visual and media support of the lesson, an adaptive learning and assessment mechanism, and infographics [6, p. 69], which have didactic advantages (compared to text). The teacher must maintain students' attention, and infographics are an effective tool for this purpose. Creating infographics on a given topic can be an engaging activity for students. «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 37 Methodology Homepage of CNN chanel is presented on screen 1. Screen 1 Methodology: Using the CNN Homepage to Develop Reading Skills and Discuss the Impact of Digital Media 1. Lesson Objective  Develop reading skills in English (skimming, scanning, detailed reading).  Develop media literacy: a critical approach to online news.  Discuss the impact of digital media on reading habits and skills. 2. Materialsneeded: CNN Homepage (current version at the time of the lesson). Projector/screen (or students' smartphones). Worksheets with tasks (comprehension questions, comparison exercises). 3. Lesson Procedure Stage 1. Lead-in – 5–7 min. The teacher asks questions:Where do you usually read the news: online or in print? Do you read the entire text or just the headlines? Brief discussion: How has digital media influenced reading habits? Stage 2. Skimming – 10 min. Students open the CNN homepage. Assignment: In 2 minutes, determine:What are the three most common topics on the page (e.g., politics, technology, sports)? Which headlines seem most attention-grabbing? Discussion: Why are these headlines so catchy? Stage 3. Scanning&Detailed Reading – 15 min. Divide students into groups, each group selecting one article. Assignments: Find key facts (who, what, when, where, why). Define new words, create a mini-dictionary. Compare the use of short paragraphs and multimedia (photos, videos, hyperlinks). Stage 4. Media Format Analysis – 10 min. Proceedings of the 12th International Scientific Conference 38 Discussion Questions: How does reading an article on CNN differ from reading a printed article? Is it easier or harder to concentrate? How do images, videos, and hyperlinks affect text comprehension? What develops reading skills better: long printed texts or digital media? Stage 5. Speaking Practice (Speaking / Discussion) – 10 min. Students discuss in pairs/groups: "Digital media makes our reading skills weaker/stronger. Do you agree?". Provide a short mini-essay/oral conclusion (2-3 sentences). Stage 6. Reflection and Consolidation – 5 min. Each student formulates: One new English word learned today. One conclusion about how digital media affects their reading. 4. Homework (options) Find another online newspaper (e.g., BBC, The Guardian) and compare the presentation with CNN. Write a mini-essay (100-150 words): "How digital media have changed my reading habits." Thus, the lesson is built around real, authentic material (CNN), trains reading skills in English, and helps students understand the role of digital media in developing reading skills. References: 1. V. P. Glukhov. Developing Reading Fluency in Primary School Students / V. P. Glukhov, A. V. Petrova. - Moscow: Prosveshchenie, 2019. 2. A. A. Safonov. Digital Pedagogy. Practical Course: Textbook and Workshop for Secondary Vocational Education / A. A. Safonov, M. A. Safonova. - Moscow: Yurait, 2024. 3. N. N. Svetlovskaya. Methods of Teaching Creative Reading: A Textbook for Universities / N. N. Svetlovskaya, T. S. Piche-ool. - 2nd ed., corrected and enlarged. - Moscow: Yurait, 2024. 4. E. V. Chizhikova. Developing Reading Accuracy and Fluency in Primary School Students Using Digital Technologies / E. V. Chizhikova [Electronic resource]. – Access mode: https://clck.ru/3NbLeZ «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 39 ЦИФРОВЫЕ ИНСТРУМЕНТЫ КАК СРЕДСТВО ПРОВЕРКИ ТЕОРЕТИЧЕСКИХ ГИПОТЕЗ В ОБУЧЕНИИ МАТЕМАТИКЕ Алдибаева Турагалды Абилакимовна к.п.н., ассоциированный профессор Алматинского гуманитарно-экономического университета, Республика Казахстан Аннотация: В статье представлен подробный анализ использования цифровых образовательных сред как эффективного инструментария для практической проверки и критической оценки математических предположений. Автор рассматривает последовательный путь познания: от формирования первичной интуитивной догадки до выстраивания строгого аналитического доказательства, которое опирается на результаты предварительных цифровых экспериментов и интерактивных вычислений. Особое внимание в работе уделено методике формирования исследовательских компетенций у студентов. Этот процесс реализуется через создание и последующее разрешение когнитивных конфликтов — ситуаций, в которых обнаруживается явное несоответствие между абстрактной теоретической моделью и её реальным отображением на экране монитора. Именно в моменты такого сопоставления теории с практическими данными происходит глубокое осмысление предмета и развитие навыков научного поиска. 1. Введение: Гносеологический статус цифрового эксперимента. Классическая образовательная модель, на протяжении десятилетий определявшая вектор преподавания математики в высшей школе, базировалась на строгом аксиоматикодедуктивном методе, где приоритетом являлось транслирование готовых доказательств. Однако в условиях глобальной цифровой трансформации образовательного пространства этот консервативный подход дополняется и обогащается методами экспериментальной математики. Данное направление переосмысливает роль студента: из пассивного слушателя он превращается в активного исследователя, который использует возможности современных цифровых сред для первичного обнаружения закономерностей. Таким образом, цифровая среда становится платформой для живого математического эксперимента, предваряющего глубокое теоретическое осмысление. Согласно концепции Дж.Борвейна, цифровой инструмент позволяет исследователю обнаруживать скрытые закономерности и проверять их устойчивость на массивах данных до момента формального вывода. В образовательном процессе это создает условия для формирования исследовательских навыков: студент перестает быть пассивным потребителем готовых алгоритмов и становится активным субъектом познания. Использование информационных технологий позволяет трансформировать обучение в итерационный процесс выдвижения, проверки и уточнения гипотез, что максимально приближает учебную деятельность к реальному научному поиску. 2. Теоретико-методологические основания исследования. 2.1. Концепция фальсифицируемости и когнитивный конфликт Процесс проверки гипотез в цифровой среде опирается на эпистемологический подход И. Лакатоса, описывающий развитие математики как динамическую цепочку Proceedings of the 12th International Scientific Conference 40 «догадок и опровержений» [2]. Цифровой инструмент (будь то система компьютерной алгебры или среда динамической геометрии) обеспечивает мгновенную обратную связь. Если выдвинутая студентом гипотеза ошибочна, система позволяет визуализировать или рассчитать контрпример. В этот момент возникает когнитивный конфликт — состояние, описанное А. М. Матюшкиным как ключевой стимул к познавательной активности [5]. Противоречие между «ожидаемым» (теоретическим предположением) и «наблюдаемым» (результатом моделирования) побуждает студента к рефлексии и поиску логической ошибки, что является необходимым этапом глубокого усвоения материала. 2.2. Инструментальный генез и дидактические инварианты Согласно теории П. Рабарделя, интеграция цифровых средств в сознание обучающегося проходит стадию «инструментального генеза» [4]. Артефакт (программное обеспечение) становится психологическим инструментом только тогда, когда субъект осознает границы его применимости. При проверке гипотез критически важным становится понимание разницы между «идеальным» математическим объектом и его дискретной цифровой аппроксимацией. Таким образом, цифровая среда выполняет не только иллюстративную, но и методологическую функцию, обучая студентов верификации данных и оценке погрешностей вычислений. 3. Практическая реализация. 3.1. Дифференциальное и интегральное исчисление. 3.1.1. Постановка проблемы и формирование гипотезы. Центральной методологической задачей в рамках курса «Дифференциальное исчисление функции одной переменной» выступает детальное изучение локального поведения функции в бесконечно малой окрестности выбранной точки. На начальном этапе освоения материала у обучающихся зачастую формируется устойчивая, но упрощенная интуитивная гипотеза. Суть её заключается в убеждении, что любая непрерывная функция априори обладает свойством «визуальной гладкости». Студенты склонны полагать, что непрерывность линии на графике автоматически гарантирует возможность построения единственной касательной в любой произвольно взятой точке области определения. 3.1.2. Содержание и методика проверки в цифровой среде Для верификации данного предположения организуется исследовательская работа в интерактивной среде динамической геометрии. Студентам предлагается выйти за рамки привычных гладких кривых (таких как парабола или синусоида) и проанализировать функции с особыми точками, например, функцию модуля f(x) = |x| или более сложную осциллирующую функцию f(x) = x sin(1/x). Основным инструментом исследования становится функция бесконечного масштабирования («zoom»). Обучающийся выбирает «критическую» точку (в данном случае — начало координат) и начинает многократно увеличивать масштаб графического изображения. Цель этого действия — найти так называемый «линейный участок», то есть такое состояние графика, при котором кривая под воздействием увеличения визуально выпрямляется и становится неотличимой от прямой линии (касательной). 3.1.3. Анализ полученных результатов и их научная интерпретация В ходе выполнения этого цифрового эксперимента студенты фиксируют результаты, которые прямо указывают на несостоятельность их исходной интуитивной гипотезы. В случае функции модуля f(x) = |x| обнаруживается, что при любом, сколь угодно мощном увеличении, график сохраняет характерный «излом» (острый угол) в точке x = 0. Процесс масштабирования не приводит к визуальному выпрямлению линии, что наглядно доказывает отсутствие единственной касательной в данной точке. «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 41 При исследовании функции f(x) = x sin(1/x) ситуация усложняется: вместо выпрямления график демонстрирует незатухающие, бесконечно повторяющиеся осцилляции. Студент видит, что при приближении к нулю частота колебаний возрастает, и график «дребезжит», не позволяя зафиксировать определенное направление для касательной. 3.1.4. Итоговый вывод и преодоление формализма Таким образом, практическое сопоставление визуальных данных с теоретическим определением производной позволяет студенту сделать глубокий аналитический вывод: этот визуальный и расчетный опыт подготавливает почву для введения строгого определения производной через пределы, демонстрируя, что непрерывность функции является лишь необходимым, но не достаточным условием её дифференцируемости [7]. Этот опыт превращает сухую формулировку учебника в осознанное знание, основанное на личном исследовательском опыте и критическом анализе увиденных феноменов. 3.2. Исследование сходимости рядов и интегралов. Проверка гипотез о суммируемости бесконечных последовательностей — область, где цифровая эмпирика вступает в прямое противоречие с обыденным восприятием. Студенты склонны полагать, что если слагаемые ряда стремятся к нулю, то общая сумма обязательно будет конечной. Метод проверки: Использование систем компьютерной алгебры (например, Maple или WolframAlpha) для вычисления частичных сумм гармонического ряда ∑    $. Студент задает расчет для N = 10, 10, 10. Наблюдение: Обнаруживается крайне медленный, но логарифмически неуклонный рост суммы, что опровергает первоначальное ожидание стабилизации. Вывод: Полученные данные служат эмпирическим основанием для изучения интегрального признака Коши и деликатных условий сходимости [8]. 3.3. Приближенные вычисления и предельные переходы. Цифровые инструменты позволяют проверить гипотезу о точности численного дифференцирования. Студент предполагает, что уменьшение шага h в формуле ()()  всегда ведет к увеличению точности. Эксперимент: С помощью скрипта на языке Python или табличного процессора вычисляется производная функции e при h, стремящемся от 10 до 10. Данные: Студент обнаруживает, что после определенного порога (h≈ 10) происходит резкое увеличение ошибки аппроксимации, обусловленное спецификой машинной арифметики, т.е. ошибка начинает стремительно расти. Анализ: Данное несоответствие теории и практики позволяет обсудить вопросы машинной точности и специфику представления вещественных чисел в памяти ЭВМ, что критически важно для современного инженера и математика. 4. Методический алгоритм предотвращения эффекта «черного ящика» Для обеспечения высокого качества математического образования и предотвращения превращения программного обеспечения в «черный ящик» — ситуацию, при которой результат выдается системой без осознания студентом внутренних механизмов и логики процесса — необходимо строгое соблюдение исследовательского алгоритма. Данный путь трансформирует механическое использование цифровых инструментов в осознанную познавательную деятельность, где технология выступает катализатором мышления, а не его заменой. Ниже представлено расширенное научное описание этапов данного алгоритма: Proceedings of the 12th International Scientific Conference 48 The formation of the technique of reading aloud to group words into a semantic whole is facilitated by reading by syntagmas, being in a series of phrases denoting time, place to practice intonation, reading after the speaker, expressive reading and reading with intonation markings. The following exercises can be used to develop reading technique at the initial stage:  reading aloud proverbs, sayings, tongue twisters, poems, short dialogues learned by heart;  filling in the gaps with missing letters;  finding a word in each row that contains the specified sound;  combining words of the text into thematic groups;  finding synonyms or antonyms in the text and recording them in writing;  filling in gaps in a sentence or text with words that fit the meaning;  selecting equivalents in the native language for words in a foreign language;  reading the text at a set time;  repeating the text after the teacher sentence by sentence;  finding a sentence in the text that contains the answer to the teacher's question;  supplementing the specified sentences, etc.; In short, reading begins with reading longer plot texts. In addition to the formation of reading techniques at the initial stage, various reading technologies, compensatory skills, and independent work skills are already beginning to form. At this stage, it is already possible to teach ignoring the unknown, if it does not interfere with the completion of the assigned task, working with a dictionary, using footnotes and comments offered in the text, as well as interpreting and transforming the text. There are three stages of work on the text: 1. Pre-text – awakening and stimulating motivation to work with the text; actualization of students’ personal experience by drawing on knowledge from other educational areas of school subjects; forecasting the content of the text based on students’ knowledge, their life experience, headings and pictures, etc. Here it is necessary to follow one important rule: all preliminary work on the text should not concern its content, otherwise schoolchildren will not be interested in reading it, since they will not find anything new for themselves in this text. The following exercises of student centred activities which may help teachers introduce and identify the topic of reading and activate previous knowledge:  Identify the topic of the text with some pre-questions and/or with the help of visual aids.  Explore key vocabulary that will come up in the text.  Anticipate and predict possible information through brainstorming or true-false activities.  Check previous assumptions with group discussions. 2. Textual – reading of the text of its individual parts with the purpose of solving a specific communicative task formulated in the assignment to the text and set by the student before reading the text itself. The object of reading control should be its comprehension. At the same time, control of comprehension of the read text should be connected both with the communicative tasks set before the students and with the type of reading. The following exercises guide the reader through the text:  General comprehension questions,  True/false activities,  Give-the-right-order activities,  Fill-in-the-blank activities,  Multiple choice activities,  Problem solving activities,  Information transfer activities i.e. maps, diagrams, drawings, tables, etc. «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 49 3. Post-text – using the content of the text to develop students’ skills in expressing their thoughts in oral and written speech. Examples of possible post-reading activities are: summaries and other types of writing tasks, discussions, role-plays and other oral interaction activities… Educational materials are also of particular importance when teaching reading, since their nature determines whether reading will proceed as a student’s speech activity or as an exercise. 1. The educational value of texts, their moral potential – to what extent do texts contribute to the education of students in the broad sense of the word and the formation of moral and ethical standards. 2. The cognitive value of texts and the scientific nature of their content. Texts should include factual material about the country and people whose language is being studied, as well as information from a wide variety of areas of human knowledge, such as popular science texts. 3. The content of texts should correspond to the age and interests of students. The content of texts should be significant in the eyes of students of a particular age group, should correspond to the level of their intellectual development and meet their cognitive and emotional interests. 4. Correctness of the ratio of the new and the known. According to (Cook, 1989) in order to make sense of any text we need to have “pre-existent knowledge of the world”. Such knowledge is referred to as schema. We carry in our heads mental representation of typical situations that we come across. Such schematic knowledge is activated when we are stimulated by particular words, expressions or contexts. It is known from psychology that one of the conditions for attracting attention to an object is such a degree of its novelty, at which, along with new elements, there are also elements that are somewhat familiar to students. The presence of known information in reading texts significantly facilitates its perception and understanding by students. In methodological literature, texts are recommended that specify and expand on information already known to students. 5. Degree of accessibility of texts. An interesting text that contains insurmountable difficulties loses all its appeal in the eyes of students. At the initial stage and especially in the first year of studying a foreign language, it is advisable to teach reading using lexical and grammatical material previously learned orally. This allows you to remove difficulties associated with understanding what is being read and pay more attention to the technique and expressiveness of reading. Gradually, texts may also contain unfamiliar words, the meaning of which can be guessed or which are given in footnotes. Texts which are accessible in terms of language help to create and maintain motivation for reading. According to (Day, R and Bamford, J 1998) specially written materials for extensive reading what are called “language learner literature” are often referred to as “simplified readers”. Such materials can take form of original fiction and non-fiction books as well as simplifications of established works of literature. Such books succeed because the writers or adaptors work within specific lists of allowed words and grammar. That is why students at the appropriate level can read them with comfort and self-confidence. At their best, such books despite the language limitations, can speak to the reader through the creation of atmosphere and compelling plot lines. According to (Carter, 1998) the language may be simplified, but it must not be unnatural. As he and his colleagues suggest “concocted, made-up language can be perfectly viable but it should be modelled on naturalistic samples.” On the other hand, according to (Farrell, 1998) authentic material can be used by students at fairly low levels, however, if the tasks that go with it are well designed and help students understand it better, rather than showing them how little they know. 6. Systematic increase in the volume of text. According to (Wallace, 1992) texts with longer words and sentences will be more difficult to understand than those with shorter ones. But on the Proceedings of the 12th International Scientific Conference 50 other hand, according to (Paran, 1996) to be successful students have to recognize a high proportion of the vocabulary without consciously thinking about it. Educational texts can be of different lengths: from one word to several dozen pages in a home-reading book. Both are important and should be included in the educational process. Short texts convey specific, sometimes extremely important information. Therefore, students should be taught to read correctly and extract the necessary information from such texts. However, it is impossible to limit oneself to teaching reading only short texts due to the following circumstances. Firstly, motivation, as is known, is directly dependent on the awareness of the success of the activity performed. Students should feel their progress, which consists not only in their understanding of increasingly complex texts, but also in the desire to read large texts. Secondly, it is possible to develop a complex reading skill, including all the specific skills that ensure it, only on expanded texts. An important issue is the suitability of the text for a particular type of reading. The decisive factors in this case are, firstly, the ratio of primary and secondary information and, secondly, the location of new words and their meanings. Conclusion Reading is one of the four types of speech activity that involves understanding and evaluating information contained in a text. Reading in English is crucial for learning English, as it helps expand vocabulary, strengthen grammar skills, and improve spelling by introducing new words and constructions to context. This skill also contributes to the development of writing and critical thinking skills, enabling earners to analyse information and better understand the structure of language. When reading, learners encounter new words and phrases in a natural context, which helps them better understand and remember their meaning and correct usage. Reading texts with correct grammar helps to intuitively grasp grammar rules without having to memorize complex theories. Constantly interacting with texts helps to memorize the correct spelling of words and phrases, improving your spelling. Reading good texts serves as a model for constructing logical and grammatically correct sentences, which helps learners write their own texts. Reading trains comprehension skills, which are important for extracting main ideas, finding details, and analysing information. Reading aloud improves pronunciation, intonation, and fluency. In addition, discussing what have been read or completing tasks based on the text develops oral communication skills. Effective foreign language reading instruction for professional learners requires an integrated approach: systematic support for decoding and vocabulary, abundant meaningful reading opportunities, explicit strategy instruction, and careful alignment to workplace genres. Teachers should design curricula that combine extensive reading with targeted lessons and ongoing assessment to promote both comprehension and real world reading competence. Carefully chosen methods and strategies, beside their associated techniques, enable educators to effectively and efficiently teach reading skills to students. «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 51 References 1. Brown, H. D. Teaching by Principles: An Interactive Approach to Language Pedagogy. Englewood Cliffs, New Jersey: Prentice Hall Regents. 2001. 2. Carter, R. 1998. Orders of Reality: CANCODE, communication, and culture. ELT Journal 51/1 3. Cook, G. 1989. Discourse. Oxford University Press. 4. Davis, C. 1995. Extensive reading: an expensive extravagance? ELT Journal 49/4 5. Day, R. and Bamford, J. 1998. Extensive Reading in the Second Language Classroom. Cambridge University Press. 6. Farrell, T. 1998. Using TV “soaps” for listening comprehension. Modern English Teacher 7/2 7. Harmer, J. 2001. The practice of English language teaching. 3rd edition. London. Longman. 8. Nicholas Carr, The Shallows: How the Internet is changing the way we think, read and remember, Atlantic Books, 2010. 9. Paran, A. 1996. Reading in EFL: facts and fictions. ELT journal 50/1. 10. Tribble, C. 1997. Writing. Oxford University Press. 11. Wallace, C. 1992. Reading. Oxford University Press. 12. Wolf, Maryanne. Proust and the Squid: The Story and Science of the Reading Brain. Harper, 2007. Proceedings of the 12th International Scientific Conference 52 PEDAGOGICAL POTENTIAL OF 3D MODELING FOR DEVELOPING VISUAL LITERACY IN ART EDUCATION Yesmagambetova L.O. Master’s student, Artistic work, graphics and design, Kazakh National Women‘s Teacher Training University, Almaty, Kazakhstan Krykbayeva S.M. Candidate of Arts Study, Leader of educational programm, Kazakh National Women‘s Teacher Training University, Almaty, Kazakhstan Abstract This study explores the pedagogical potential of 3D modeling for developing visual literacy in art education, addressing the growing need for digital competencies in contemporary learning environments. Using a systematic and bibliometric review methodology based on PRISMA 2020 guidelines, the research analyzes 13 Scopus-indexed studies published between 2015 and 2025. The findings reveal a significant increase in scholarly interest in 3D modeling, particularly after 2020, reflecting global technological advancements and the expansion of digital instructional practices. The results demonstrate that 3D modeling enhances key components of visual literacy, including spatial reasoning, visual interpretation, structural analysis, and creative thinking. Keyword co-occurrence analysis shows that research in this field is structured around four major thematic clusters: pedagogy and digital learning, artistic and creative applications, e-learning and virtual reality, and creativity-centered approaches. Despite its strong potential, the integration of 3D technologies faces challenges such as limited technical infrastructure, insufficient instructor preparedness, and a lack of methodological frameworks. The study contributes to the theoretical and practical understanding of 3D modeling as a transformative tool in art education and highlights future directions for instructional design, empirical research, and digital literacy development. Keywords: 3D modeling; visual literacy; art education; digital pedagogy; computer graphics; spatial thinking; virtual reality; creativity; bibliometric analysis. Introduction Research relevance. In an era of rapidly evolving technologies, the field of art education is undergoing profound transformations and entering a period of fundamental change. In recent years, 3D modeling has emerged not only as a visualization tool but also as a key direction that is reshaping instructional approaches in visual arts, design, and architecture. Integrating threedimensional modeling technologies into the educational process significantly contributes to the development of spatial thinking skills. It enables learners to understand the form, structure, proportions, and spatial relations of objects at a fundamentally new level. All of these aspects promote the development of visual literacy an essential competency in the current era of globalization (Lee & Chen, 2024). Visual literacy refers to the ability to understand, construct, and interpret visual representations, as well as to critically evaluate visual messages within the media environment. In the field of art education, this concept is of particular importance because it fosters not only aesthetic perception but also analytical, logical, and creative thinking (Bamford, 2022). Traditional art education methods such as drawing and painting no longer fully meet the visual communication needs of contemporary learners living in a digital environment. This gives «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 53 rise to a new challenge: introducing 3D modeling into the learning process as an important pedagogical tool that combines creative thinking with technical literacy. In art education, 3D modeling enhances students’ spatial imagination, accuracy of visual visualization, ability to analyze objects from different perspectives, and understanding of the interrelation between form and composition (Rodriguez, 2023). Additionally, during practical classes, it develops students’ research skills and allows them to experiment with materials, forms, and object textures. As a result, learners become more engaged, and the learning process becomes interactive and closer to professional practice. However, despite the advantages of 3D modeling in art education, several challenges persist. These include the lack of adequate technical infrastructure in educational institutions, insufficient digital competency among instructors, and the absence of systematic methodological resources for integrating 3D modeling into the curriculum. In some cases, students perceive three-dimensional modeling purely as a technical activity, failing to grasp its artistic significance. Therefore, there is a need to develop methodological frameworks that promote conscious and purposeful use of 3D technologies. The purpose of this study is to theoretically substantiate the pedagogical potential of 3D modeling technologies in art education and to identify ways to enhance learners’ visual literacy. The study aims to: 1. Conceptualize the structure and components of visual literacy based on theories of art education and digital literacy; 2. Explore the pedagogical and methodological potential of 3D modeling in developing visual literacy; 3. Develop and pilot a model for integrating 3D modeling into the art education process; 4. Assess the effectiveness of 3D modeling and determine the level of visual culture formation based on a pedagogical experiment and result analysis. Research question: How does the use of 3D modeling technologies in the art education process contribute to the development of learners’ visual literacy? Theoretical and Methodological Framework. The study examines the development of visual literacy through the application of 3D modeling technologies in art education, emphasizing the importance of innovative methods and digital tools in modern educational systems. The research is grounded in the principles of digital pedagogy, which highlight the active role of learners in constructing new knowledge and understanding through personal experience. Rather than simply receiving ready-made information, learners explore artistic objects independently through 3D modeling, mastering texture, form, tone, and spatial relations through experiential learning. This approach significantly enhances visual thinking and creative abilities. Methodologically, the study employs systematic and bibliometric review techniques. Articles indexed in Scopus were selected using PRISMA guidelines based on relevant keywords. The collected data were categorized, visualized, and analyzed using Bibliometrix and VOSviewer software. Research Significance. This study is significant in its focus on enhancing learners’ visual literacy through the integration of 3D modeling in art education. By combining the principles of digital pedagogy and constructivism, the findings contribute to the development of students’ creative and spatial thinking skills. Furthermore, the study supports the modernization of art education through contemporary digital technologies and offers practical insights for improving teaching effectiveness. Literature Review Although the concept of visual literacy first entered educational theory in the 1960s, its meaning has evolved significantly with the development of digital technologies. Bamford (2022) defines visual literacy as «the ability to understand, analyze, and create new visual content». This competency has become essential not only in the arts but also in fields such as engineering, design, Proceedings of the 12th International Scientific Conference 54 media, and communication. 3D modeling directly contributes to several core components of visual literacy, including: – spatial thinking, – visual interpretation, – structural analysis of objects, – recognition of visual codes and symbols, – creative visual thinking. According to Rodriguez (2023), working with 3D objects strengthens the «see–construct– explain» cognitive cycle among children and university students. That is, learners do not merely observe an object; they reconstruct its structure in a digital environment, thereby deepening their understanding of visual information. The development of visual literacy depends not only on technical skills but also on aesthetic perception and creative thinking. Benham, Burroughs, and Osland (2024) demonstrate that working with computer graphics significantly enhances visual culture and helps learners develop a new artistic language. Over the last decade, 3D modeling technologies have become widely integrated into educational systems around the world. Scholars have extensively examined the positive impact of these technologies on students’ innovative thinking, creative decision-making, and understanding of complex visual systems (Chemerys, 2020). Their studies highlight the role of 3D modeling as a key tool in developing professional competencies among design students. Three-dimensional environments enable learners to analyze form, compare compositional solutions, and conduct visual experiments. 3D modeling is particularly important within the STEM + ART → STEAM framework. According to Ford and Minshall (2018), integrating 3D modeling into STEM and art classes is an effective way to foster creativity and engineering thinking. Priavolou and Pantazis (2017) describe 3D modeling as a new form of educational communication, emphasizing that the collaborative creation of digital objects strengthens students’ roles within the group and develops skills such as self-assessment and critical thinking. In recent years, art education has been increasingly enriched by project-based learning, interdisciplinary approaches, and peer learning methods. Otto and Mandorli (2015) argue that 3D modeling allows students to combine theoretical knowledge with practical skills through the creation of real products. The interdisciplinary approach connects art with informatics, engineering graphics, architecture, and media technologies. Knochel (2021) terms this model «multimodal inquiry» and shows that 3D modeling becomes a core component of creative research in mobile makerspace environments. Peer learning has also demonstrated high effectiveness in 3D modeling courses. Gladovic et al. (2024) emphasize that qualitative self-assessment and group discussions improve the quality of visual projects. By analyzing each other’s models, students gain a deeper understanding of visual articulation and aesthetic choices. Beyond artistic expression, 3D modeling strengthens several cognitive functions, including: – spatial visualization, – mental rotation, – visual memory, – constructive reasoning. Suzuki (2014) shows that computer graphics courses significantly enhance students’ spatial transformation skills, which are relevant not only in art but also in engineering, robotics, and medicine. Yoon (2010) finds that working in virtual and 3D environments expands students’ creative freedom, enabling them to think beyond the constraints of the physical world. However, several challenges have been identified in the integration of 3D modeling into art education systems: − insufficient technical infrastructure and equipment (Irwin et al., 2014; Lazarakou, 2024), − low digital skills among instructors (Schmidt, 2021), «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 55 − lack of methodological resources (Svobodova, Benesova & Cernochova, 2016), − students’ tendency to perceive 3D modeling as a purely technical skill (Patera, 2009). To address these issues, researchers recommend implementing blended learning, flipped classroom approaches, and workshop-based instructional models. Methods A systematic review methodology was adopted, following the PRISMA 2020 guidelines. The step-by-step selection procedure is illustrated in Figure 1, which presents the flow diagram detailing identification, screening, eligibility assessment, and final inclusion of studies. The database search was carried out in Scopus, using a structured Boolean query to ensure the precision and completeness of the results. The final search string was: TITLE-ABS-KEY ( "3D graphics" "modeling" ) AND PUBYEAR > 2014 AND PUBYEAR < 2026 AND ( LIMIT-TO ( LANGUAGE , "English" ) ) AND ( LIMIT-TO ( SUBJAREA , "SOCI" ) OR LIMIT-TO ( SUBJAREA , "ARTS" ) OR LIMIT-TO ( SUBJAREA , "COMP" ) ) AND ( LIMIT-TO ( EXACTKEYWORD , "3d Modeling" ) OR LIMIT-TO ( EXACTKEYWORD , "3d Graphics" ) OR LIMIT-TO ( EXACTKEYWORD , "Virtual Reality" ) ) This query made it possible to focus on English-language research published between 2015 and 2025, specifically within the subject areas of Social Sciences, Arts, and Computer Science, and indexed under keywords directly related to 3D technologies. The initial search produced 119 records, and at the identification stage automated tools flagged 3 records as ineligible, while no duplicate entries were detected; therefore, 116 records proceeded to the screening stage. During screening, titles and abstracts were reviewed for thematic relevance, and as shown in Figure 1, a total of 13 records were excluded for not meeting the initial criteria, leaving 103 records sought for full-text retrieval. However, the full texts of 52 studies were not accessible, which reduced the set to 51 records eligible for content analysis. Proceedings of the 12th International Scientific Conference 56 At the eligibility stage, all 51 studies were examined in detail, resulting in the exclusion of 29 studies that did not align with the topic of 3D modelling in art education, 5 studies that lacked relevance to artificial intelligence or academic writing, and 4 studies that fell outside the specified publication timeframe; in total, 38 studies were excluded. Ultimately, 13 studies fully met the inclusion criteria and were incorporated into the final systematic synthesis. These selected studies constitute the empirical and theoretical foundation for assessing the pedagogical potential of 3D modelling in developing visual literacy, and the complete flow of identification, screening, and eligibility assessment procedures is presented in Figure 1. Results In accordance with the topic “Enhancing Visual Literacy Through 3D Modelling in Art Education,” the analysis provides a quantitative overview of publication activity over the past decade. The dynamics of publications and citations reveal several distinct developmental phases. During the initial phase (2015–2021), the overall activity in this field remained very low. As illustrated in Figure 2, only 2 to 4 publications appeared annually, indicating that the field had not yet fully emerged as an independent research direction. This trend suggests that scholarly interest in the pedagogical application of 3D modelling and its role in visual literacy development was still «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 57 forming during this period. A noticeable shift occurred in the rapid growth phase (2020–2025). The increasing availability and accessibility of 3D modelling tools led to a significant rise in research output. Beginning in 2020, both publication counts and citation rates grew sharply, demonstrating heightened global interest in integrating 3D technologies into art and design education. This surge reflects broader technological trends as well as the expansion of digital practices in educational settings. The stabilisation phase (from 2025 onward) shows a marked increase compared to earlier periods, indicating that 3D modelling has become a well-established research topic. During this stage, the field matured into a wide-ranging thematic area with strong academic positioning. Studies began to focus not only on the technical aspects of 3D modelling but also on its methodological, pedagogical, and cognitive dimensions, highlighting its importance as a powerful instructional tool. The findings demonstrate that over the past 15 years, 3D modelling has evolved from a marginal topic into a rapidly expanding and influential domain within art education. While earlier research was limited, recent years show a clear upward trajectory, confirming the growing relevance of 3D modelling in contemporary educational and creative practices. Geographical analysis of publications and transition to conceptual trends. The geographical distribution of publications demonstrates that research on the pedagogical use of 3D modelling in art education is developing unevenly across regions but shows clear patterns of global expansion. The most active contributors are China (4 publications) and the United States (4 publications), which lead due to their strong digital infrastructure, established technological innovation ecosystems, and long-standing integration of computer graphics into educational frameworks. Their significant output also reflects national strategies aimed at advancing STEAM education and strengthening connections between art, technology, and engineering. The Russian Federation (2 publications) represents a growing research segment, reflecting an increasing emphasis on digital literacy and the adoption of 3D technologies in both art and engineering education, driven by curricular modernization and interdisciplinary collaboration. A wide group of countries including Australia, Brazil, Colombia, Ethiopia, Germany, Grenada, India, Malaysia, Nigeria, Thailand, and Turkey contributed one publication each, signaling early but promising stages of engagement with 3D modelling in pedagogical contexts. These isolated contributions often emerge from universities experimenting with digital tools, pilot projects, or national initiatives to integrate technology into creative disciplines. Although individually modest, these publications collectively indicate the global relevance of the topic and reveal how diverse educational systems explore 3D technologies for visual literacy development. The presence of countries from multiple continents Asia, Africa, Europe, and Latin America highlights the universal applicability of 3D modelling as both an educational and professional skill. This international distribution suggests that the field is transitioning from localized experimentation toward broader, globally coordinated research interests, where shared technological challenges and pedagogical goals create opportunities for future collaboration and comparative studies. As the geographic patterns confirm the worldwide spread of 3D modelling practices, a deeper understanding of the thematic and conceptual connections within this research area becomes essential. These conceptual relationships are visualized in Figure 2, which presents the keyword co-occurrence map and highlights the core clusters shaping the scientific landscape of 3D modelling in art education. The keyword co-occurrence map (Figure 2) illustrates the main conceptual clusters shaping the research landscape on 3D modelling in art education. The visual structure demonstrates how different thematic areas intersect, indicating interdisciplinary connections and emerging research priorities. Proceedings of the 12th International Scientific Conference 64 With the advent of AI, the teacher's role transforms: the educator is no longer the sole source of knowledge but becomes a facilitator of the learning process, helping students develop critical thinking, analysis, and independent decision-making. This shift from a reproductive model of education to a constructivist approach, where the student becomes an active participant in the learning process, requires teachers to develop new competencies, including digital literacy and ethical responsibility. AI enables the creation of a more personalized educational space, where each student can learn at their own pace and according to their needs. This opens up new horizons for adaptive learning, where educational materials, assignments, and tests are tailored based on the student's progress and abilities. AI can track each student's progress in real time, providing instructors with up-to-date data to adjust the curriculum and teaching methods. One of the prominent examples of AI usage is the case method, which is widely used in digital educational environments. In the case method, students analyze real or hypothetical situations, which helps develop critical thinking and provides students with the skills needed to analyze real-life situations, an essential component of their future professional activities. AI, in turn, can help create interactive case studies that automatically adapt to the student's level, offering more complex or simpler tasks depending on their progress Furthermore, AI greatly expands the possibilities for personalized learning. For example, adaptive learning systems can analyze student responses and, based on the collected data, adjust the upcoming tasks and educational materials. This allows students to receive an optimal level of difficulty, which not only enhances the quality of material comprehension but also reduces stress and frustration during learning. Digitalization of education, supported by AI, opens up vast possibilities for creating innovative content. With the help of AI, interactive courses can be developed that not only provide learning material but also actively engage students in the learning process. For example, interactive laboratories allow students to conduct experiments in a virtual environment, or simulations of professional situations help students acquire practical skills in a safe, controlled setting. Additionally, using AI, adaptive learning systems can be created that adjust the educational experience based on each student’s pace of learning. This allows effective work with both highly motivated students and those who need additional support, providing everyone with an individualized approach. While AI offers numerous advantages for education, it also brings about certain ethical challenges. One of the main concerns is the privacy of student data. To create a personalized learning experience, AI systems collect extensive data on students' actions and progress. These data can be used to improve learning further, but it is essential to ensure their proper protection to avoid any negative consequences. Another concern is the reduced personal interaction between teachers and students, which could negatively impact the quality of the educational experience. While AI can efficiently analyze and provide data on student progress, it cannot replace the emotional support and motivation that only humans can provide. Moreover, as AI is integrated into educational practices, it is essential for teachers to develop skills like digital empathy-the ability to perceive and respond to students' behavior in virtual environments-and moral responsibility for using technology in education. These are important aspects that must be considered when developing and implementing AI in educational processes. With the development of AI technologies, future education will see new learning formats, such as virtual teachers and smart educational platforms, which can personalize the learning process even further. This will provide real-time interaction between students and AI, helping to quickly respond to students' needs and providing adaptive learning materials. Thus, the integration of AI into pedagogical activity not only improves educational effectiveness but also «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 65 creates new challenges for educators, requiring continuous updating of competencies and pedagogical reflection. The Role of the Teacher as a Facilitator With the development of AI, according to the UNESCO model (2023), the role of the educator is shifting from the traditional role of the teacher as the transmitter of knowledge to the role of a facilitator - a helper in the learning process. The teacher helps students analyze data, interpret information, and work in teams. They guide students in the search for solutions but are no longer the sole source of knowledge. Thus, in the digital age, the educator must become the organizer of the educational process, capable of working with new technologies and managing the interaction between students and technologies. Ethics and the Humanistic Mission of the Teacher Although AI is changing the professional competencies of teachers, it is important to note that the humanistic mission of the educator remains unchanged. According to OECD (2024), teachers must retain their role as mentors who nurture rather than simply teach. This is especially crucial in the era of technology, where technology should not replace ethical values and the humanistic approach. Ethical guidelines such as empathy, respect for differences, and the creation of an inclusive environment should form the foundation for the interaction between humans and AI. Development of New Competencies for Educators With the development of technologies, teachers must acquire new competencies, such as: − Digital literacy - the ability to effectively use AI and other digital technologies in the educational process. − Critical thinking - the ability to evaluate and analyze data obtained through AI. − Ethical responsibility - understanding the risks associated with the use of AI and the ability to make decisions while considering ethical norms. These competencies will contribute to a more harmonious integration of the technological and humanistic components of the educational process. Advantages and Challenges of Education Digitalization Advantages:  Personalized learning, where each student receives material tailored to their level of knowledge.  Increased accessibility of education through online platforms and the use of AI to create adaptive courses.  Enhanced learning effectiveness through the use of intelligent systems that can analyze student progress and suggest additional resources. Challenges:  Ethical risks associated with the use of AI, such as data privacy and dependency on technologies.  Reduced personal interaction between teachers and students, which could lower the quality of the educational experience.  The need for continuous knowledge updates for educators so they can effectively work with new technologies. Model of the Teacher's New Professional Identity Based on the analysis of international studies such as those by OECD and UNESCO, a model of the new professional identity of the teacher has been proposed, which includes several key aspects: − Digital competence and the ability to integrate AI into the educational process. − Teamwork with other educators and professionals in the field of technology. − Ethical responsibility in the use of technologies in the educational environment. Proceedings of the 12th International Scientific Conference 66 − Humanistic thinking, which helps preserve the values of education despite the widespread adoption of AI. Conclusion The transformation of the teacher’s role in the era of artificial intelligence is an inevitable process in the context of education digitalization. The teacher of the future is not only a specialist using new technologies but also an educator who maintains their humanistic mission, capable of working in collaboration with AI and ensuring high-quality education for all students. Further research in this area should focus on studying the ethical aspects of human–AI interaction, as well as developing methodologies that will ensure a balance between technological efficiency and the humanistic approach in education. References 1. UNESCO. Artificial Intelligence in Education: Challenges and Opportunities. – Paris, 2023. 2. OECD. AI and the Future of Teaching. – Paris, 2024. 3. Bates T. Teaching in a Digital Age. – Vancouver, 2022. 4. Bulatbaeva A.A. Method of Modeling in Research Activities of Master's Students // Vestnik KazNU. – 2013. – No. 2(39). 5. Kotlyarova I.O. Method of Modeling in Pedagogical Research // Vestnik YuUrGU. – 2019. – No. 1. «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 67 ӘОЖ 57:004.8 БИОЛОГИЯЛЫҚ BIG DATA ТАЛДАУДА ЖАСАНДЫ ИНТЕЛЛЕКТІНІҢ ИННОВАЦИЯЛЫҚ МҮМКІНДІКТЕРІ Кыдырбаева Н.Х. 8D01503 – «Биология» білім беру бағдарламасының 1 курс докторанты, І.Жансүгіров атындағы Жетісу университеті, Қазақстан Республикасы, Талдықорған қ. Мукашева Д.М. PhD, І.Жансүгіров атындағы Жетісу университеті, Қазақстан Республикасы, Талдықорған қ. Ауелбек М.А. PhD, І.Жансүгіров атындағы Жетісу университеті, Қазақстан Республикасы, Талдықорған қ. Аннотация 2010-жылдардың соңынан бастап жасанды интеллект (ЖИ), оның ішінде машиналық оқыту технологиялары өмір туралы ғылымдар саласындағы зерттеулерді түбегейлі өзгертіп келеді. ЖИ биологиялық үдерістерді есептік талдауда, табиғи қосылыстарды анықтауда және экожүйелер динамикасын зерттеуде маңызды құралдардың біріне айналды. Осы шолуда жоғары өнімді жүйелі молекулалық деректер деректерді жинаудың жедел өсуі өмір туралы ғылымдарда ЖИ-ге негізделген талдау әдістерінің қажеттілігін арттырғаны қарастырылады. Жүйелі молекулалық деректер (omics data) - бұл биологиялық жүйелерді толық әрі жанжақты зерттеуге арналған жоғары өнімді молекулалық деңгейдегі деректер жиынтығы. Олар жасушадағы барлық молекулалардың (гендер, РНҚ, ақуыздар, метаболиттер және т.б.) тұтас жиынтығын қамтиды. Негізгі назар ботаника және микробиология салаларындағы қолданбалы мүмкіндіктерге аударылған. Біз жүйелік биологияда жүйелі молекулалық деректерге негізделген болжау модельдерінің рөлін және күрделі биологиялық жүйелерді тереңірек түсінуге мүмкіндік беретін ЖИ-ге негізделген инновациялық аналитикалық тәсілдерді атап өтеміз. Сонымен қатар, жүйелі молекулалық деректердің FAIR принциптеріне (деректерді оңай табу мүмкіндігі, қолжетімділік, үйлесімділік және қайта пайдалану мүмкіндігі) сәйкестігін қамтамасыз етудің маңыздылығы талқыланады. Кілт сөздер: жасанды интеллект, геномика, феномика, протеомика, транскриптомика, Dendral жүйесі, Big Data, Оmics Data, FAIR принциптері. Dendral – 1960-1970 жылдары жасалған жасанды интеллекттің алғашқы эксперттік жүйелерінің бірі. Ол химиялық деректерді талдауға арналған және масс-спектрометриялық деректер негізінде органикалық қосылыстардың молекулалық құрылымын анықтау үшін қолданылған. Жүйе логикалық қорытындылау ережелері мен сарапшылардың білімін пайдалана отырып, күрделі ғылыми мәселелерді шешуге жасанды интеллекттің қабілетті екенін көрсетті. Dendral химия, биология және ғылыми деректерді талдау салаларындағы заманауи ЖИ жүйелерінің дамуына негіз қалады. Биология саласындағы рөлі. Биология саласында Dendral маңызды әдіснамалық рөл атқарды. Алғашында химия үшін жасалғанымен, оның негізгі қағидалары - эксперттік білімді қолдану, логикалық шығару және гипотезаларды автоматты түрде құру - кейінірек Proceedings of the 12th International Scientific Conference 68 биоинформатикада кеңінен қолданылды. Бұл тәсілдер биологиялық құрылымдарды, молекулаларды, метаболизмдік жолдарды және генетикалық деректерді талдауда пайдаланылып, биологиялық Big Data-ны интеллектуалды өңдеудің дамуына ықпал етті. Dendral жүйесінің жұмыс істеу принципі эксперттік тәсіл мен логикалық қорытындылауға негізделген: Деректерді енгізу - жүйе эксперименттік деректерді (мысалы, масс-спектрлер) және бастапқы шектеулерді қабылдайды. Білім қоры - сарапшылар (химиктер, биологтар) енгізген ережелер мен эвристикалардан тұрады. Гипотезаларды генерациялау - берілген шектеулерге сәйкес келетін барлық ықтимал құрылымдар автоматты түрде құрылады. Іріктеу және тексеру - логикалық ережелер арқылы мүмкін емес нұсқалар алынып тасталады. Нәтижелерді бағалау - қалған гипотезалар эксперименттік деректерге сәйкестік деңгейі бойынша реттеледі. Осылайша, Dendral сарапшының ғылыми ойлау процесін модельдейді, бұл кейінгі биология және биоинформатика саласындағы интеллектуалды жүйелердің дамуына негіз болды. Эксперттік жүйенің алғашқы үлгісі - 1965 жылы Эдвард Фейгенбаум (оны «эксперттік жүйелердің атасы» деп те атайды) және Калифорния штатындағы Стэнфорд университетінің өкілі Джошуа Ледерберг әзірлеген Dendral жүйесі болды (Сурет 1). Сурет 1. – Эдвард Фейгенбаум мен Джошуа Ледербергтің Dendral жүйесін сынақтан өткізуі Dendral жүйесі жасанды интеллекттің ғылыми зерттеулердегі алғашқы қадамдарының бірі болып, эксперттік білімді пайдаланып күрделі химиялық деректерді талдауды автоматтандырған болатын. Оның жұмыс принциптері - деректерді енгізу, гипотезаларды генерациялау және логикалық қорытынды шығару - кейінгі биоинформатика мен молекулалық модельдеудегі көптеген шешімдердің негізін қалаған. Бүгінде биология мен биомедицина саласында Dendral сияқты принциптерді дамытқан көптеген заманауи бағдарламалар мен платформалар бар. Олар молекулалардың құрылымын болжау, дәрілік заттарды іздеу, ақуыз және нуклеин қышқылдарының өзара әрекеттесуін талдау сияқты күрделі мәселелерді шешуге арналған. Жүйелі молекулалық (omics data) деректердің қарқынды өсуі өмір ғылымдарын зерттеуде жасанды интеллектіні қолдануды қажет етеді. Соңғы екі онжылдықта зерттеулер «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 69 мен қоғам «өмір ғылымдарындағы үлкен деректер» дәуіріне аяқ басты. Технологиялық прогресс биологиялық молекулалардың (мысалы, ДНҚ, РНҚ, ақуыздар, метаболиттер) және фенотиптердің сапалық және сандық өзгергіштігін өлшеу мүмкіндіктерін кеңейтті, бұл бір эксперимент аясында ірі және күрделі omics data жиынтықтарын алу үрдісін кеңінен таралуына әкелді. Өмір ғылымдарында жүйелі молекулалық (omics data) деректердің қарқынды өсуі негізінен геномикадан басталды. Бұл процесс шамамен 20 жыл бұрын келесі буын ДНҚ секвенирлеу платформаларының (Next-Generation Sequencing, NGS) пайда болуымен байланысты. ДНҚ-ны секвенирлеудің Сэнгер әдісі 1970-жылдары ашылғанына қарамастан, қысқа оқылымдарға негізделген екінші буын NGS әдісінің енгізілуі үшін үш онжылдық қажет болды. Осы әдістің қолданылуы ДНҚ секвенирлеуде жаңа революция туғызып, оның қолжетімділігін және өнімділігін едәуір арттырды. Нәтижесінде жануарлар мен өсімдіктердің мыңдаған de novo геномдары жинақталып, бүкіл геном бойынша миллиондаған бір нуклеотидтік полиморфизмдер (Single Nucleotide Polymorphisms, SNP) анықталды. Сонымен қатар, бірнеше гендік транскрипттерді жоғары өнімді талдау, яғни транскриптомика, 1990-жылдардың ортасында гибридизацияға негізделген микрочип технологияларының енгізілуімен басталды. Өмір ғылымдарында зерттелетін деректер өте үлкен және күрделі. ЖИ бұл деректерді талдауға, түсінуге және болжау жасауға көмектеседі. Соңғы 20 жылда технологияның дамуы молекулалық деңгейде деректер жинауды әлдеқайда жеңілдетті және жылдамдатты [1]. 2000-жылдардан бастап NGS (жаңа буын секвенирлеу) технологиясы РНҚ молекулаларының әртүрлілігін бұрынғыдан да дәл зерттеуге мүмкіндік берді. Бұл әдіс экспрессия деңгейлерінің кең диапазонын, сондай-ақ альтернативті сплайсинг сияқты күрделі процестерді анықтауға жол ашты. Мұндай зерттеу әдісі РНҚ-секвенирлеу (RNA-seq) деп аталады және РНҚ-дан алынған кДНҚ-ларды жаңа буын секвенирлеу құрылғылары арқылы оқу негізінде жүргізіледі [2]. Үшінші буын секвенирлеу технологиялары (PacBio, Oxford Nanopore Technologies) ДНҚ мен РНҚ-ны оқу ұзындығын ұзартты, деректерді тезірек алуға және нәтижелердің дәлдігін арттыруға мүмкіндік берді. Протеомика мен метаболомика ақуыздар мен метаболиттердің құрамын зерттеу үшін масс-спектрометрияға сүйенеді. Масс-спектрометрия 1940-жылдардан бері белгілі, бірақ оның газдық және сұйықтық хроматографиясымен біріктірілуі, сондай-ақ ESI (электрораспылдау ионизациясы) және MALDI сияқты жаңа ионизация әдістерінің пайда болуы бұл технологияның биологияда қолдану аясын ерекше кеңейтті [3]. Бұрын электрондық соққы арқылы ионизациялау кең қолданылған, бірақ ол молекулаларды қатты фрагментациялайтын. Қазіргі кезде оны жұмсақ ионизация әдістері - ESI және MALDI алмастырды. Бұл әдістер биомолекулалардың құрылымын бұзбай, оларды дәл анықтауға мүмкіндік береді. Соңғы 20 жылда жоғары айырымдылықтағы масс-спектрометрия (HR-MS) күшейіп, ақуыздар мен метаболиттерді өте дәл анықтауға жағдай жасады. Осылайша, протеомика мен метаболомика күрделі биологиялық үлгілерді зерттеуде кеңінен қолданылатын маңызды әдістерге айналды [4]. Соңғы жылдары визуализация технологиялары (яғни, түрлі құрылғылар арқылы объектілерді түсіру және бақылау әдістері) тез дамып, жаратылыстану ғылымдарына үлкен өзгеріс әкелді. Бұл жаңалықтар тек медицинаға ғана емес, өсімдіктерді зерттеу саласына да көп пайдасын тигізіп жатыр. Бүгінде өсімдіктердің және ауыл шаруашылығы дақылдарының сыртқы белгілерін (феномикасын) зерттеу қарқынды дамуда. Себебі жаңа сенсорлар, машиналық көру жүйелері (камералар мен арнайы программалар) және автоматтандыру технологиялары бұл саланың мүмкіндіктерін кеңейтіп отыр [5]. Қазір автоматтандырылған, өсімдікке зиян келтірмей жұмыс істейтін, көп мәлімет жинауға қабілетті құрылғылар өте үлкен көлемде суреттер мен датчик деректерін шығарады. Бұл деректер зерттеушілерге өсімдіктерді тереңірек түсінуге көмектеседі. Бірақ мәліметтің тым көп болуы - оларды өңдеу мен талдау кезінде қосымша қиындықтар туғызады. Алдыңғы қатарлы технологиялардың Proceedings of the 12th International Scientific Conference 70 дамуы биологияда өте көп және күрделі omics dataдеректерді (геномика, транскриптомика, протеомика, метаболомика сияқты) үлкен көлемде алуға мүмкіндік берді. Мұндай деректер биологиялық жүйелердің қаншалықты күрделі екенін терең зерттеуге жол ашады. Бір эксперименттен бірнеше түрлі omics dataмәліметті біріктіре отырып, ғалымдар организмнің ішкі деңгейдегі молекулалық өзгерістері оның сыртқы белгілерімен қалай байланысты екенін толық түсіне алады. Алайда соңғы 20 жылда бір-бірімен тығыз байланысты мыңдаған, тіпті миллиондаған молекулалық көрсеткіштердің (мысалы, SNP, транскрипттер, ақуыздар, метаболиттер) фенотиппен байланысын анықтау өте қиын мәселе болып қалуда [6]. Бұл деректер соншалықты көп және күрделі болғандықтан, оларды адам миы өздігінен талдай алмайды. Осындай үлкен және көп өлшемді деректерді түсіну үшін қуатты компьютерлер қажет. Қазіргі уақытта жеке компьютерлерден бастап үлкен физикалық және бұлттық серверлерге дейін - барлығы деректерді тез әрі тиімді талдауға мүмкіндік береді. Сондықтан жасанды интеллект (ЖИ) өмір туралы ғылымдарда негізгі құралға айналды. ЖИ болашақта көптеген биологиялық жаңалықтардың ашылуына жетекші рөл атқарады деп күтіледі. Сурет 2 жасанды интеллекттің omics data деректерін талдауда қолданылуы жыл сайын қарқынды өсіп жатқанын көрсетеді. Соңғы он жылда бұл тақырыпқа қызығушылық бірнеше есе артқан. “Source: PubMed search results (as of 19 Sept 2024)”. Сурет 2. Соңғы 20 жылдағы жасанды интеллект пен Omics зерттеулерінің жарияланым динамикасы 2024 жылдар аралығында PubMed деректер қорынан жасанды интеллект және Omics, өмір туралы ғылымдар бойынша барлығы 1362 жарияланым табылған (2024 жылдың 19 қыркүйегіндегі мәлімет). Соңғы 20 жылдағы осы кілт сөздермен жүргізілген әдебиеттерді талдау көрсеткендей, өмір туралы ғылымдар саласында жасанды интеллекттің қолданылуы өте жылдам дамып келе жатқан зерттеу бағыты болып табылады. Қарапайым түрде алғанда, жасанды интеллект (ЖИ) – бұл компьютерлік ғылымның бір саласы деп қарастыруға болады, ол машиналарды (әдетте бір немесе бірнеше компьютерді) белгілі тапсырмаларды орындауға үйретуге бағытталған, яғни алдын ала берілген мәліметтер жиынтығына негізделген оқыту арқылы жұмыс істейді [7] (сурет 3). «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 71 Сурет 3. Жасанды интеллект (ЖИ), машиналық оқыту (МО), Нейрондық желілер арқылы күрделі үлгілерді үйрену (НҮ) және Omics ғылымдарының өзара байланысын көрсетеді. Мұндай ЖИ күнделікті қолданылатын көптеген тапсырмаларды орындайды, мысалы: спамды сүзу, сөйлеуді тану, тілдік аударма, онлайн жарнама, суреттерге таңба қою және т.б. ЖИ әлі дамудың бастапқы кезеңінде. Мұндай ЖИ түрлері мәліметтерді адам интеллектімен салыстырмалы немесе одан да жоғары деңгейде үйреніп, түсінуге қабілетті машиналарды жасауға бағытталған [8]. 1. Artificial Intelligence - Жасанды интеллект. Бұл адамдардың интеллектін имитациялайтын компьютерлерді жасауға бағытталған теориялар мен әдістер жиынтығы. Оның құрамына білімді көрсету, автоматты пайымдау, визуалды қабылдау, ақылды роботтар кіреді. 2. Machine Learning -Машиналық оқыту. Компьютерлерге мәліметтерден үйренуге және уақыт өте өнімділігін арттыруға мүмкіндік беретін әдістер жиынтығы. Мысалы: Support Vector Machine, Decision Tree, Random Forest, Bayesian Classifier, Linear Regression. 3. Deep Learning - Нейрондық желілер арқылы күрделі үлгілерді үйрену. Мұнда жасанды нейрондық желілер қолданылады, олар адамның миы сияқты жұмыс істейді және үлкен көлемдегі мәліметтен үйренеді. 4. Data Science - Деректер ғылымы. Бұл мәліметтерді жинау, басқару және талдау саласы. Omics-Data Science шеңберімен байланысты. Жасуша құрамын кешенді талдауға бағытталған ғылымдар: геномика, транскриптомика, протеомика, энзимомика, метаболомика, феномика. Өсімдіктер ғылымында жасанды интеллект (AI) қолдану. Omics зерттеулерінің қарқынды дамуы өсімдіктер ғылымында зерттеулерді түбегейлі өзгертті. Сонымен бірге, бұл тәсіл жоғары күрделілігі мен үлкен көлемі бар деректерді өңдеу үшін машиналық оқытуды (МО) қолдануды қажет етті [9]. Феномика - өсімдіктердің сыртқы белгілері мен қасиеттерін зерттейтін сала, ол бастапқыда тек зерттеушілерге арналған перспективалы бағыт болса, қазір өсімдіктер мен ауыл шаруашылық мәдениеттерінде кең қолданылатын құралға айналды. Бұл прогресс заманауи сенсорлар мен визуализация технологияларының (RGB, мультиспектральды, гиперспектральды, жылулық және флуоресценттік камералар мен сенсорлар) дрондар мен жердегі роботтармен бірігуінің арқасында мүмкін болды. Олар жоғары өнімді фенотиптеу деректерін жинай алады. Машиналық оқыту алгоритмдері үлкен көлемдегі суреттер мен сенсорлық деректерден маңызды белгілер мен сипаттамаларды шығару үшін тиімді әдіс болып табылады. Мысалы, Аймақтық нейрондық желілер (CNN) өсімдіктердің суреттеріне негізделген фенотиптеу кезінде ең тиімді болып саналады. Олар биотикалық және абиотикалық стресс әсерлерін болжауда, сондай-ақ өсімдіктер ауруларын тез әрі дәл диагностикалауда аса пайдалы. Аймақтық нейрондық желілер (CNN, Proceedings of the 12th International Scientific Conference 72 Convolutional Neural Network) – бұл суреттер, видео немесе басқа көпөлшемді деректердегі үлгілерді тануға арналған нейрондық желі. Суреттегі жапырақтың пішінін немесе тамыр жолдарын анықтау керек делік. CNN кішкентай фильтрді сурет бойымен «сырғытып», әр аймақтағы маңызды белгілерді шығарады. Осылайша желі суреттегі күрделі үлгілерді автоматты түрде таниды. Қорыта айтқанда сурет немесе деректерді кішкентай аймақтарға бөліп, олардың ерекшеліктерін (features) шығару әдісін білдіреді, және бұл операция CNNнің негізгі құрылымы болып табылады (сурет -4). Сурет 4. Конволюция – сурет немесе сигналдағы автоматты түрде бөліп шығару әдісі, CNN-нің негізгі операциясы Конволюция – CNN архитектурасындағы негізгі операция, ол суреттен маңызды белгілерді автоматты түрде бөліп алуға мүмкіндік береді. Сонымен қатар, тамыр жүйесінің құрылымын талдау үшін AI қолдану өсімдіктер физиологиясының аз зерттелген аймақтарын зерттеуге мүмкіндік береді және ауыл шаруашылығында «Екінші жасыл революцияны» жылдамдатуы мүмкін. Өсімдіктерді селекциялау да геномика жетістіктерінің арқасында түбегейлі өзгерді. Селекционерлер геномдық болжам (GP) әдісін пайдаланып, SNP маркерлері бойынша мақсатты белгілерді тезірек жақсартуға тырысады. Классикалық GP модельдері жақсы дәлдік берсе де, жаңа МО негізіндегі алгоритмдер күрделі белгілерді болжауды жақсартуға бағытталған. Күрделі белгілерді болжаудағы бір мәселе – генетикалық емес вариациялар мен гендердің өзара әсерлері. Бұл мәселені шешу үшін транскриптомика, протеомика, метаболомика сияқты басқа Omics деректерін GP модельдеріне қосу ұсынылады. Әсіресе, метаболомика фенотипке ең жақын қабат болып саналады және оның қолданылуы өсімдіктердің өнімділігін дәлірек болжауға мүмкіндік береді [10]. Қазіргі таңда өмір туралы ғылымдарда жүйелі молекулалық деректердің (omics data) қарқынды өсуі биологиялық зерттеулердің сипатын түбегейлі өзгертті. Геномика, транскриптомика, протеомика, метаболомика және феномика сияқты көпөлшемді деректердің үлкен көлемі биологиялық жүйелердің күрделілігін тереңірек түсінуге мүмкіндік бергенімен, оларды дәстүрлі статистикалық әдістермен талдау айтарлықтай шектеулерге ие. Осы тұрғыдан алғанда, жасанды интеллект пен машиналық оқыту әдістері биологиялық Big Data талдауда шешуші рөл атқаратын инновациялық құралдарға айналды. Мақалада omics деректерінің қалыптасу тарихы мен технологиялық дамуы, сондай-ақ олардың биологиядағы Big Data мәселесін қалай туындатқаны жан-жақты қарастырылды. Жаңа буын секвенирлеу технологиялары, жоғары айырымдылықтағы масс-спектрометрия және заманауи визуализация жүйелері бір эксперимент аясында көпқабатты деректерді жинауға жол ашты. Мұндай деректерді интеграциялау және олардың фенотиппен өзара байланысын анықтау үшін жасанды интеллектіге негізделген әдістердің қажеттілігі айқын көрсетілді. Жасанды интеллектінің, әсіресе машиналық оқыту мен нейрондық желілерге негізделген тәсілдердің, өсімдіктер ғылымындағы қолданбалы мүмкіндіктері ерекше атап өтілді. Жоғары «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 73 өнімді фенотиптеу, биотикалық және абиотикалық стресс факторларын болжау, өсімдік ауруларын диагностикалау және геномдық болжамды жетілдіру бағыттарында бұл технологиялар жоғары тиімділік көрсетуде. Сонымен қатар, метаболомика сияқты фенотипке жақын omics қабаттарын машиналық оқыту модельдеріне енгізу күрделі белгілерді болжаудың дәлдігін арттыруға мүмкіндік беретіні көрсетілді. Жасанды интеллект биологиялық Big Data талдауда тек көмекші құрал ғана емес, биологиялық жүйелерді кешенді түсінуге және жаңа ғылыми заңдылықтарды ашуға бағытталған негізгі қозғаушы күшке айналып отыр. Пайдаланылған әдебиеттер тізімі 1. Stephens ZD, Lee SY, Faghri F, et al. Big data: astronomical or genomical?. PLoS Biol. 2015;13:e1002195. 10.1371/journal.pbio.1002195. 2. Giani AM, Gallo GR, Gianfranceschi L, et al. Long walk to genomics: history and current approaches to genome sequencing and assembly. Comput Struct Biotechnol J. 2020;18:9–19. 10.1016/j.csbj.2019.11.002. 3. Wang Z, Gerstein M, Snyder M. RNA-seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10:57–63. 10.1038/nrg2484. 4. Lowe R, Shirley N, Bleackley M, et al. Transcriptomics technologies. PLoS Comput Biol. 2017;13:e1005457. 10.1371/journal.pcbi.1005457. 5. Amarasinghe SL, Su S, Dong X, et al. Opportunities and challenges in long-read sequencing data analysis. Genome Biol. 2020;21:1–16. 10.1186/s13059-020-1935-5. 6. Marx V. Method of the year: long-read sequencing. Nat Methods. 2023;20:6–11. 10.1038/s41592-022-01730-w. 7. Griffiths J. A brief history of mass spectrometry. Anal Chem. 2008;80:5678–83. 10.1021/ac8013065. 8. McLafferty FW. A century of progress in molecular mass spectrometry. Annu Rev Anal Chem. 2011;4:1–22. 10.1146/annurev-anchem-061010-114018. 9. Mann M, Kelleher NL. Precision proteomics: the case for high resolution and high mass accuracy. Proc Natl Acad Sci USA. 2008;105:18132–38. 10.1073/pnas.0800788105. 10. Alseekh S, Fernie AR. Metabolomics 20 years on: what have we learned and what hurdles remain?. Plant J. 2018;94:933–42. 10.1111/tpj.13950. . Proceedings of the 12th International Scientific Conference 80 üçün bir çərçivə təmin edir. Bu çərçivə ilə pedaqoqlar, tələbələr Onlar üçün həqiqətən inklüziv təhsil yaratmaq məqsədi ilə prosesi nəzərdən keçirə bilərlər (Anderson və Boyle, 2014). Bir sözlə, təhsilin ekologiyası konsepsiyasında deyildiyi kimi, şagird uğurlu, dəyərli və işrakçı bir fərd kimi qiymətləndirilməli və fərdin ətrandakı sistemlər qiymətləndirilməlidir. Bunun fərdin təhsilinə necə təsir etdiyi nəzərə alınmalıdır. İnklüziv təhsilin məqsədi bütün tələbələrin müxtəlif öyrənmə ehyaclarını ödəməkdir. (YUNESKO, 1994). Bu məsələ ilə bağlı maarifləndirmə gücləndirilməlidir. İnklüziv təhsil təcrübələri bütün sistemlərə tətbiq oluna bilər. İnklüziv təhsil inkişaf etdikdə, yəni ümumən cəmiyyətə təsir etdikdə daimilik qazanar. Ədəbiyyat: 1. Ainscow, M. (2002). İnklüziv məktəblərin inkişanın başa izahı. London: Falmer Press. 2. Ainscow, M., Dyson, A., Goldrick, S., and West, M. (2012). Bərabər təhsil sistemlərinin inkişa. 3. Abington, Oxon: Routledge. 4. Anderson, J., Boyle, C. və Deppeler, J. (2014). İnklüziv təhsilin ekologiyası: 5. Bronfenbrenner. H. Zhang və P. Chan (Red.), Təhsildə Bərabərlik: Ədalət və Daxiletmə (səh. 24-38). Avstraliya: Brill və Sense 6. Birləşmiş Millətlər Təşkila (1948). Ümumdünya İnsan Hüquqları Bəyannaməsi. 7. hp://www.unicankara.org.tr/doc_pdf/h_rigths_turkce.pdf sayndan götürülüb. 8. BlackHawkings, K. (2010). İşrak üçün Çərçivə: Tədqiqat üçün tədqiqat alə nailiyyət və məktəbə daxil olma arasında əlaqə. Beynəlxalq Araşdırmalar Jurnalı & Təhsildə Metod, 33(1), 21-40. doi: 10.1080/17437271003597907 9. Boyle, C., Topping, K., Jindal-Snape, D., and Norwich, B. (2012). üçün həmyaşıd dəstəyinin əhəmiyyə 10. xüsusi təhsilə ehyacı olan uşaqları daxil edərkən pedaqoji heyət. Beynəlxalq Məktəb psixologiyası, 3 3(2), 167–184. 11. Bronfenbrenner, U. (1970). Uşaqlar və valideynlər (səh. 241–255). Vaşinqton, DC: ABŞ Hökumət Çap Ofisi. «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 81 Müəllimin peşə ustalığı və nüfuzu Rəfiyeva Xuraman Əli qızı Baş müəllim, Azərbaycan Dövlət Pedaqoji Universitetinin, Şəki filialı, Fransız dili müəllimi Orcid 0000-00019887-4619 Müəllimin dünyagörüşü, onun davranışı, həyatı, hadisələrə münasibəti bu və ya digər şəkildə bütün şagirdlərə təsir edir M.İ.Kalinin "Müəllim bir çıraqdır" ifadəsi müəllimin işıq saçan, yol göstərən, bilik və dəyərləri nəsildən-nəslə ötürən bir mənbə olduğunu bildirən metaforik bir fikirdir. Müəllim şagirdin həyatına işıq gətirir, cəhalətdən xilas edir və onların gələcəyinə "çıraq" tutur. Bu işıq müəllimdən şagirdə, şagirddən isə yeni nəsillərə keçərək davam edir. Müəllim tək bir fərd deyil, bir çox şagirdin həyatına təsir edən və onların "çağdaş" olmasında mühüm rol oynayan bir insandır.Bu işığın heç vaxt sönməyəcəyinə dair ümid yaradır. Müəllimindən şagirdə, şagirddən də öz yetirmələrinə ötürülən bu işıq daim davam edir. Ona görə də müəllim fənninə aid dərin bilik sahibi olmalıdır.Bunsuz ən mütərəqqi metodika belə onun işində irəlıləyişə səbəb ola bilməz. Müəllim öyrədici olmalıdır – bu vəzifə müəllim fəaliyyətinin əsasını təşkil edir. Belə ki, müəllim öyrətməklə bərabər, öyrədə-öyrədə özü də öyrənir. Şagirdlər müəllimin biliyinə və öyrətmə tərzinə xüsusi diqqətlə yanaşdığı üçün, müəllimdə hər hansısa bir qüsur tapdıqları zaman bu səhvə qarşı reaksiya verirlər. Ona görə də pedaqoji fəaliyyətə başlayan hər bir gənc müəllim buna diqqət yetirməlidirlər. Müəllim özünün tədris etdiyi fənni yaxşı bilməlidir, əks halda dərs zamanı istifadə edilən hər hansısa metod və üsul onun üçün yararlı və əlverişli şərait yaratmayacaqdır. Müəllim peşə ustalığı zamanı müəllim adına və nüfuzuna, habelə təhsil müəssisəsinin işgüzar nüfuzuna xələl gətirə biləcək hərəkətlərə yol verməməlidir. Mədəni davranış – müəllim təhsilalanlarla, rəhbərlik və həmkarları ilə davranışında nəzakətli, xeyirxah, diqqətli və təmkinli olmalıdır. Şagirdlər müəllimlərinin hansı bilik sahibi olduğunu çox tez hiss edirlər və onların zəif (necə deyərlər zəif damarını ) tərəflərindən tutmağa çalışırlar. Hətta onlar ,,proqram” dairəcindən kənara çıxan suallara da cavab axtarırlar. Elə hallar da olur ki, müəllim şagirdlərin suallarına cavab verə bilmir. Bəziləri bu halda çıxılmaz vəziyyətdə qalıb, bəziləri də hazırda bu suala cavab verə bilmərəm, bir qədər sonra bu suala qayıdarıq deyə cavab verib. Bu, müəllimin nüfuzunu aşağı salmır, əksinə şagirdlər onlara verilən səhv cavabı tez hiss edir və müəllimə inanırlar. Müəllim özünün pedaqoji təcrübəsini günbəgün artırmalı və təkmilləşdirməlidir. ”Müəllimin pedaqoji təcrübəsi onun pedaqoji statusundan asılıdır,, fikrini qəti şəkildə demək olmaz. “Pedaqoji təcrübə,, anlayışı illərin sayından asılı deyil. Hamımızın çalışdığı məktəblərdə uzun illər işləməsinə baxmayaraq öz dərslərini yüksək səviyyədə qura bilməyən, şagirdlərin hörmətini qazanmayan müəllimlərlə yanaşı, qısa vaxtda əsl pedaqoq səviyyəsinə yüksələn müəllimlərimiz də vardır. Hətta çoxdur desək daha düz olar. Pedaqoji ustalığın ilkin şərtləri biliyin toplanması, qabaqcıl təcrübənin öz işində yaradıcılıqla tətbiq edilməsi və özünün iş təcrübəsini ümumiləşdirərək yoldaşlarına çatdırmasıdır. Müəllim nüfuzuna təsir edən ən başlıca amillərdən biri də onun şəxsi nümunəsi, özünə və şagirdlərinə qarşı yüksək tələbkarlığıdır. Şagirdlər öz ixtisasını dərindən bilən tələbkar müəllimlərə hörmət edirlər. Təcrübə göstərir ki, müəllimin nüfuzunu qoruyub saxlaması çox çətindir, ona görə ki, bu proses özünə nəzarətin bir an da zəifləməməsini, əksinə, gücləndirilməsini tələb edir. Proceedings of the 12th International Scientific Conference 82 Gənclərlə açıq söhbət etmək, onlarla birlikdə çətin suallara cavab tapmaq, özünün işindəki zəif cəhətləri şagirdlərdən öyrənmək müəllimlərin iş təcrübəsinə daxil olmalıdır. Biz şagirdləri deyilənlərə passif surətdə qulaq asmağa məcbur etməklə onların yaradıcılıq imkanlarını məhdudlaşdırmış oluruq. Ən başlıcası odur ki, şagirdlərin real imkanlarını nəzərə almaqla onların hər birinin bilik səviyyəsinə uyğun bilik verilməsidir. Orta səviyyəli uşaqlar üçün nəzərdə tutulan proqram və dərsliklər mənimsəmə qabiliyyəti aşağı olan şagird üçün alınmaz qaladırsa, dərketmə qabiliyyəti yüksək olan şagird üçün oyunçağa çevrilər. Məşhur bir aforizmdə deyildiyi kimi yaxşı doldurulmuş başa nisbətən yaxşı qurulmuş baş daha yaxşıdır. Bu o deməkdir ki, tədris materialını öyrətməklə yanaşı şagirdləri müstəqil fikir yürütməyə yaradıcılıq imkanlarını inkişaf etdirməyə çalışmalıyıq. Çox təəssüf ki, dərslərdə yalnız dərslikdəki materialları, faktları sadalamaqla öz işini bitmiş hesab edən müəllimlərimiz də vardır. Orta məktəbdə tədris olunan fənlərin əksəriyyətində müasir həyatla əlaqələndirmək üçün istənilən qədər material var. Bunlardan istifadə etmək üçün isə müəllim əlavə mənbələrə müraciət etməlidir. Müəllimin nüfuzuna, başqa amillərlə yanaşı el - oba içərisində özünü necə aparması, xalqının taleyinə biganə qalmaması, haqqın tərəfində durması da az təsir etmir. Müəllim təlim və tərbiyənin mövcud mərhələdə məqsəd və vəzifələrini dəqiq müəyyənləşdirməli, onlara nail olmalı, mərhələnin sonunda qarşıya qoyduğu məqsəd və vəzifələrin necə yerinə yetirildiyini tapşırıq vasitəsilə yoxlamalı, müsbət və mənfi cəhətləri müəyyənləşdirməlidir.O, müsbət cəhətləri möhkəmləndirməli, zəif cəhətləri isə aradan qaldırmaq üçün müəyyən perspektiv tədbirlər hazırlamalıdır. Müəllim keçmiş nəsillərin ən yaxşə həyati təcrübəsini və özünün əldə etdiyi elmi nailiyyətləri yeni gənc nəslə verir və bu nəsli gələcəyin qurucuları ruhunda tərbiyə edir. Cəmiyyətimizdə müəllim elə bir mötəbər, elə aqil, elə kamil adamdır ki, bizim hər birimiz özümüzün ən əziz və ən sevimli övladlarımızın tərbiyyə və təhsilini onlara etibar edirik. Bizim müəllimlərimiz sözün əsil mənasında insan qəlbinin mühəndisi rolunu ifa edirlər. Müəllim məktəblilərlə qaynayıb – qarışır, onların bütün coşqun fəaliyyətinin mərkəzində durur, onlara bu mərkəzdən düzgün istiqamət verir. Müəllim necədirsə, onun şagirdi də elədir,yəni müəllim hansı biliyə, hansı tərbiyəyə malikdirsə onun şagirdləri də, əsasən elə biliyə və tərbiyəyə yiyələnmiş olurlar. Dözümlülik, səbrlilik və təmkinlilik müəllimin mühüm keyfiyyətlərindən biridir. Müəllim daim gözlənilməz situasiyalarla qarşılaşmalı olur. O, hər bir situasiyaya yüksək gərginlik və emosionallıqla cavab verməli olsa uzun müddət səmərəli fəaliyyət güstərə bilməz. Təlim situasiyaları nə qədər rəngarəng və təzadlı olsa da, müəllim səbrli və təmkinli hərəkət etməlidir. Təmkinli müəllim baş vermiş vəziyyəti düzgün başa düşür və onun çıxış yolunu dəqiq müəyyinləşdirir. Təmkinlə hərəkət edən müəllim bütün situasiyalarda dözümlü olmağa, hər şeyi səbrlə ölçüb – biçməyə çalışır və nəticədə səbirli və təmkinli müəllim kimi hərəkət etməyə çalışır. Deməli, pedaqoji prosesdə müəllimin fəaliyyətini düzgün idarə etməsi böyük rol oynayır. Öz imkanlarına inamı, peşəsini sevməsi, metodik ustalığı və zəngin pedaqoji təcrübəsi müəllimin psixi vəziyyətini idarə etməsi prosesinin formalaşmasına müsbət təsir göstərir. Müəllimlik sənəti ,,qəhrəmanlıq” sənətidir. Valideynlərin minnətdarlığı, şagirdlərin məhəbbəti isə ən böyük mükafatdır. “Müəllim nə qədər ki, oxuyur, öyrənir, o yaşayır, o oxumağı dayandırdıqda ondakı müəllimlik ölür”. Müəllimlik – yüksək insani istedad tələb edir. Təbiidir ki, bu keyfiyyət hər kəsdə olmur. Ardıcıl özünənəzarət, əhvalı kökdə saxlamaq, şagirdə, tələbəyə münasibət ayrıca nəzərdə tutulur. Peşə fəaliyyətinin əsas uğuru müəllimin peşəkarlıq və səriştəlilik səviyyəsinin yüksəkliyi ilə xarakterizə olunur. Səriştəlilik, uğura aparan əsas yolun başlanğıcıdır. Müəllim səriştəli olduqda onun pedaqoji fəaliyyəti bütün istiqamətlərdə özünəməxsus məzmun və xarakter alır, seçilir, fərqlənir. «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 83 Pedaqoji təcrübədə zəif oxuyan yeniyetmənin bir sıra mənəvi normalara (dəqiq,mehriban,ədalətli, çalışqan və s.) əməl etməkdə nümunəvi olması halları az deyildir. Müəllim bu keyfiyyəti mütləq nəzərə almalı, şagirdi yaxşı işlər görməyə həvəsləndirməlidir. Müəllim şagirdlərdə bizim ideallarımızın həqiqiliyinə inam tərbiyə etməli gənclərə onları qaldırılması üçün çalışmaq bacarığı aşılamalıdır. Müəllim və şagirdin söhbətində qadağan edilmiş mövzu olmamalıdır. Əlbəttə, müəllim şagirdin istənilən sualına inandırıcı cavablar vermək və ya ona cavab tapmaqda kömək etməkdə kifayət qədər əhatəli biliyə malik olmalıdır. Müəllim məsuliyyəti çoxcəhətlidir. Müəllim böyüməkdə olan şəxsiyyətin fəallağının, ideya inanımın onun şüur və davranışında formalaşdırılması işinə kömək göstərməkdə fövqəlada dərəcədə vacib pedaqoji vəzifələr daşıyır. Bu vəzifələr yalnız müəllimin öz şagirdlərinin nüfuzunu qazandığı zaman yerinə yetirilə bilər. Onsuz bu vəzifələrin yerinə yetirilməsi mümkün deyil. Bura həm də əxlaq sahəsindəki müəyyən biliklər daxildir. Bunlarsız nüfüz qazanmaq olduqca çətindir.( Bəzi istedadlı müəllimlər bu bilikləri bir növ instinktiv şəkildə əldə edirlər) Cəsarətlə demək olar ki, əgər müəllim nüfuzludursa, onun bəzi adamlara təsiri ömürlük qalır. Buna görə də müəllimin özünə nəzarət etməsi vacibdir. O hiss etməlidir ki, onun davrvnış və hərəkətləri dünyada heç bir adamın məruz qalmadığı güclü nəzarət altındadır. Amma müəllimin bu nəzarəti yalnız tapşırılan iş üçün yüksək mənəvi məsuliyyət daşıdığı zaman hiss edə bilər. Əks təqdirdə, bu nəzarət az hiss edilər. Bu da o deməkdir ki, müəllim nəinki özü özünü tərbiyə işindən kənar edəcək, həm də təkcə şagirdlərinə yox, bütün cəmiyyətə böyük ziyan gətirəcək. Müəllimin rolu, dünyagörüşü, hər şeydən əvvəl, hər bir insanın niyyətlərinin qiymətləndirildiyi meyarlarla qiymətləndirilir. Müəllim hazırlığı işində, hər şeydən əvvəl, kəmiyyət dalınca qaçmaqdan imtina etmək lazımdır. Cəmiyyətin qarşıya qoyduğu tələblərə cavab verə bilən həqiqi müəllim tərbiyəsinin yalnız bir yolu var: ,,tələbə skamyasında oturandan ta uzun illərin iş təcrübəsinə malik müəllim olana qədər müəllim hazırlığının bütün mərhələlərində hər bir şəxsə fərdi yanaşmaq”. Kamenski yazırdı: ,,Şagirdlərin daşıdığı mənəvi inkişafının valideynləri olan” müəllimlər çox şey bilməli və bacarmalıdırlar: təlim və tərbiyə sənətinə yiyələnməyi, nə vaxt və hansı şəraitdə tələbkarlıq göstərməyi, uşaqlarda biliklərə və təhsilə qızğın maraq oyatmaq və s. Bütün bunlar isə yalnız müəllimin şagirdləri öz atalıq qayğısı, hərəkətləri və sözləri ilə cəlb etdiyi zaman ola bilər. Müəllimin şagirdlərlə mənəvi əlaqələri özünəməxsusluğu ilə zəngin, vaxt etibarı ilə uzunmüddətlidir və bu onun yaradıcı iş bacarığında, dünyagörüşündə və kamil şəxsiyyətə aid bütün keyfiyyyətlərində də öz əksini tapır. Müəllimlik – yüksək insani istedad tələb edir. Təbiidir ki, bu keyfiyyət hər kəsdə olmur. Ardıcıl özünənəzarət, əhvalı kökdə saxlamaq, şagirdə, tələbəyə münasibət ayrıca nəzərdə tutulur. Bu bir həqiqətdir ki, şagirdlərə, təlimə məsuliyyətli münasibətin yaradılmasında müəllimin rolu böyükdür. Təlim prosesində yalnız şagirdin müəllimlə görüşü yox, bir də onların bir-birini anlaması əsas şərtdir. Müəllimlə şagirdin açıq ürəklə danışması, sərbəst fikir mübadiləsi çox şey öyrədir» Dərin bilik, mədənilik, mənəviyyat nümunəsi olan müəllimin nüfuzu da yüksək olur. Nüfuzu qazanmaq çətin, itirmək isə asandır: yersiz hərəkət, ədalətsiz münasibət, ehtiyatsız söz müəllimi nüfuzdan sala bilər. Buna görə də, hər bir müəllim sözlərinə və hərəkətlərinə məsuliyyətlə yanaşmalı, öz nüfuzunun keşiyində durmalıdır. Müəllim gənc nəslin mənəvi estetik tərbiyəsinin inkişaf etməsində böyük rol oynamalı, həm özünün, həm də şagirdlərin milli əxlaq, ictimai davranış, pedaqoji-etik normalarına riayət etməlidir. Bütün bu qaydalara layiqincə əməl edən müasir müəllim öz üzərində işləyərək kollektivin hörmətini qazanır və yüksəlir. Müasir müəllim şəxsiyyətinin formalaşması, hərtərəfli inkişaf etməsi, gənc nəslə dayaq olması dövrümüzün ən vacib, aktual məsələlərindən biridir. Hər bir Azərbaycan müəllimi öz peşə nüfuzunu möhkəmləndirməyə, şərəf və ləyaqətini uca tutmağa Proceedings of the 12th International Scientific Conference 84 borcludur. Hər an öz üzərində çalışan, yeni fikirlər axtaran, hörmətli müəllimlərimizlə fəxr edir, onlara bu şərəfli məsləkdə uğurlar arzulayırıq. Bu gün müəllim peşəkar olmalı, müxtəlif pedaqoji təsir qabiliyyətinə malik olmalı, mümkün ola biləcək nəticələri proqnozlaşdırmalı, bir sıra təhlil və özünənəzarət üsullarını mənimsəməli, yeni sosial-iqtisadi tərbiyə şəraitini, bazar münasibətlərinin reallığını pedaqoji cəhətdən dərk etməlidir. Qeyd etdklərimi yekunlaşdırıb demək istəyirəm ki, müəllimin mənəvi məsuliyyəti şagirdlərinin bilik, inam, mənəvi dünyası və siması üçün daşıdığı məsuliyyətdir. Lakin müəllim öz fəaliyyətində süni və formal hərəkətlərə yol verirsə, onda nə şagirdlərinin bilikləri, nə inamları, nə də mənəvi simaları mükəmməl,sözün həqiqi mənasında zəngin ola bilər. «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 85 Economic Sciences Роль качественного человеческого капитала в развитии делового туризма в Казахстане Темирбеков А.Г. докторант ОП «Инновационный менеджмент» ЕНУ им. Л.Н.Гумилева Бердибекова А.Ш. главный менеджер АО «НК «Kazakh Tourism» Майдырова А.Б. д.э.н., профессор Кафедры «Экономика и предпринимательство» ЕНУ им.Л.Н.Гумилева Аннотация Современные тенденции глобализации и экономического роста предъявляют высокие требования к качеству человеческого капитала. Особенно это актуально в контексте развития делового туризма, который становится драйвером экономической активности, инвестиционной привлекательности и международного сотрудничества. Казахстан, обладая выгодным географическим положением и политической стабильностью, стремится развивать MICE-сектор. В данной статье анализируется взаимосвязь между уровнем человеческого капитала и показателями делового туризма в стране на основе статистических данных за 2020–2024 гг. Введение Деловой туризм (MICE: Meetings, Incentives, Conferences, Exhibitions) представляет собой важную составляющую современной экономики, способствующую не только развитию гостиничного и транспортного секторов, но и стимулированию международной деловой активности. В этой связи формирование высококвалифицированного кадрового потенциала становится необходимым условием конкурентоспособности Казахстана на мировом рынке делового туризма. По данным официальной статистики, с 2020 по 2024 годы наблюдается устойчивый рост числа иностранных туристов, особенно в сегменте бизнеспоездок. Proceedings of the 12th International Scientific Conference 86 Рост турпотока сопровождается увеличением затрат на деловой туризм, что свидетельствует о росте доверия к Казахстану как к деловой площадке: «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 87 Дополнительно стоит отметить рост числа проводимых международных мероприятий, который вырос почти в 3 раза за пять лет: Роль человеческого капитала Индекс человеческого капитала (условная метрика, учитывающая уровень образования, навыков и цифровой грамотности) также демонстрирует положительную динамику: Ключевыми аспектами, влияющими на развитие делового туризма, являются:  Профессиональное образование в сфере туризма и менеджмента мероприятий;  Знание иностранных языков и культура сервиса;  Навыки цифровой трансформации (работа с CRM, онлайн-платформами, аналитикой). Несмотря на положительные тренды, Казахстан сталкивается с рядом вызовов:  Недостаток кадров с опытом международной работы;  Ограниченность инфраструктуры для проведения крупных мероприятий;  Необходимость более активного продвижения на глобальном рынке MICE. В результате исследования разработаны следующие рекомендуемые меры:  Усиление взаимодействия вузов с индустрией туризма;  Создание центров повышения квалификации с международными программами; Proceedings of the 12th International Scientific Conference 88  Поддержка стартапов в сфере туристических технологий. Хочется отметить, что человеческий капитал выступает фундаментом устойчивого развития делового туризма. Его качественное развитие напрямую влияет на имидж страны, объем инвестиций и экономическое благополучие. Казахстан обладает необходимым потенциалом, и дальнейшие усилия по развитию кадрового ресурса помогут ему закрепиться на международной арене как привлекательной MICE-локации. «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 89 International Experience in Dental Business Marketing Tamar Orjonikidze Doctoral Student, Doctoral Program in Business Administration, Caucasus International University, Faculty of Business and Technology Abstract The paper “International Experience in Dental Business Marketing” examines marketing strategies used to enhance the attractiveness and expand the scope of dental services across different countries. It explores approaches for improving these strategies and identifies effective methods for integrating marketing practices into specialized dental institutions. The study reviews scientific literature by foreign researchers, enabling a comparative analysis of marketing strategies employed in similar and related sectors internationally. The research methods and methodological approaches discussed in international academic sources provide a foundation for selecting and applying suitable research methods for this study. Keywords: Marketing, International Experience, Dentistry Revised Introduction Introduction Relevance of the Topic The issue of the adequacy and quality of dental services is observed with varying degrees of severity across economically developed and developing countries. The accessibility of dental clinics is one of the key indicators of service quality, and this remains a global challenge regardless of a country’s level of economic development. Ensuring greater accessibility for patients is a major priority for managers of dental clinics, particularly in the context of expanding dental markets and intensifying competition. The development and practical implementation of effective marketing strategies is widely recognized as one of the most reliable approaches to addressing this challenge. This consideration served as the basis for formulating the purpose of the present study. Purpose of the Study The purpose of this work is to examine the fundamental principles of marketing in the dental business through a review and analysis of international experience. Subject of the Research The subject of the research is the practical experience of successful dental businesses around the world. Research Method This study employs a secondary research method, which involves analyzing existing information and drawing conclusions based on previously published data. Discussion and Results Branding and promotion play a significant role in the dental market. The development of a brand for a private dental organization and the promotion of its services is a relevant issue for owners and managers of such medical institutions worldwide, regardless of whether they operate in economically strong or weak countries. The primary source of revenue for a dental clinic is the payment received from patients for services. In most countries, the supply of commercial dental services is extensive, and patients have well-defined expectations. In this context, researchers highlight that one of the key factors for economic success is ensuring brand recognition. Proceedings of the 12th International Scientific Conference 96 Internet World Stats. (2022). Internet usage in the Middle East (Bahrain). Lancaster, PA: Internet World Stats. https://www.internetworldstats.com/stats5.htm Kahale, L. A., Elkhoury, R., El Mikati, I., et al. (2021). PRISMA flow diagrams for living systematic reviews: A methodological survey and a proposal. F1000Research, 10, 1–11. https://pubmed.ncbi.nlm.nih.gov/35136567/ Lee, S.-Y., Kwak, M.-G., Kim, M.-J., et al. (2021). Relevance between marketing route of social media and consumer age group for choosing dental clinics. Journal of Dental Hygiene Science, 21, 260–266. https://www.jkdhs.org/journal/view.html?doi=10.17135/jdhs.2021.21.4.260 Lira, A., & Magalhaes, B. (2018). Digital marketing in dentistry and ethical implications. Brazilian Dental Science, 21, 237–246. https://bds.ict.unesp.br/index.php/cob/article/view/1524 Mathur, D., Babu, J., Joseph, M., et al. (2019). Efficiency of digital marketing & medical health care tourism: Double ended swords. Saudi Journal of Medical and Pharmaceutical Sciences, 5, 552– 557. http://jurnal.pdgi.or.id/index.php/jida/article/view/1041 Marina, D., & Maria, A. (2020). Branding in dentistry: A historical and modern approach to a new trend. GSC Advanced Research and Reviews, 3, 51–68. https://www.researchgate.net/publication/342282447 Melkers, J., Hicks, D., Rosenblum, S., Isett, K. R., & Elliott, J. (2017). Dental blogs, podcasts, and associated social media: Descriptive mapping and analysis. Journal of Medical Internet Research, 19(7), e269. https://doi.org/10.2196/jmir.7868 Modha, B. (2023). Exploring customer journeys in the context of dentistry: A case study. Dentistry Journal (Basel), 11, 1–18. https://pubmed.ncbi.nlm.nih.gov/36975572/ Nur, H., Nor, F., Muhammad, S., et al. (2020). The pattern of social media marketing by dentist in Malaysia. Malaysian Dental Journal, 1, 24–42. https://www.researchgate.net/publication/347992774 Parmar, N., Eisingerich, A. B., & Dong, L. (2018). Connecting with your dentist on Facebook: Patients’ and dentists’ attitudes towards social media usage in dentistry. Journal of Medical Internet Research, 20, 1–12. https://pubmed.ncbi.nlm.nih.gov/29959108/ Salim, N. A., Jubair, F., Hassona, Y. M., Izriqi, S., & Al-Fuqaha’a, D. (2021). Esthetic dentistry on Twitter: Benefits and dangers. International Journal of Dentistry. https://pubmed.ncbi.nlm.nih.gov/34925512/ Sivaramakrishnan, G., Abdulameer, F., Faisal, F., et al. (2023). Use of social media to view and post dentistry-related information in Bahrain: A cross-sectional study. Healthcare Informatics Research, 29, 31–39. https://pubmed.ncbi.nlm.nih.gov/36792099/ Sivrikaya, E. C., Yilmaz, O., & Sivrikaya, P. (2021). Dentist-patient communication on dental anxiety using social media: A randomized controlled trial. Scandinavian Journal of Psychology, 62(6), 780– 786. https://doi.org/10.1111/sjop.12769 Uma, E., Nieminen, P., Mani, S. A., John, J., Haapanen, E., Laitala, M. L., et al. (2021). Social media usage among dental undergraduate students: A comparative study. Healthcare (Basel), 9(11), 1408. https://doi.org/10.3390/healthcare9111408 Vukusic Rukavina, T., Viskic, J., Machala Poplasen, L., Relic, D., Marelic, M., Jokic, D., et al. (2021). Dangers and benefits of social media on e-professionalism of health care professionals: Scoping review. Journal of Medical Internet Research, 23(11), e25770. https://doi.org/10.2196/25770 «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 97 Методологические подходы к оценке эффективности интеграции бизнеса: сравнительный анализ моделей А. Цой Almaty Management University, г. Алматы Н. Никифорова Almaty Management University, г. Алматы Аннотация. Эффективность бизнес-интеграции – ключевой фактор успешной реализации стратегий слияния, поглощения и партнерства. В условиях динамичной рыночной среды традиционные методы оценки часто оказываются недостаточными для всестороннего анализа интеграционных процессов. Цель данной статьи – определить наиболее целесообразные методологические подходы к оценке эффективности интеграции бизнеса и сформировать практические рекомендации по их применению с учетом типа интеграции, уровня риска, отрасли и синергетического потенциала. В работе систематизированы существующие модели оценки, включая финансово-экономические, сбалансированные системы показателей, методы оценки синергетического эффекта, а также нефинансовые подходы, учитывающие культурные, управленческие и цифровые аспекты интеграции. Особое внимание уделяется применимости различных моделей в зависимости от типа интеграции и целей бизнеса. На основе анализа выделены преимущества и ограничения каждого метода, а также предложены рекомендации по их практическому применению. Полученные результаты имеют теоретическое и прикладное значение для исследователей и практиков в области корпоративного управления, стратегического менеджмента и оценки инвестиций. Ключевые слова: интеграция бизнеса, оценка эффективности, методологические подходы, синергия, модели DCF и EVA, стратегический менеджмент, инвестиционные проекты, слияния и поглощения, Balanced Scorecard, нефинансовые показатели. Аңдатпа. Бизнес-интеграцияның тиімділігі – бірігу, жұтылу және әріптестік стратегияларын табысты жүзеге асырудың негізгі факторы болып табылады. Нарықтық ортаның тұрақсыздығы жағдайында интеграциялық процестерді жан-жақты талдауға дәстүрлі бағалау әдістері жиі жеткіліксіз болып жатады. Зерттеудің мақсаты – бизнес интеграциясының тиімділігін бағалауға арналған ең орынды әдістемелік тәсілдерді айқындау және оларды интеграция түрі, тәуекел деңгейі, сала ерекшелігі мен синергетикалық әлеуетін ескере отырып қолдануға қатысты практикалық ұсынымдар әзірлеу. Зерттеу барысында қазіргі қолданыстағы бағалау модельдері жүйеленіп, олардың қатарына қаржылық-экономикалық әдістер, теңгерімделген көрсеткіштер жүйесі, синергетикалық әсерді бағалау әдістері, мәдени, басқарушылық және цифрлық аспектілерді ескеретін қаржылық емес тәсілдер енгізілді. Әрбір әдістің интеграция түріне және бизнестің мақсаттарына байланысты қолдану ерекшеліктері көрсетілді. Әдістердің артықшылықтары мен шектеулері талданып, оларды практикалық тұрғыда қолдану бойынша ұсынымдар берілді. Алынған нәтижелер корпоративтік басқару, стратегиялық менеджмент және инвестицияларды бағалау салаларындағы зерттеушілер мен тәжірибешілер үшін теориялық және қолданбалы маңызға ие. Proceedings of the 12th International Scientific Conference 98 Кілт сөздер: бизнес интеграциясы, тиімділікті бағалау, әдістемелік тәсілдер, синергия әсері, DCF және EVA модельдері, стратегиялық менеджмент, инвестициялық жобалар, бірігу мен жұтылу, Balanced Scorecard, қаржылық емес көрсеткіштер. Annotation. The effectiveness of business integration is a key factor in the successful implementation of merger, acquisition, and partnership strategies. In a dynamic market environment, traditional evaluation methods often fall short of providing a comprehensive analysis of integration processes. The objective of the study is to identify the most appropriate methodological approaches for evaluating the effectiveness of business integration and to develop practical recommendations for their application, taking into account the type of integration, risk level, industry specifics, and synergistic potential. The study systematizes existing evaluation models, including financial and economic methods, balanced scorecard systems, methods for assessing synergy effects, as well as non-financial approaches that consider cultural, managerial, and digital aspects of integration. Special attention is paid to the applicability of different models depending on the type of integration and business objectives. The analysis highlights the advantages and limitations of each method and provides recommendations for their practical application. The results have both theoretical and applied significance for researchers and practitioners in corporate governance, strategic management, and investment evaluation. Keywords: business integration, effectiveness assessment, methodological approaches, synergy, DCF and EVA models, strategic management, investment projects, mergers and acquisitions, Balanced Scorecard, non-financial indicators. 1. Введение Современные процессы глобализации, цифровизации и усиливающейся конкуренции на мировом и региональном рынках побуждают компании к объединению ресурсов и созданию устойчивых стратегических альянсов. Интеграционные процессы в бизнесе, будь то слияния, поглощения или иные формы сотрудничества, рассматриваются как ключевые инструменты повышения конкурентоспособности и устойчивости организаций. Однако без четкой и обоснованной методологии оценки эффективности таких проектов существует риск недостижения стратегических целей и потерь ресурсов. Это делает проблему оценки эффективности интеграции бизнеса особенно актуальной как для теоретиков, так и для практиков в области управления. Целью данной статьи является определение наиболее целесообразных методологических подходов к оценке эффективности интеграции бизнеса и разработка практических рекомендаций по их применению с учетом типа интеграции, уровня риска, отрасли и синергетического потенциала. Для достижения цели были поставлены следующие задачи:  систематизировать существующие модели оценки эффективности;  определить ключевые критерии сравнительного анализа;  выявить преимущества и ограничения применяемых подходов;  разработать практические рекомендации по выбору оптимального метода в зависимости от типа интеграции. Гипотеза: Предполагается, что не существует универсального методологического подхода, одинаково эффективного для оценки всех типов бизнес-интеграции. Вместе с тем, комбинирование финансовых и нефинансовых моделей, адаптированных к специфике интеграционного проекта, позволяет более точно и объективно оценить его эффективность. «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 99 2. Литературный обзор Методологические подходы к оценке эффективности интеграции бизнеса на протяжении последних десятилетий эволюционировали от монодисциплинарных финансовых моделей к сложным мультифакторным и гибридным конструкциям. Этому способствовало не только усложнение самих интеграционных процессов, но и рост требований к стратегической обоснованности, прозрачности и устойчивости сделок. Современные исследователи всё чаще рассматривают интеграцию как многоуровневое явление, охватывающее не только экономические, но и институциональные, поведенческие, культурные и цифровые аспекты. Традиционно оценка эффективности интеграции основывалась на методах корпоративных финансов. В первую очередь, на модели дисконтированных денежных потоков (DCF), позволяющей вычислить прирост стоимости объединённой компании. В работе Родионова И. и Михальчука В. обосновывается применимость DCF в условиях развивающихся рынков, где синергия оценивается как разность между интегральной стоимостью объединённой структуры и суммой стоимостей отдельных компаний до сделки. Однако авторы подчёркивают уязвимость метода в условиях институциональной нестабильности и ограниченного доступа к качественным данным [1]. Классические модели, подобные EVA, ROI и TSR, активно используются в западной практике, однако подвергаются обоснованной критике за чрезмерную сосредоточенность на акционерной стоимости как единственном индикаторе успешности интеграции. В отличие от этого, исследование Вашакмадзе Т., Мартиросян Э. акцентирует внимание на необходимости включения в модель оценки нефинансовых параметров, таких как темпы операционного сближения, коэффициент текучести ключевого персонала и индекс синергетической реализации, отражающий достижение качественных целей интеграции. Авторы подчёркивают, что такая многомерная перспектива позволяет адекватнее отразить реальную эффективность постинтеграционного периода, особенно в контексте долгосрочных стратегических целей [2]. Одним из наиболее влиятельных поведенческих направлений остаётся модель «стратегического соответствия», предложенная П.К. Хаспеслагом и Д.Б. Джемисоном ещё в 1991 году и впоследствии адаптированная современными авторами. Особое внимание к культурным и институциональным аспектам интеграции прослеживается в работах Картрайт С. и Купера К., где эмпирически доказано, что несогласованность корпоративных культур часто нивелирует ожидаемые финансовые выгоды [3]. Федорову Е.А. и Изотову Е.И. демонстрируют потенциал применения метода кумулятивной избыточной доходности для оценки эффективности сделок в энергетическом секторе. Данный подход позволяет учитывать волатильность отрасли и формировать более объективную оценку прироста доходности по сравнению с бенчмарком [4]. Се Юн С. и Сюй Хунбо также подчёркивают необходимость включения стратегических и нефинансовых параметров в модели оценки, особенно в случае вертикальной интеграции [5]. Значительный вклад в развитие регионально адаптированных моделей внесли Лалаян Г.Г. и Кремянская Е.В., рассмотревшие интеграционные процессы в агропромышленном комплексе. Они выдвигают идею применения трансакционного подхода в связке с моделью кластерной эффективности Портера, что позволяет оценивать не только финансовые, но и логистические, инфраструктурные и управленческие синергии [6]. В научной литературе вопросам комплексной оценки эффективности интеграционных процессов также уделяется значительное внимание. Среди первых работ, направленных на преодоление ограниченности исключительно финансовых подходов, выделяется исследование Фихтнера О.А., где подчёркивается значимость организационно-структурных Proceedings of the 12th International Scientific Conference 100 изменений, включая усиление рыночных позиций, управляемость и корпоративную культуру [17]. Современные формы интеграции как кластерные объединения были предметом анализа в работах Николаевой М.А. и Махотаевой М.Ю., где акцент сделан на способности к совместному созданию инновационной экосистемы [8]. Белоусова В.О. и Кожевина О.В. провели типологизацию подходов и предложили комбинированную систему выбора модели оценки на разных этапах сделки [9]. Отдельный интерес представляет работа Ефремова В.С. и Владимировой И.Г., в которой формализована мультифакторная модель, учитывающая кросс-культурные и институциональные различия при международной интеграции [10]. Современные интеграционные формы всё чаще реализуются в формате сетевых структур, требующих особых критериев оценки. Авторы предлагает ввести понятие «качества межфирменной связи» и применять методы анализа сетей для оценки устойчивости и результативности таких объединений. Это особенно актуально в условиях цифровизации и платформенной экономики, где интеграция может не носить формально юридического характера, но иметь значительный управленческий эффект. Наиболее заметной тенденцией последних лет становится переход от статических моделей к динамическим, способным учитывать временные лаги, фазность интеграционного процесса и его обратимость. Всё чаще интеграция трактуется не как одноразовое действие, а как многоступенчатый процесс, требующий системной оценки в среднеи долгосрочной перспективе. Возрастает интерес к моделям, основанным на теории реальных опционов, машинном обучении и поведенческой экономике. Cовременное состояние научной мысли по проблеме оценки эффективности интеграции бизнеса характеризуется высокой степенью междисциплинарности, теоретической фрагментацией и региональной дифференциацией. В литературе выстраивается единый тренд в сторону гибкости, адаптивности и ориентации на долгосрочную ценность, что определяет направление дальнейшего методологического поиска. 3. Методы Настоящее исследование основано на применении как качественных, так и количественных методов анализа, направленных на систематизацию и сравнительное сопоставление существующих методологических подходов к оценке эффективности интеграции бизнеса. Методологическая база строится на междисциплинарном синтезе концепций стратегического менеджмента, корпоративных финансов, институциональной теории и организационной трансформации, моделей стратегического соответствия. На первом этапе был проведён углублённый анализ современных теоретических и прикладных источников за 2020-2025 гг., включая рецензируемые научные статьи, аналитические отчёты международных консалтинговых компаний, кейс-исследования, а также диссертационные исследования. Такая база позволила сформировать репрезентативный спектр моделей, охватывающих различные классы интеграции, что стало основой для последующего сравнения. Применялся метод сравнительного анализа, позволяющий соотнести модели по единому набору универсальных критериев: применимость к типу сделки, учёт финансовых и нефинансовых параметров, уровень требуемых входных данных, адаптивность к отраслевым и организационным условиям, а также сложность внедрения. Особое внимание уделено способности моделей к долгосрочному прогнозированию синергетического эффекта и оценке стратегической устойчивости интеграции. С учётом комплексности поставленных задач, в исследовании использованы элементы когнитивного картирования, основанные на модели стратегического соответствия, а также балльные и мультикритериальные подходы, применявшиеся в «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 101 эмпирических работах отечественных и зарубежных исследователей. Это позволило оценить не только эффективность конкретных моделей, но и выявить зоны их применимости в зависимости от масштаба сделки, корпоративного контекста и институциональной среды. Результаты анализа представлены в виде матрицы применимости моделей, содержащей качественную и количественную оценку каждой модели по заданным критериям. В работе обобщены экспертные оценки и результаты эмпирических кейсов, что позволило перейти от теоретического осмысления к выработке практических рекомендаций. Методологическая конструкция статьи сочетает в себе системный анализ, критическую оценку подходов и их прикладную верификацию, обеспечивая надёжную основу для последующего синтеза выводов и разработки обоснованных рекомендаций по выбору оптимального подхода к оценке эффективности бизнес-интеграции. 4. Результаты На основе системного сравнительного анализа моделей оценки эффективности интеграции бизнеса были получены комплексные результаты, позволяющие выявить как методологические различия между подходами, так и области их применимости. Исследование включало анализ как классических финансовых методов (DCF, EVA), так и поведенческих, стратегических и гибридных моделей, представленных в современных академических и прикладных источниках. Оценка проводилась по пяти ключевым критериям: полнота охвата финансовых и нефинансовых факторов, гибкость адаптации модели к контексту, точность долгосрочного прогнозирования, а также общая применимость в различных отраслях. Балльная шкала (от 1 до 5) формировалась на основе интерпретации подходов, описанных в работах Родионова И. и Михальчука В., Федоровой Е.А., а также ряда международных публикаций по стратегическому соответствию и мультикритериальной оценке. Proceedings of the 12th International Scientific Conference 102 Таблица 1. Сравнительный анализ моделей оценки эффективности интеграции бизнеса № Модель Финансовы е показатели (1–5) Нефинансовы е факторы (1–5) Гибкость адаптаци и (1–5) Прогнозна я точность (1–5) Обща я оценк а (из 25) 1 DCF (дисконтированные денежные потоки) 5 1 3 5 14 2 Модель EVA (экономическая добавленная стоимость) 5 2 3 4 14 3 Мультикритериальн ый анализ (MCDM) 3 5 4 3 15 4 Модель стратегического соответствия (Strategic Fit) 2 5 5 4 16 5 Интеграционная матрица Хаспеслага– Джемисона 2 4 4 3 13 6 Метод кумулятивной избыточной доходности 4 2 2 4 12 В обобщающей таблице 1 представлены интегральные оценки моделей по пяти критериям, на основании которых формируются дальнейшие методические рекомендации. Совокупный анализ указывает на то, что наиболее эффективными в современных условиях являются модели, способные интегрировать количественные и качественные параметры, адаптироваться к отраслевым и региональным условиям и учитывать долгосрочный стратегический горизонт интеграции. Как показывает таблица 1, наиболее сбалансированным инструментом оказался подход стратегического соответствия, на основе классической модели Хаспеслага П. К. и Джемисона Д. Б. Эта модель получила высокую оценку за способность интегрировать нефинансовые переменные, включая культурные, цифровые и организационные различия, а также адаптироваться к различным масштабам сделок. Несмотря на умеренные показатели по финансовой точности, именно она оказалась наиболее универсальной по совокупности критериев. Модель мультикритериального анализа (MCDM), также продемонстрировала высокую результативность благодаря своей способности учитывать разнообразные показатели, от стратегических целей до операционных рисков. Однако её применение требует значительных аналитических ресурсов и высокого уровня квалификации исполнителей, что ограничивает её распространённость в бизнес-среде. Финансовые модели, такие как DCF и EVA, по-прежнему демонстрируют высокую точность в условиях полной информации и стабильной макроэкономической среды. Однако их неспособность охватывать поведенческие и культурные параметры интеграции делает их менее релевантными в кросс-культурных сделках или при оценке нематериальной синергии. Эта ограниченность была подчёркнута в работах Вашакмадзе Т., Мартиросян Э., а «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 103 также в отчётах EY, где подчёркивается тренд перехода к многоуровневым моделям оценки интеграции. Интересные результаты продемонстрировала интеграционная матрица Хаспеслага и Джемисона, которая, несмотря на свою концептуальную природу, эффективно классифицирует типы интеграции и определяет соответствующую стратегию оценки. Однако в силу своей абстрактности она требует операционализации через другие прикладные методы. Метод кумулятивной избыточной доходности, используемый Федоровой Е.А. и Изотовой Е.И. в энергетическом секторе, показал умеренные результаты. Его сила, в отраслевой специфичности и способности учитывать волатильность доходности; слабость, в ограниченной применимости за пределами строго регулируемых сегментов. Для наглядного представления зависимости между охватом финансовых и нефинансовых факторов был построен график (рисунок 1). Он визуализирует позиционирование моделей вдоль двух критически важных осей, выявляя два полюса: модели с высокой финансовой точностью, но слабым охватом нефинансовых факторов (DCF, EVA), и наоборот, модели, ориентированные на стратегическую и поведенческую согласованность, но менее точные в финансовом прогнозировании. Рисунок 1. Сравнительная интегральная оценка моделей 5. Выводы и обсуждение Научный анализ выявил, что доминирующие в прошлом универсальные подходы к оценке эффективности интеграции бизнеса сегодня утрачивают значимость в силу роста институциональной сложности, цифровизации и глобализации сделок. Полученные данные подтверждают, что изолированное применение исключительно финансовых моделей, таких как DCF и EVA, хотя и даёт высокую точность при стабильной среде, оказывается недостаточным для комплексной оценки интеграционного потенциала в стратегической перспективе. Эти методы игнорируют социокультурную динамику, организационную адаптацию и институциональные ограничения, что снижает их применимость в транснациональных или мультикультурных сделках. С другой стороны, модели, основанные на стратегическом соответствии, демонстрируют способность учитывать нефинансовые факторы интеграции такие как Proceedings of the 12th International Scientific Conference 104 культура, стиль лидерства, цифровая зрелость. Именно они получили наиболее высокие оценки в сравнительной матрице (Таблица 1). Эти подходы находят подтверждение и в эмпирических исследованиях, где показатели интеграционного успеха напрямую коррелируют с качеством взаимодействия команд и степенью предварительного стратегического согласования. Теоретический вклад работы заключается в верификации гипотезы о необходимости комплексного и контекстуализированного подхода, что согласуется с выводами современных исследователей. Модель стратегического соответствия может рассматриваться как интегративная рамка, сочетающая поведенческие и структурные параметры и способная адаптироваться к типу сделки. С практической точки зрения, результаты исследования показывают, что выбор методологии оценки интеграции должен быть обусловлен отраслевым контекстом, размером компаний и типом сделки (горизонтальная, вертикальная, кластерная). Например, при вертикальной интеграции в сырьевом секторе, где доступна отчётность и цели фокусируются на снижении трансакционных издержек, целесообразно применение классических моделей (DCF, EVA), подтверждённое в работах Федоровой Е.А. В то же время, при интеграции стартапа в структуру крупной корпорации высокотехнологичного сектора (например, цифрового сервиса в банковский холдинг), ключевыми становятся элементы организационного дизайна и совместимость культур, здесь эффективнее использовать поведенческие и стратегические модели, такие как Strategic Fit. Особое внимание также следует уделить мультикритериальному анализу (MCDM), который позволяет учитывать одновременно экономические, социальные и стратегические параметры сделки. Несмотря на высокую сложность внедрения, он предоставляет компании инструментарий для интеграционной диагностики и сценарного анализа, особенно в условиях неопределённости и многопараметричности среды. Результаты обсуждения подтверждают, что современная оценка эффективности интеграции должна носить мультидисциплинарный и гибридный характер. Выбор подхода зависит от контекста, стратегических целей и степени открытости данных. Научный и практический интерес представляет дальнейшее развитие типологий и адаптивных моделей оценки, способных учитывать не только синергетический эффект, но и институциональные и поведенческие риски. Список использованных источников 1. Родионов И., Михальчук В. Оценка синергии в сделках M&A // SSRN Electronic Journal. – 2020. – DOI: 10.2139/ssrn.3064921. – URL: https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3064921 (дата обращения: 01.12.2025). 2. Vashakmadze T., Martirosyan E. Stakeholder management in M&A // SSRN Electronic Journal. – 2020. – DOI: 10.2139/ssrn.2599457. – URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2599457 (дата обращения: 01.12.2025). 3. Cartwright S., Cooper C. Managing M&A integration: Cultural dimensions and HR role // ResearchGate. – 2022. – URL: https://www.researchgate.net/publication/330652907 (дата обращения: 01.12.2025). 4. Федорова Е.А., Изотова Е.И. Оценка слияний в энергетике на основе кумулятивной доходности // Экономика и предпринимательство. – 2021. – № 5. – С. 120–130. – URL: https://cyberleninka.ru/article/n/otsenka-sliyaniy-i-pogloscheniy-v-sektore-energetikirossiyskoy-federatsii-na-osnove-metoda-kumulyativnoy-izbytochnoy-dohodnosti.pdf (дата обращения: 01.12.2025). 5. Юн С., Хунбо С. Современные подходы к стоимости бизнеса в интеграционных процессах // Современные научные исследования и инновации. – 2021. – № 6. – URL: «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 105 https://cyberleninka.ru/article/n/sovremennye-podhody-k-stoimosti-biznesa-v-integratsionnyhprotsessah/pdf (дата обращения: 01.12.2025). 6. Лалаян Г.Г., Кремянская Е.В. 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Proceedings of the 12th International Scientific Conference 112 consumers adapting technologies to their needs, and informal sector innovations in developing economies that rarely generate patents or formal R&D statistics (von Hippel, 2017; Baldwin & von Hippel, 2011). If innovation activity has partially migrated from traditional organizational forms and geographical locations to these alternative modes, then conventional indicators may underestimate aggregate innovation activity while accurately measuring decline within their specific domains (Radjou & Prabhu, 2015). The platform economy and digital transformation more broadly present particular measurement challenges (Kenney & Zysman, 2020). Digital platforms like mobile applications, cloud computing services, and data analytics tools enable innovation by firms and individuals that may generate substantial economic and social value without corresponding to traditional innovation indicators (Gawer, 2021). A small business adopting cloud-based customer relationship management software, artificial intelligence-powered marketing analytics, or automated accounting systems may substantially enhance its productivity and competitive position through innovation adoption, yet this activity appears nowhere in conventional innovation statistics unless the platform providers themselves register patents or publish research (Nambisan et al., 2019). Similarly, individual content creators, software developers, or designers building businesses on platform infrastructures engage in creative and innovative activities that generate income and value but remain invisible to traditional innovation measurement frameworks focused on institutional actors (Boudreau & Lakhani, 2013). The artificial intelligence revolution exemplifies these measurement challenges while simultaneously transforming the substantive nature of innovation processes (Agrawal et al., 2019). AI technologies enable automation of certain cognitive tasks previously requiring human intelligence, potentially augmenting productivity across broad swathes of economic activity (Brynjolfsson & McAfee, 2017). However, measuring AI innovation capacity and AI-driven innovation outcomes presents formidable difficulties (Cockburn et al., 2018). Should AI capacity be measured through published research papers, trained models, computational infrastructure, data availability, algorithmic sophistication, or downstream adoption and economic impact (Furman & Seamans, 2019)? Each measurement approach captures different dimensions and may generate divergent assessments of national AI innovation capabilities (Baruffaldi et al., 2020). Moreover, the global nature of AI development—with research talent, computational resources, training data, and commercial applications distributed across national boundaries—challenges nation-state-focused innovation measurement frameworks (Goldfarb & Trefler, 2018). These technological and organizational changes intersect with broader economic transformations that complicate innovation measurement and assessment (Perez, 2020). The servicification of advanced economies—with services representing growing shares of GDP and employment— implies that innovation increasingly manifests in service process improvements, business model innovations, and customer experience enhancements rather than in tangible product developments that more readily correspond to patent counts or R&D expenditures (Djellal & Gallouj, 2018). Service innovations often involve intangible elements like organizational routines, customer relationship protocols, or digital interface designs that resist quantification and may not generate formal intellectual property (Gallouj & Savona, 2019). Financial services deploying algorithmic trading systems, healthcare providers implementing telemedicine platforms, or educational institutions adopting adaptive learning technologies all engage in substantial innovation activities that conventional metrics may inadequately capture (Rubalcaba et al., 2021). The increasing importance of data as an economic input and innovation enabler further complicates measurement challenges (Jones & Tonetti, 2020). Data assets—including customer information, operational metrics, sensor readings, and behavioral traces—enable machine learning applications, personalized services, operational optimizations, and predictive capabilities that drive innovation and competitive advantage (Goldfarb & Tucker, 2019). However, data assets «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 113 appear inadequately in conventional economic statistics, lack well-established valuation methodologies, and raise conceptual challenges regarding ownership, privacy, and appropriate measurement units (Coyle & Diepeveen, 2021). National innovation frameworks that fail to assess data infrastructure quality, data governance effectiveness, or data analytical capabilities may overlook critical dimensions of contemporary innovation capacity (OECD, 2020). Simultaneously, innovation policy discourse has increasingly emphasized the concept of "innovation ecosystems" rather than linear models of innovation proceeding from basic research through applied research, development, and commercialization (Granstrand & Holgersson, 2020). The ecosystem perspective recognizes that innovation emerges from complex interactions among diverse actors including universities, public research institutions, established firms, startups, venture capitalists, skilled workers, specialized suppliers, and supportive institutions providing legal, financial, and technical services (Autio et al., 2018). Measuring ecosystem vitality requires indicators capturing relationship density, knowledge flows, institutional quality, entrepreneurial culture, and risk capital availability rather than merely aggregate R&D expenditure or patent counts (Stam & van de Ven, 2021). While the GII incorporates some ecosystem-oriented indicators, critics argue that the emphasis remains skewed toward traditional input and output measures that inadequately represent ecosystem dynamics (Brown & Mason, 2017). The geographical dimension of innovation presents additional complexity particularly relevant for cross-national comparison (Feldman & Kogler, 2010). Innovation activities concentrate spatially in urban agglomerations and regional clusters that may exhibit innovation capabilities dramatically different from national averages (Audretsch & Belitski, 2021). Countries with a few highly innovative urban regions and large peripheral areas with minimal innovation activity may receive similar overall GII scores to countries with more geographically distributed innovation capacity, yet these different spatial configurations have distinct implications for inclusive growth and territorial cohesion (Iammarino et al., 2019). National-level innovation indices necessarily obscure these subnational variations, potentially missing critical dimensions of innovation geography that matter for policy effectiveness (Balland et al., 2020). Developing country contexts present specific challenges for innovation measurement frameworks largely designed based on advanced economy experiences (Cozzens & Sutz, 2014). Innovation in developing economies often involves absorption, adaptation, and incremental improvement of technologies developed elsewhere rather than frontier research, yet these activities may be equally important for productivity growth and development progress (Fu et al., 2020). Informal sector innovation—including creative adaptations of existing technologies to resourceconstrained contexts, development of appropriate technologies serving local needs, and indigenous knowledge applications—rarely generates patents or formal R&D statistics but may substantially enhance livelihoods and economic opportunities (Kaplinsky, 2011). Frugal innovation, jugaad innovation, and grassroots innovation concepts attempt to characterize these alternative innovation modes, yet their measurement remains embryonic (Radjou & Prabhu, 2015; Gupta, 2016). Applying innovation measurement frameworks developed for advanced economies to developing country contexts risks systematic underestimation of genuine innovation capabilities and activities (Kraemer-Mbula & Wunsch-Vincent, 2016). The institutional and governance dimensions of innovation capacity receive growing recognition in scholarly literature, yet their measurement remains challenging (Edquist & Johnson, 2021). Innovation system effectiveness depends not only on resource availability but on institutional arrangements governing intellectual property, research funding allocation, university-industry collaboration, entrepreneurship support, procurement policies, regulatory frameworks, and competition policy (Lundvall, 2010). These institutional dimensions involve qualitative characteristics resistant to quantification, political economy considerations, and path-dependent historical legacies that simple cross-sectional indicators may not adequately capture (Nelson, Proceedings of the 12th International Scientific Conference 114 2018). Moreover, optimal institutional configurations may vary across development contexts, technological domains, and political systems, challenging universal benchmarking approaches (Amsden & Chu, 2003). Human capital represents another domain where measurement complexity intersects with critical importance for innovation capacity (Castellacci & Natera, 2016). While educational attainment indicators—such as expected years of schooling, tertiary enrollment rates, or PISA test scores— provide readily quantifiable metrics widely used in composite innovation indices, they may inadequately capture the specific skills, creative capabilities, and entrepreneurial orientations most relevant for innovation (Hanushek & Woessmann, 2020). Educational systems producing high test scores through rote memorization may not cultivate the critical thinking, problemsolving, risk-taking, and collaborative capabilities that drive innovation (Zhao, 2018). Conversely, educational systems emphasizing creativity, experimentation, and interdisciplinary synthesis may generate greater innovation capacity than formal attainment metrics suggest (Wagner, 2012). Measuring these qualitative dimensions of human capital relevant for innovation remains an ongoing challenge (Liu & Steiner-Khamsi, 2020). The gender dimension of innovation presents both equity concerns and measurement challenges (Hunt et al., 2015). Growing evidence suggests that diverse teams—including gender diversity— generate more creative solutions and innovative outcomes than homogeneous groups (DiazGarcia et al., 2013). However, women remain substantially underrepresented in science, technology, engineering, and mathematics fields, entrepreneurship, and innovation leadership positions across most countries (Stoet & Geary, 2018). Innovation indices that fail to incorporate gender dimensions may overlook both inequities that constrain innovation potential and differences in innovation performance attributable to varying success in mobilizing diverse talent (Naldi et al., 2020). The GII includes some gender-related indicators, but critics argue that more comprehensive assessment of gender equity in innovation systems warrants priority attention (UNESCO, 2021). Environmental sustainability considerations introduce fundamental tensions into innovation assessment frameworks (Geels et al., 2017). Traditional innovation metrics emphasize technological advancement, productivity enhancement, and economic growth largely without regard to environmental impacts or resource consumption (Jackson, 2017). However, if innovation activities generate environmental degradation, resource depletion, or climate destabilization, their contribution to genuine societal welfare may be negative despite positive scores on conventional innovation indices (Hickel, 2021). The concept of "eco-innovation" or "green innovation" attempts to characterize innovation explicitly oriented toward environmental sustainability, yet measurement frameworks remain underdeveloped and agreement on appropriate indicators remains elusive (Kemp & Pearson, 2007; Ghisellini & Ulgiati, 2020). More fundamentally, scholars debate whether incremental eco-efficiency improvements suffice or whether genuine sustainability requires transformative system-level innovations that may appear disruptive or even regressive by conventional growth-oriented metrics (Schot & Steinmueller, 2018). The COVID-19 pandemic provided a natural experiment revealing both the importance of innovation capacity for crisis response and significant limitations of existing measurement frameworks (Harris et al., 2020). Countries demonstrating effective pandemic responses often relied on rapid innovation in diagnostic testing, digital contact tracing, vaccine development and manufacturing, and healthcare delivery adaptations (Toner et al., 2021). However, these crisis innovation capabilities corresponded only partially to pre-pandemic GII rankings, with some highly-ranked innovation leaders struggling with pandemic response while some lower-ranked countries demonstrated impressive crisis innovation capacities (Tonne & Martin, 2021). This divergence suggests that existing innovation indices may emphasize dimensions of innovation «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 115 capacity—such as patents, publications, and high-tech manufacturing—that prove less relevant for rapid innovation in response to urgent societal challenges compared to dimensions like institutional adaptability, state capacity, social cohesion, or local production capabilities (Mazzucato & Kattel, 2020). Mission-oriented innovation policy frameworks gaining prominence in recent years present another challenge to traditional innovation measurement approaches (Mazzucato, 2018). Mission-oriented approaches define ambitious societal goals—such as climate neutrality, circular economy transition, or health equity—and organize innovation policies, investments, and partnerships around achieving these missions (Kattel & Mazzucato, 2018). Assessing missionoriented innovation capacity requires indicators capturing directionality toward mission goals, cross-sectoral coordination effectiveness, public-private collaboration quality, and experimentation tolerance rather than merely aggregate innovation inputs or generic outputs (Wanzenböck et al., 2020). Traditional innovation indices structured around universal input and output categories may inadequately assess national capabilities for mission-oriented innovation approaches increasingly central to policy discourse (Larrue, 2021). The political economy of innovation measurement itself warrants critical examination (Godin, 2022). Composite indices like the GII inevitably involve subjective choices regarding indicator selection, data sources, normalization procedures, weighting schemes, and aggregation methods, with each choice potentially privileging certain countries, economic systems, or policy approaches over others (Cherchye et al., 2008). Rankings generate political pressures, influence policy agendas, and affect international perceptions and investment flows, creating incentives for countries to prioritize improvements in measured dimensions regardless of whether they address genuine innovation capacity constraints (Davis et al., 2012). "Gaming" possibilities—where countries optimize performance on measured indicators without corresponding improvement in substantive innovation capabilities—represent a constant concern for composite indicator designers (Ravallion, 2012). Moreover, the institutional actors producing innovation indices— including international organizations, academic institutions, and consulting firms—possess their own organizational interests, ideological orientations, and stakeholder relationships that may influence methodological choices (Muller, 2018). Transparency and reproducibility represent additional concerns in composite indicator methodology (Saisana & Saltelli, 2020). Complex aggregation procedures involving multiple stages of normalization, weighting, and calculation may obscure the sensitivity of final rankings to specific methodological choices, data values, or outlier observations (Paruolo et al., 2018). Robust composite indicators should undergo sensitivity analysis testing how rankings change under alternative reasonable methodological assumptions, yet such analyses remain uncommon in innovation index reporting (Saisana et al., 2005). Similarly, data quality issues—including missing values, estimation procedures, temporal misalignment, and definitional inconsistencies across countries—affect final index values in ways rarely transparent to users (Bandura, 2008). Enhanced transparency regarding data sources, quality assessments, methodological choices, and sensitivity analyses would strengthen the credibility and appropriate use of innovation indices (OECD, 2008). The temporal dimension presents yet another layer of complexity in innovation measurement (Archibugi et al., 2009). Innovation is inherently a dynamic process involving temporal lags between inputs and outputs, path-dependent trajectories, punctuated equilibria, and long-wave patterns that cross-sectional indicators poorly capture (Dosi & Nelson, 2010). Research and development investments may require years or decades to generate commercially viable innovations, while patent counts represent legal claims whose economic value remains uncertain until market validation occurs (Hall et al., 2014). Educational investments shaping human capital for innovation involve even longer time horizons spanning generations (Hanushek & Woessmann, 2015). Cross-sectional innovation indices necessarily provide snapshots of systems better Proceedings of the 12th International Scientific Conference 116 understood through longitudinal analysis, yet tracking changes over time introduces additional challenges when indicator definitions, data sources, or methodologies evolve (Godin, 2011). Benchmarking and ranking dynamics inherent in innovation indices create both opportunities and risks for policy learning (Ladi, 2011). Comparative rankings can stimulate policy reflection, identify successful practices meriting emulation, and generate political momentum for reform in countries lagging perceived peer competitors (Drezner, 2001). However, these same dynamics may encourage uncritical policy transfer without adequate attention to contextual differences, provoke defensive reactions where countries question methodology rather than examining substantive performance, or incentivize narrow focus on improving index position rather than addressing fundamental innovation system challenges (Peck & Theodore, 2015). The optimal role for innovation indices in policy processes—providing useful benchmarking information while avoiding mechanistic ranking obsession—remains contested terrain (Bandola-Gill, 2019). Regional and international innovation policy coordination efforts increasingly reference innovation indices as assessment tools and agenda-setting mechanisms (Borrás & Edquist, 2013). The European Union's Innovation Union Scoreboard, for example, tracks member state innovation performance and informs cohesion policy resource allocation (European Commission, 2021). The African Union's Science, Technology and Innovation Strategy for Africa 2024 emphasizes innovation measurement and benchmarking as tools for accountability and learning (African Union, 2014). ASEAN innovation policy discussions similarly reference innovation indices in priority-setting and strategy development (ASEAN, 2016). These applications amplify the practical significance of innovation measurement methodologies while potentially institutionalizing any systematic biases or limitations inherent in existing frameworks (Mahroum & Al-Saleh, 2013). The research presented in this study emerges from this complex landscape of measurement challenges, economic transformations, conceptual debates, and practical policy applications. By systematically examining the empirical relationships among key innovation and development indicators across diverse national contexts, we aim to generate evidence regarding which components of the GII methodology demonstrate robust reliability across contexts and which may require reconsideration, replacement, or context-specific adaptation. The specific focus on intangible investment dynamics reflects the hypothesis that current innovation measurement frameworks inadequately capture the shift toward knowledge-intensive, intangible-asset-driven innovation processes increasingly characteristic of 21st-century economies (Haskel & Westlake, 2022). Our research design builds upon prior studies examining innovation indicator validity and composite index methodology while extending this literature in several directions (Grupp & Schubert, 2010; Hollanders & Celikel-Esser, 2007). First, we stratify countries by innovation performance level to test whether indicator reliability varies systematically across development contexts—a hypothesis suggested by theoretical literature but rarely examined empirically with comprehensive indicator sets (Zanello et al., 2016). Second, we incorporate recently available data on intangible investment patterns that enable connecting innovation measurement debates to documented structural economic changes (WIPO & LBS, 2025). Third, we examine temporal dynamics by comparing indicator relationships across multiple years (2021-2024), allowing assessment of whether observed patterns reflect stable relationships or transient fluctuations (Archibugi et al., 2009). Fourth, we ground the analysis in practical policy concerns by articulating concrete implications for GII methodology refinement and innovation strategy development. The countries included in our analysis span the global innovation landscape from leading innovation performers like Switzerland, Sweden, and the United States through emerging innovation economies including China, India, and Brazil to developing countries at earlier stages of innovation system development (WIPO, 2024). This diversity enables robust testing of whether innovation measurement frameworks demonstrate universal validity or require adaptation to «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 117 different development contexts. The 13 indicators examined were selected to represent multiple dimensions of innovation capacity including demographic dynamics, human capital, social development, governance quality, political institutions, economic competitiveness, international integration, research capacity, globalization, and health outcomes—dimensions emphasized across innovation systems literature as potentially relevant for innovation performance (Edquist & Johnson, 2021; Lundvall, 2010; Nelson, 2018). Materials and Methods 2.1 Research Design and Conceptual Framework This study employs a quantitative correlational research design to examine the reliability and validity of innovation indicators used in the Global Innovation Index (GII) calculation across different country development contexts. The conceptual framework underlying this investigation posits that innovation measurement indicators demonstrate varying degrees of relevance, predictive power, and mutual correlation depending on the economic development stage, institutional maturity, and structural characteristics of national innovation systems (Zanello et al., 2016; Fu et al., 2020). Consequently, the application of uniform indicator sets and weighting schemes across heterogeneous national contexts may introduce systematic measurement error that compromises the accuracy and policy utility of composite innovation indices (Paruolo et al., 2018; Greco et al., 2019). The research tests this core hypothesis through systematic correlation analysis examining relationships among 13 key innovation and development indicators across 100 countries stratified into performance-based groups according to their GII rankings. The analytical approach enables identification of indicators demonstrating robust reliability—operationalized as consistent correlation patterns with overall innovation performance and with theoretically-related indicators—versus those exhibiting unreliable or context-dependent relationships that may undermine measurement accuracy (Saisana & Saltelli, 2020). By stratifying the country sample into distinct performance groups, the analysis can detect whether indicator reliability varies systematically across development contexts, providing empirical evidence regarding the appropriateness of universal versus context-adapted innovation measurement frameworks (Cozzens & Sutz, 2014; Kraemer-Mbula & Wunsch-Vincent, 2016). The conceptual model informing indicator selection recognizes that innovation capacity emerges from complex interactions among demographic conditions, human capital endowments, social development achievements, governance effectiveness, political institutions, economic competitiveness, international integration, scientific research capabilities, global connectivity, and population health (Edquist & Johnson, 2021; Lundvall, 2010; Nelson, 2018). Each of these dimensions theoretically contributes to enabling or constraining national innovation performance, though their relative importance and specific manifestations may vary across contexts (Amsden & Chu, 2003). The selected indicators represent operationalizations of these theoretical constructs using internationally comparable data sources with broad country coverage, enabling systematic cross-national analysis (OECD, 2008). 2.2 Data Sources and Country Sample 2.2.1 Primary Data Sources The analysis integrates data from multiple authoritative international sources to construct a comprehensive dataset encompassing innovation performance and related development dimensions: Global Innovation Index (GII): The dependent variable and primary classification criterion derives from the Global Innovation Index published annually by the World Intellectual Property Organization in partnership with Cornell University and INSEAD (WIPO, 2021; 2024). The GII scores Proceedings of the 12th International Scientific Conference 118 for 2021 and 2024 provide the basis for country stratification and serve as the reference standard against which other indicators' reliability is assessed. The GII itself comprises 82 indicators aggregated into seven pillars: institutions, human capital and research, infrastructure, market sophistication, business sophistication, knowledge and technology outputs, and creative outputs (Dutta et al., 2023). The index employs a scoring system ranging from 0 to 100, with higher scores indicating stronger innovation performance. Demographic Data: Population growth rate data derives from the United Nations Department of Economic and Social Affairs Population Division database, which provides annual population estimates and growth rates based on national statistical office reporting, census data, and demographic modeling (UN DESA, 2023). The Population Growth Rate Index (PGRI) employed in this analysis represents the annual percentage change in total population, reflecting demographic dynamics that influence labor force growth, market expansion, and dependency ratios relevant for innovation capacity (Bloom et al., 2003). Educational Indicators: Two distinct educational measures are incorporated. The Current Educational Index (EI*) represents mean years of schooling for adults aged 25 years and older, derived from the United Nations Development Programme's Human Development Index database (UNDP, 2023). This indicator reflects accumulated educational capital in the current adult population. The Expected Educational Index (EI**) represents expected years of schooling for children entering the educational system, combining official enrollment ratios and population data to project future human capital accumulation (UNDP, 2023). These complementary indicators capture both present educational endowments and future human capital trajectories. Social Development: The Social Progress Index (SPI), produced by the Social Progress Imperative, provides a comprehensive assessment of societal wellbeing across three dimensions: basic human needs (nutrition, water, sanitation, shelter, personal safety), foundations of wellbeing (access to basic knowledge, information and communications, health and wellness, environmental quality), and opportunity (personal rights, personal freedom and choice, inclusiveness, access to advanced education) (Stern et al., 2020). Unlike GDP-based measures, the SPI focuses exclusively on social and environmental outcomes, making it particularly valuable for assessing development dimensions beyond economic growth that may support or constrain innovation capacity (Porter et al., 2015). Governance Quality: The Worldwide Governance Quality Index (WGQI) derives from the World Bank's Worldwide Governance Indicators project, which aggregates data from multiple sources to assess six dimensions of governance: voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rule of law, and control of corruption (Kaufmann et al., 2010; World Bank, 2023). The composite index provides a comprehensive measure of institutional quality and state capacity relevant for innovation system functioning (Edquist & Johnson, 2021). Political Institutions: The Political and Civil Liberties Index (PCLI) comes from Freedom House's Freedom in the World report, which assesses political rights and civil liberties through expert coding of multiple indicators related to electoral processes, political pluralism, government functioning, freedom of expression, associational rights, rule of law, and personal autonomy (Freedom House, 2023). This index captures the political institutional environment affecting knowledge circulation, critical inquiry, and entrepreneurial initiative (Inglehart & Welzel, 2005). Economic Competitiveness: The Global Competitiveness Index (GCI) published by the International Institute for Management Development provides a comprehensive assessment of national competitiveness based on economic performance, government efficiency, business efficiency, and infrastructure quality (IMD, 2023). This index synthesizes multiple dimensions of economic capability relevant for translating innovation inputs into commercial outcomes (Schwab, 2019). «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 119 Foreign Investment: The Foreign Direct Investment Index (FDII) represents net FDI inflows as a percentage of GDP, combining data from the World Bank and International Monetary Fund balance of payments databases (World Bank, 2023; IMF, 2023). Foreign direct investment serves as both a channel for international technology transfer and an indicator of international investor confidence in national economic prospects (UNCTAD, 2020). Research Activity: The Science-Research Activity Index (SRAI) is compiled from data published by the U.S. National Science Foundation's Science and Engineering Indicators, which tracks publications, citations, and research output across countries (NSF, 2023). This indicator captures scientific research productivity, a core dimension of innovation capacity emphasized across theoretical frameworks (Lundvall, 2010). Globalization: The Country Globalization Index (CGI) produced by the KOF Swiss Economic Institute at ETH Zurich measures economic, social, and political dimensions of globalization through 42 variables covering trade flows, investment stocks, information flows, cultural proximity, and political engagement (Gygli et al., 2019). This comprehensive index captures the degree of international integration that may facilitate knowledge flows and technology diffusion supporting innovation (Dreher, 2006). Health Outcomes: The Healthy Life Expectancy Index (HLEI) derives from World Health Organization data estimating the average number of years that a person can expect to live in good health, taking into account mortality and morbidity (WHO, 2023). Health outcomes reflect both the effectiveness of healthcare systems and broader determinants of population wellbeing that may influence productive capacity and innovation performance (Bloom & Canning, 2008). 2.2.2 Country Sample Selection and Stratification The study analyzes 100 countries selected based on three criteria: (1) inclusion in the GII 2021 and 2024 reports with complete index scores, (2) availability of data for all 13 indicators examined in the analysis for both time periods, and (3) representation of diverse geographic regions and development levels to ensure analytical generalizability (Landman & Carvalho, 2016). Countries with incomplete data across the required indicators were excluded to maintain analytical consistency and enable valid correlation analysis. The 100-country sample encompasses all major economic regions including Europe (37 countries), Asia (26 countries), Americas (18 countries), Middle East and North Africa (12 countries), and SubSaharan Africa (7 countries). Development levels span high-income OECD economies, emerging market economies, and lower-income developing countries, providing variance necessary for examining context-dependent indicator relationships (OECD, 2008). Countries were stratified into two primary groups based on GII rankings: Group 1 (Top 50 Countries): Nations ranked 1-50 in the GII, representing global innovation leaders and strong innovation performers. This group predominantly comprises high-income advanced economies with mature innovation systems, though it also includes several upper-middle-income countries demonstrating strong innovation performance relative to development level. Representative countries include Switzerland, Sweden, United States, United Kingdom, South Korea, Netherlands, Finland, Singapore, Denmark, Germany, France, China, Japan, and Israel among others. Group 2 (Second 50 Countries): Nations ranked 51-100 in the GII, representing moderate innovation performers and emerging innovation systems. This group encompasses greater heterogeneity including upper-middle-income emerging markets, lower-middle-income developing countries, and some high-income economies with relatively lower innovation performance. Representative countries include Iran, Belarus, Georgia, Moldova, Uruguay, Saudi Arabia, Colombia, Qatar, Armenia, Peru, Tunisia, Kuwait, Argentina, and others. This binary stratification enables systematic comparison of indicator reliability patterns between higher-performing and moderate-performing innovation systems. Additional exploratory analysis Proceedings of the 12th International Scientific Conference 120 further subdivides Group 1 to distinguish the top five innovation leaders (Switzerland, Sweden, USA, UK, South Korea) from ranks 6-50, enabling more granular examination of whether indicator relationships vary even within the high-performance category. 2.2.3 Temporal Coverage The analysis incorporates data from two time points—2021 and 2024—enabling assessment of whether observed indicator relationships represent stable patterns or temporal fluctuations (Archibugi et al., 2009). The three-year interval provides sufficient time for meaningful changes in some indicators while remaining short enough that fundamental country characteristics exhibit relative stability, enabling meaningful longitudinal comparison (Landman & Carvalho, 2016). For each indicator, values corresponding to the respective GII publication year were used to maintain temporal alignment and ensure that indicator values reflect conditions contemporaneous with measured innovation performance. 2.3 Variables and Operationalization 2.3.1 Dependent Variable Global Innovation Index (GII): The primary dependent variable is the overall GII score published for each country in 2021 and 2024. This composite index ranges from 0 to 100, with higher scores indicating stronger innovation performance. The GII serves as both the stratification criterion for country grouping and the reference standard against which other indicators' reliability is assessed. An indicator demonstrating strong correlation with GII within a country group is considered more reliable for measuring innovation-relevant dimensions in that context (Saisana & Saltelli, 2020). 2.3.2 Independent Variables (Innovation and Development Indicators) Population Growth Rate Index (PGRI): Operationalized as the annual percentage change in total population. This continuous variable can take positive values (population growth), negative values (population decline), or zero (stable population). The indicator is expressed in percentage terms and sourced from UN population databases with values corresponding to the year preceding GII publication (2020 for GII 2021; 2023 for GII 2024). Current Educational Index (EI):* Operationalized as mean years of schooling for adults aged 25+. This continuous variable typically ranges from approximately 1 year (countries with very low educational attainment) to approximately 14 years (countries with very high educational attainment including substantial tertiary education). Values are sourced from UNDP databases with temporal alignment to GII publication years. Expected Educational Index (EI) * Operationalized as expected years of schooling for children entering education. This continuous variable typically ranges from approximately 8 years to approximately 22 years, reflecting both basic education duration and tertiary education participation rates. Values are sourced from UNDP databases with temporal alignment to GII years. Social Progress Index (SPI): Composite index ranging from 0 to 100, with higher scores indicating stronger social progress across basic human needs, foundations of wellbeing, and opportunity dimensions. Values are sourced from Social Progress Imperative annual reports corresponding to GII publication years. Worldwide Governance Quality Index (WGQI): Composite index expressed in percentile rank terms ranging from 0 to 100, with higher values indicating better governance quality. The index aggregates six governance dimensions into an overall assessment. Values are sourced from World Bank Worldwide Governance Indicators database with temporal alignment to GII years. Political and Civil Liberties Index (PCLI): Composite index derived from Freedom House scores, expressed on a 0-100 scale where higher values indicate greater political rights and civil liberties. Original Freedom House scoring is inverted where necessary to maintain consistent directional interpretation (higher values = more positive outcomes) across all variables in the analysis. «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 121 Global Competitiveness Index (GCI): Composite index ranging from 0 to 100, with higher scores indicating stronger competitive capabilities. Values are sourced from IMD World Competitiveness reports corresponding to GII publication years. Foreign Direct Investment Index (FDII): Operationalized as net FDI inflows as percentage of GDP. This continuous variable can take negative values (net FDI outflows), zero, or positive values (net FDI inflows), with magnitude indicating the relative importance of FDI flows to the economy. Values are sourced from World Bank and IMF databases with temporal alignment to GII years. Science-Research Activity Index (SRAI): Composite measure of scientific publication output and citation impact, expressed in index form with values typically ranging from near zero (minimal research activity) to several hundred (major research producers like the United States or China). Values are sourced from NSF Science and Engineering Indicators with temporal alignment to GII years. Country Globalization Index (CGI): Composite index ranging from 0 to 100, with higher values indicating greater globalization across economic, social, and political dimensions. Values are sourced from KOF Globalization Index database with temporal alignment to GII years. Healthy Life Expectancy Index (HLEI): Operationalized as healthy life expectancy in years at birth. This continuous variable typically ranges from approximately 50 years (countries with poorest health outcomes) to approximately 75 years (countries with best health outcomes). Values are sourced from WHO databases with temporal alignment to GII years. 2.3.3 Variable Standardization Given that the selected indicators employ different measurement scales—percentages, years, index values with different ranges—all variables were standardized prior to correlation analysis to enable meaningful comparison of relationship strengths (Field, 2013). Standardization was performed using z-score transformation: z = (X - μ) / σ where X represents the original value, μ represents the mean across all countries in the analysis sample, and σ represents the standard deviation. This transformation produces variables with mean = 0 and standard deviation = 1, facilitating direct comparison of correlation coefficients across variables with originally different scales (Gelman & Hill, 2006). 2.4 Analytical Methods 2.4.1 Correlation Analysis The primary analytical technique employed is Pearson product-moment correlation analysis, which quantifies the linear relationship between pairs of variables (Cohen et al., 2003). Correlation coefficients (r) range from -1 (perfect negative linear relationship) through 0 (no linear relationship) to +1 (perfect positive linear relationship). The strength of correlations is interpreted following conventional guidelines: |r| < 0.3 indicates weak correlation, 0.3 ≤ |r| < 0.7 indicates moderate correlation, and |r| ≥ 0.7 indicates strong correlation (Cohen, 1988). For each country group (top 50 and second 50), correlation matrices were computed showing pairwise correlations among all 13 indicators plus the GII score. This approach enables examination of: Indicator-GII correlations: The relationship between each individual indicator and overall GII performance, indicating whether the indicator effectively discriminates innovation capacity within that country group. Inter-indicator correlations: Relationships among the independent indicators themselves, revealing whether theoretically-related dimensions (e.g., education and health, governance and civil liberties) demonstrate expected associations within each context. Proceedings of the 12th International Scientific Conference 128 to Japan's gradual transition—suggests differential progress in knowledge economy transformation (WIPO & LBS, 2025). This variation raises questions about whether innovation indices adequately distinguish between countries following tangible-capital-intensive development pathways and those pursuing intangible-intensive strategies with profoundly different innovation requirements, capabilities, and outcomes (Zanello et al., 2016). The application of measurement frameworks emphasizing tangible dimensions may systematically favor countries at earlier industrialization stages while undervaluing the sophisticated intangible capabilities increasingly critical for frontier innovation in advanced economies (Goodridge et al., 2019). Sustainability Imperatives and Innovation Directionality Our findings intersect with growing scholarly emphasis on innovation directionality toward sustainability objectives rather than mere innovation quantity or efficiency (Schot & Steinmueller, 2018; Mazzucato, 2018). The GII and similar composite indices primarily assess innovation capacity and output without systematic evaluation of whether innovation activities advance or undermine environmental sustainability, climate stability, resource conservation, or social equity (Geels et al., 2017). This omission proves increasingly problematic as policy discourse shifts toward concepts like "transformative innovation," "sustainability transitions," and "mission-oriented innovation" that explicitly incorporate environmental and social criteria alongside economic performance (Weber & Rohracher, 2012; Kattel & Mazzucato, 2018). The intangible investment revolution potentially offers pathways toward more sustainable economic development given that knowledge-intensive activities typically involve lower material throughput and energy consumption than tangible-capital-intensive production processes (Haskel & Westlake, 2022). Software development, data analytics, organizational innovation, and creative industries generate economic value with relatively modest environmental footprints compared to heavy manufacturing, extractive industries, or transportation-intensive supply chains (Coyle & Diepeveen, 2021). However, this potential remains contingent on factors including energy sources powering digital infrastructure, electronic waste management, resource requirements for hardware supporting software systems, and whether intangible-intensive growth substitutes for or supplements material consumption (Jackson, 2017; Hickel, 2021). Innovation measurement frameworks must evolve to incorporate sustainability dimensions through indicators assessing eco-innovation capacity, green technology development, circular economy implementation, renewable energy transition progress, and alignment with Sustainable Development Goals (Kemp & Pearson, 2007; Ghisellini & Ulgiati, 2020). The current study's identification of indicator reliability variations across contexts suggests that sustainability-oriented indicators may similarly exhibit context-dependent relationships, requiring careful empirical validation across development groups (Saisana & Philippas, 2019). For instance, renewable energy innovation capacity may demonstrate different indicator patterns in countries at different stages of energy system development or with varying fossil fuel dependency. Implications for Policy and Practice The demonstrated unreliability of certain indicators across development contexts carries significant implications for how innovation indices should inform policy decisions and resource allocation (Bandola-Gill, 2019; Godin, 2022). Policymakers in moderate-performing innovation economies should interpret their rankings with appropriate skepticism regarding indicators showing weak correlation with innovation performance in their development group (Peck & Theodore, 2015). Prioritizing improvements in political liberties or aggregate foreign direct investment attraction based on their apparent importance in overall GII calculations may constitute inefficient resource allocation if these dimensions demonstrate weak actual relationship to innovation capacity in emerging system contexts (Ladi, 2011; Drezner, 2001). «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 129 Instead, policy attention should concentrate on dimensions demonstrating robust reliability across contexts—particularly research activity capacity, globalization integration, and to some extent governance quality and educational attainment—while recognizing that optimal policy configurations for advancing these dimensions may vary across national institutional contexts (Edquist & Johnson, 2021). The consistent relationship between research activity and innovation performance suggests that investments in scientific research infrastructure, university research capacity, researcher training, and research funding merit policy priority regardless of development stage (Lundvall, 2010). Similarly, the stable globalization-innovation relationship supports policies facilitating international knowledge flows, global value chain integration, scientific collaboration, and technology transfer (Dreher, 2006). For organizations producing composite innovation indices like the GII, our findings suggest several methodological refinements merit consideration (Saisana & Saltelli, 2020). First, transparent reporting of indicator reliability across development contexts would enable users to interpret rankings with appropriate awareness of measurement uncertainties and context-dependencies (OECD, 2008). Second, sensitivity analyses examining how rankings change under alternative indicator selections or weighting schemes would enhance user understanding of ranking robustness (Paruolo et al., 2018). Third, development of context-adapted sub-indices with different indicator compositions or weights for different country groups might improve measurement accuracy, though this approach trades off simplicity and universal comparability against context-appropriate assessment (Greco et al., 2019). Fourth, enhanced incorporation of intangible investment indicators—including software and database investment, intellectual property expenditure, organizational capital formation, brand equity development, and employee training intensity—would better capture contemporary innovation processes in knowledge-intensive economies (Haskel & Westlake, 2022; Corrado et al., 2021). Fifth, integration of sustainability-oriented indicators assessing green innovation capacity, environmental impact, and contribution to sustainable development goals would align innovation measurement with evolving policy priorities emphasizing innovation directionality alongside innovation quantity (Schot & Steinmueller, 2018; Mazzucato, 2018). Methodological Considerations and Limitations Several methodological considerations warrant acknowledgment in interpreting these findings. The correlation analysis approach reveals indicator reliability patterns but cannot establish causal relationships or definitively identify optimal indicator sets (Saisana & Saltelli, 2020). Strong correlation between an indicator and innovation performance may reflect either genuine causal contribution to innovation capacity or correlation through common underlying factors (Greco et al., 2019). For instance, political liberties might correlate with innovation performance in advanced economies not because democratic governance directly causes innovation but because both variables correlate with deeper institutional characteristics including rule of law, property rights protection, educational quality, or social capital (Edquist & Johnson, 2021). The binary stratification of countries into two groups based on GII rankings, while enabling clear comparison of high-performing versus moderate-performing contexts, necessarily obscures heterogeneity within groups (Landman & Carvalho, 2016). The top 50 countries include both ultrahigh-income innovation leaders like Switzerland and Sweden and upper-middle-income countries like China and Malaysia that may exhibit fundamentally different innovation system characteristics (WIPO, 2024). Similarly, the second 50 countries encompass both middle-income emerging markets and lower-income developing countries potentially requiring distinct measurement approaches (Zanello et al., 2016). More granular stratification by income level, regional characteristics, or innovation system typology might reveal additional context-dependent patterns, though sample size constraints limit feasible subdivisions (Paruolo et al., 2018). Proceedings of the 12th International Scientific Conference 130 The reliance on existing composite indices and aggregate indicators as independent variables introduces measurement error and conceptual complexity (Bandura, 2008). Many indicators employed in this analysis—including the Social Progress Index, Worldwide Governance Quality Index, Global Competitiveness Index, and Country Globalization Index—are themselves composite measures aggregating multiple underlying variables through methodological choices involving subjective elements (Saisana et al., 2005). This "composite-on-composite" analysis risks compounding measurement errors and obscuring relationships between specific underlying variables and innovation performance (Cherchye et al., 2008). More granular analysis examining relationships between innovation performance and specific underlying variables rather than composite indices would provide finer-grained insights, though at the cost of analytical complexity (OECD, 2008). Temporal dynamics present additional complexity given the three-year interval between 2021 and 2024 observation points (Archibugi et al., 2009). Innovation systems exhibit path-dependent evolution and long-term trajectories that cross-sectional or short-interval longitudinal analysis may inadequately capture (Dosi & Nelson, 2010). Educational investments shaping future human capital, institutional reforms establishing new governance frameworks, or infrastructure projects building research capacity involve multi-year or multi-decade timescales before fully manifesting in innovation performance (Hanushek & Woessmann, 2015). The observed indicator relationships therefore reflect primarily short-to-medium-term associations rather than capturing full dynamic processes through which various dimensions contribute to innovation capacity development over longer horizons (Godin, 2011). Data quality and availability constraints affect all cross-national comparative research, with particular severity in developing country contexts where statistical capacity limitations, inconsistent definitions, estimation procedures, and temporal misalignment introduce measurement error (Jerven, 2013). The restriction of analysis to 100 countries with complete data availability for all 13 indicators necessarily excludes many lower-income countries lacking comprehensive statistical systems, potentially limiting generalizability to the poorest nations with the least developed innovation systems (Bandura, 2008). Moreover, some indicators—particularly those derived from expert assessments rather than objective statistics—incorporate subjective judgment potentially subject to systematic biases favoring certain political systems, economic models, or cultural contexts (Davis et al., 2012). Future Research Directions These findings open multiple avenues for future investigation. First, expanded analysis examining indicator reliability across more granular development stratifications—including income level, regional characteristics, economic structure, or innovation system typology—would test whether the patterns identified here reflect broad development stage effects or more specific contextual factors (Zanello et al., 2016). Second, longitudinal analysis examining how indicator reliability evolves as countries transition across development stages would illuminate whether unreliable indicators in emerging systems gain relevance as innovation systems mature, or whether fundamental differences persist (Archibugi et al., 2009). Third, investigation of specific intangible investment dimensions—disaggregating software from intellectual property from organizational capital—and their relationships to innovation performance would provide finer-grained understanding of intangible-innovation linkages (Corrado et al., 2021). Fourth, comparative case study research examining innovation measurement challenges in specific national contexts would complement this quantitative analysis by revealing qualitative dimensions, institutional specificities, and policy processes that aggregate statistical analysis cannot capture (Yin, 2018). Fifth, experimental or quasi-experimental research designs examining how innovation index rankings causally affect policy decisions, investment flows, or development assistance allocation would illuminate the practical consequences of measurement methodologies «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 131 (Ravallion, 2012). Sixth, participatory research involving policymakers, business leaders, and innovation system stakeholders in validating indicator relevance and interpreting measurement results would enhance practical utility and contextual appropriateness of innovation assessment frameworks (Godin & Gingras, 2000). 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Менеджер получает готовый список приоритетных клиентов («Hot Leads»), что позволяет сфокусировать усилия отдела продаж именно на тех компаниях, которые с наибольшей вероятностью заинтересованы в продукте. Заключение Разработанное программное средство «SME-PromoOpmizer» демонстрирует высокую эффективность применения методов машинного обучения для задач банковского маркетинга. Точность модели классификации на тестовой выборке достигла 88%, что подтверждает валидность выбранного подхода. Внедрение подобного инструмента в ITландшафт банка позволит трансформировать процесс продаж: перейти от «ковровых» рассылок к точечным, своевременным предложениям, что приведет к росту конверсии и снижению стоимости привлечения клиента (CAC). «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 145 Список литературы 1. Bessi F. Opmizing Bank Markeng Campaigns with Machine Learning // Medium. – 2023. [Электронный ресурс]. 2. Accenture Report. Data-driven mastery in commercial banking. – 2022. 3. Müller A.C., Guido S. Introducon to Machine Learning with Python. – O'Reilly Media, 2017. – 392 p. 4. Chen T., Guestrin C. XGBoost: A Scalable Tree Boosng System // Proceedings of the 22nd ACM SIGKDD Internaonal Conference on Knowledge Discovery and Data Mining. – 2016. – P. 785–794. Proceedings of the 12th International Scientific Conference 146 Justification of the parameters and operating modes of a biogas plant for small farms Telmanov Imran master, Faculty of Engineering and Technology, Kazakh National Agrarian Research University, Almaty, Kazakhstan Sarkynov Yerbol Professor, Faculty of Engineering and Technology, Kazakh National Agrarian Research University, Almaty, Kazakhstan Zhakupova Zhanar PhD, Faculty of Water Resources and information technology, Kazakh National Agrarian Research University, Almaty, Kazakhstan Abilda Zhansaiya student, Faculty of Water Resources and information technology, Kazakh National Agrarian Research University, Almaty, Kazakhstan Abstract. The article presents the justification of the main design parameters and operating modes of a biogas plant intended for small farms. The relevance of biogas technologies is due to the need for renewable energy sources and sustainable waste management in agricultural production. The study analyzes the influence of substrate composition, hydraulic retention time, temperature regime, and organic loading rate on biogas yield and energy efficiency. Based on the analysis, optimal operating parameters are proposed for small-scale biogas plants, ensuring stable operation and economic feasibility. Keywords. biogas plant, small farms, operating modes, anaerobic digestion, renewable energy Introduction The development of renewable energy sources is one of the key priorities for sustainable agricultural production. Small farms generate significant amounts of organic waste, including livestock manure and crop residues, which can be effectively utilized through anaerobic digestion. Biogas plants allow simultaneous waste treatment and energy production, reducing environmental impact and increasing farm energy independence. However, for small farms, the efficiency of biogas plants strongly depends on the correct selection of design parameters and operating modes. Incorrect parameter selection can lead to unstable digestion processes, low biogas yield, and economic inefficiency. Therefore, the justification of optimal parameters and operating regimes for small-scale biogas plants is an актуальный research task. Materials and methods of research. The study is based on the analysis of typical biogas plants used in small farms with livestock populations of up to 50–100 heads. Cattle manure was considered as the main substrate. The following parameters were analyzed:  substrate moisture content;  organic loading rate; «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 147  hydraulic retention time (HRT);  temperature regime of anaerobic digestion;  specific biogas yield. The calculations were carried out using standard engineering methods for biogas plant design and generalized experimental data from previous studies. Comparative analysis of mesophilic and psychrophilic operating modes was performed. For any one feedstock, daily biogas production can be estimated using the following equation (Fulford, 2015): Where: - G is the biogas production (in m3 /day) - C is the biogas potential, which is the maximum amount of gas that can be produced from 1 kg of volatile solids in a feedstock (in m3 /kg) - Vd is the digester volume (in m3 ) - S is the initial concentration of volatile solids in the slurry (in kg/m3 ) - R is the feedstock retention time (in days) - k is a constant indicating the rate of gas production at a given temperature To simplify this equation, IRENA has calculated gas production across a wide range of temperatures and retention times, so that biogas production can be calculated as follows: Where G, Vd and S are the same as before and Y is a yield factor based on temperature and the feedstock retention time (see Table 3). Assuming that the digester volume (Vd) has already been calculated, it is only necessary to calculate the feedstock retention time (R) and initial concentration of volatile solids (S) in order to calculate daily biogas production and this can all be done in the following four steps. Step 1: First, the total feedstock volume should be calculated. This starts by multiplying the number of animals recorded in the survey by the total waste production per day (from Table 5) and adding to this the total weight of all other feedstocks used. This gives the daily waste input in kg. This figure can be assumed to be about the same as the volumetric input in litres. The answer to question 3 is then used to multiply that value to take into account the added water (e.g. if they add twice as much water then the waste input should be multiplied by three). The final value is divided by 1,000 to convert from litres to m3 . Step 2: The feedstock retention time is calculated by dividing the digester volume by the total feedstock volume. So, for example, if the digester volume is 5.0 m3 and the total daily feedstock volume (including water) is 0.08 m3 /day, the feedstock retention time (R) is 5.0/0.08= 62.5 days. Step 3: The initial concentration of volatile solids (S) is calculated by dividing the weight of volatile solids added each day by the daily waste inputs. The weight of volatile solids from animal waste can be calculated from the figures shown in Table 1 and the numbers of animals providing waste for the digester. The weight of volatile solids from other wastes can be calculated using the figures shown in Table 2,3. These figures can then be added together to get the total weight of volatile solids added each day, which can be divided by the total feedstock volume. So, for example, if the weight of volatile solids added each day is 5.6 kg/day and the daily feedstock volume is 0.08 m3 /day, the initial concentration of volatile solids (S) is 5.6/0.08 = 70 kg/m3 . Proceedings of the 12th International Scientific Conference 148 Table 1: Animal waste feedstock properties Animal Total production (kg/day) Volatile solids (kg/day) Buffalo 14 1,94 Cow 10 1,42 Calf 5 0,50 Sheep/goat 2 0,44 Pig 5 1,00 100 hens 7,5 2,77 Horse 10 2,24 Human 0,2 0,03 Table 2: Other feedstocks volatile solid content Animal Volatile solids (in %) Cereals/grains 0,81 Rice straw 0,36 Wheat straw 0,39 Grass 0,51 Corn stalk 0,43 Fruit waste 0,14 Vegetable waste 0,16 Fat 0,83 Mixed food waste 0,08 Mixed organic waste 0,26 Table 3: Yield factors for biogas production, by temperature and feedstock retention time Feedstock retention time (in days) Temperature (°C) 16-18 19-21 22-24 25-27 28-30 31-33 6-10 5.41 7.98 10.83 13.59 15.91 18.33 11-15 4.73 6.79 8.99 11.09 12.88 14.74 16-20 4.21 5.90 7.68 9.37 10.82 12.32 21-25 3.79 5.22 6.70 8.11 9.33 10.59 26-30 3.44 4.69 5.95 7.15 8.20 9.28 31-35 3.16 4.25 5.35 6.39 7.32 8.26 36-40 2.91 3.88 4.86 5.78 6.60 7.44 41-45 2.71 3.58 4.45 5.27 6.02 6.77 46-50 2.53 3.32 4.10 4.85 5.53 6.21 51-55 2.37 3.09 3.81 4.49 5.11 5.74 56-60 2.23 2.89 3.55 4.18 4.75 5.33 61-65 2.10 2.72 3.33 3.91 4.44 4.98 66-70 1.99 2.57 3.13 3.67 4.17 4.67 71-75 1.89 2.43 2.95 3.46 3.93 4.40 76-80 1.80 2.30 2.80 3.27 3.71 4.15 81-85 1.72 2.19 2.66 3.10 3.52 3.94 86-90 1.65 2.09 2.53 2.95 3.34 3.74 91-95 1.58 2.00 2.41 2.81 3.19 3.56 96-100 1.52 1.92 2.31 2.69 3.04 3.40 «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 149 Results of research. The analysis showed that for small farms, the mesophilic temperature regime (35–38 °C) provides the most stable biogas production with acceptable energy costs for heating. The optimal hydraulic retention time was found to be 20–25 days, which ensures sufficient substrate decomposition without excessive digester volume. The recommended organic loading rate for small-scale biogas plants is 2.0–3.0 kg of volatile solids per cubic meter of digester volume per day. Under these conditions, the specific biogas yield reaches 0.25–0.35 m³ per kg of organic matter. It was established that stable operating modes reduce fluctuations in gas production and improve the overall energy efficiency of the system. The produced biogas can be used for heat supply of farm buildings or for electricity generation using small gas engines. Conclusion. The justification of the parameters and operating modes of a biogas plant is a key factor for its efficient application in small farms. The proposed operating parameters ensure stable anaerobic digestion, acceptable biogas yield, and economic feasibility. The results of the study can be used in the design and operation of small-scale biogas plants in agricultural enterprises. References 1. Джусупова Д.Б. Экологиялық биотехнология [Мәтін]: оқулық / Д.Б. Джусупова; Қр білім және ғылым м-гі, ҚР жоғары оқу орындарының қауымдастығы.- Алматы, 2013.- 336 б. 2. Ауланбергенов А.А. Сумен жабдықтау, садыра ағындысын тасымалдау және өндеу технологиялары (мал шаруашылығы нысандарында) [Мәтін]: моногр. / А.А. Ауланбергенов.- Алматы: ҚазҰАУ, 2013. 3. Акынбеков Е.К. Биогаз өндірудің негіздері [Текст]: оқу құралы / Е.К. Акынбеков, Р. Нусипали, Б.А. Койайдаров.- Алматы: Эпиграф, 2016.- 108 с. 4. Ершина А.К. Теория и практика использования возобновляемых источников энергии [Текст]: учеб. пособие / А.К. 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Oslo, Norway, 2025 151 АҚПАРАТТЫҚ ЖҮЙЕЛЕРДЕ ҚОРҒАЛҒАН АУТЕНТИФИКАЦИЯ: МОДЕЛЬДЕУ ЖӘНЕ ТАЛДАУ Ергеш Нұрсұлтан Сагитянұлы PhD докторанты, 8D06301 – Ақпараттық қауіпсіздік жүйелері, Әл‑Фараби атындағы Қазақ ұлттық университеті Ғылыми жетекші: Капалова Нурсулу Алдажаровна техника ғылымдарының кандидаты, қауымдастырылған профессор Аннотация Мақалада ақпараттық жүйелерде қолданылатын қорғалған аутентификация схемаларының қауіпсіздігін формальды модельдер арқылы негіздеу және тәжірибелік модельдеу нәтижелерімен дәлелдеу жүзеге асырылады. Аутентификация процесі ықтималдық‑статистикалық, энтропиялық және ойындық (game‑based) ұстанымдар тұрғысынан сипатталып, бір факторлы (SFA), екі факторлы (2FA) және көп факторлы (MFA) тәсілдер үшін шабуылдың сәтті болу ықтималдығы, ақпараттық белгісіздік өлшемдері және қате жіберу көрсеткіштері (FAR/FRR) есептеледі. Ұсынылған тұжырымдар мен эксперименттік деректер көп факторлы аутентификацияны дұрыс конфигурациялау және қосымша қорғаныс механизмдері (salted hash, OTP/TOTP, WebAuthn) ақпараттық жүйелердің бұзылу ықтималдығын бірнеше реттік дәрежелерге төмендететінін көрсетеді. Түйін сөздер: ақпараттық қауіпсіздік, аутентификация, формальды модель, энтропия, FAR/FRR, MFA, OTP/TOTP. Кіріспе Ақпараттық жүйелердің қауіпсіздігі үш базалық қасиетпен сипатталады: құпиялылық, тұтастық және қолжетімділік (CIA). Осы қасиеттердің орындалуы көбіне қолжетімділікті басқару (access control) механизмдерінің сапасына тәуелді. Қолжетімділікті басқарудың алғашқы және міндетті кезеңі - аутентификация, яғни субъектінің (пайдаланушы, қызмет, құрылғы) кім екенін дәлелдеу процесі. Практикада шабуылдардың едәуір бөлігі аутентификациялық деректердің компрометациясы, фишинг, қайталау (replay), парольдерді толық іріктеу және сессияны ұрлау сценарийлеріне негізделеді [1-3]. Сондықтан аутентификация схемаларын ғылыми тұрғыдан талдау: (а) формальды қауіп моделін құру; (ә) қауіпсіздік қасиеттерін дәлелдеу; (б) нақты параметрлермен тәжірибелік модельдеу арқылы сандық бағалау - өзекті ғылыми міндет болып табылады.Зерттеудің мақсаты - ақпараттық жүйелерде қорғалған аутентификацияның формальды моделін құру, қауіпсіздік қасиеттерін дәлелдеуге жақын тұжырымдар ұсыну және тәжірибелік модельдеу арқылы әртүрлі схемалардың қауіпсіздік көрсеткіштерін салыстыру. Зерттеу объектісі - аутентификация протоколдары мен олардың параметрлері; пәні - шабуыл ықтималдығын және қате шешімдер статистикасын төмендететін қорғаныс тәсілдері. Proceedings of the 12th International Scientific Conference 152 Байланысты жұмыстар және теориялық негіз Аутентификация қауіпсіздігін бағалау әртүрлі теориялық аппаратқа сүйенеді: ықтималдық теориясы, ақпарат теориясы (энтропия/мин‑энтропия), криптографиялық дәлелдер және биометриялық сәйкестендіру статистикасы. Парольдік жүйелердің әлсіздігі және оларды алмастыру/толықтыру бағыттары көптеген еңбектерде қарастырылған [4]. Стандарттар деңгейінде цифрлық идентификация талаптары NIST SP 800‑63 құжаттарында жүйеленген [5], ал ұйымдық деңгейде ақпараттық қауіпсіздік менеджменті ISO/IEC 27001 стандартында белгіленеді [6]. Бір реттік парольдер HOTP/TOTP протоколдарымен, уақытқа тәуелді генерациямен сипатталады [7-8]. Қазіргі тренд - фишингке төзімді аутентификация (FIDO2/WebAuthn), онда қоғамдық‑жеке кілттер инфрақұрылымы және құрылғыдағы қауіпсіз модуль қолданылады [9]. Парольдік аутентификация жүйелерінің әлсіздігі мәселесі көптеген іргелі және қолданбалы еңбектерде жан-жақты зерттелген. Florêncio және Herley еңбектерінде пайдаланушылардың пароль таңдау әдеттері төмен энтропияға ие екендігі және шабуылдаушылар үшін болжау оңай болатыны эксперименттік түрде көрсетілген [4]. Bonneau және әріптестері парольдерді толық алмастырудың күрделілігін, сондай-ақ көп факторлы тәсілдерге көшу қажеттігін негіздеді [5]. Бұл зерттеулер парольдік жүйелердің өздігінен ақпараттық жүйелердің қауіпсіздігін қамтамасыз етуге жеткіліксіз екенін дәлелдейді. Стандарттау деңгейінде цифрлық идентификация және аутентификация талаптары NIST SP 800-63 сериялы құжаттарында жүйеленген. Бұл құжаттарда аутентификация деңгейлері (Authenticator Assurance Level - AAL), факторлар типтері және оларды қолдану сценарийлері нақты регламенттелген [6]. Аталған стандарттар ықтимал қауіп моделін, шабуылдаушының мүмкіндіктерін және қабылданатын тәуекел деңгейін анықтауға мүмкіндік береді. Ұйымдық және басқарушылық тұрғыдан алғанда, ақпараттық қауіпсіздік менеджментінің негіздері ISO/IEC 27001 стандартында белгіленіп, аутентификация қолжетімділікті басқару (access control) механизмдерінің ажырамас бөлігі ретінде қарастырылады [7]. Бір реттік парольдерге негізделген аутентификация HOTP және TOTP протоколдары арқылы жүзеге асырылады. RFC 4226 және RFC 6238 құжаттарында көрсетілгендей, бұл протоколдар құпия кілт пен санауыш немесе уақыт параметріне негізделіп, қайта қолдану шабуылдарына төзімділікті қамтамасыз етеді [8-9]. Алайда зерттеулер көрсеткендей, дәстүрлі OTP схемалары фишинг шабуылдарына толық төзімді емес, себебі шабуылдаушы пайдаланушы енгізген кодты нақты уақыт режимінде қайта пайдалана алады. Соңғы жылдары аутентификация саласындағы негізгі ғылыми және технологиялық трендтердің бірі - фишингке төзімді аутентификация механизмдері болып табылады. Бұл бағытта FIDO2/WebAuthn стандарттары ерекше орын алады. Аталған тәсілдерде аутентификация асимметриялық криптографияға негізделіп, әрбір сервис үшін жеке кілт жұбы қолданылады, ал аутентификациялық жауап нақты доменге (origin binding) криптографиялық түрде байланады [10]. Мұндай архитектура фишинг және man-in-themiddle шабуылдарының тиімділігін айтарлықтай төмендетеді. Биометриялық аутентификация жүйелері де әдебиеттерде кеңінен қарастырылған. Jain, Ross және Prabhakar еңбектерінде биометриялық сәйкестендірудің теориялық негіздері, сондай-ақ қате қабылдау (FAR) және қате кері қайтару (FRR) көрсеткіштерінің арасындағы компромис ғылыми тұрғыда талданған. Бұл көрсеткіштер биометриялық факторларды көп факторлы аутентификация схемаларына енгізу кезінде шешуші мәнге ие, себебі қауіпсіздік пен қолайлылық арасындағы баланс дәл осы параметрлер арқылы анықталады. «Theoretical Hypotheses and Empirical results» (December 18-19, 2025). Oslo, Norway, 2025 153 Қауіп моделі және негізгі анықтамалар Қорғалған аутентификацияны талдау үшін шабуылдаушы моделін нақтылау қажет. Шабуылдаушы A келесі қабілеттерге ие болуы мүмкін: (i) желілік арнаны тыңдау/өзгерту (MitM), (ii) онлайн сұраулар жасау (rate‑limited), (iii) офлайн хэш базасын қолға түсіру, (iv) әлеуметтік инженерия (phishing), (v) құрылғы жоғалту сценарийі. Бұл қабілеттерді формальды түрде мүмкіндіктер жиыны арқылы белгілейміз. A = ⟨Cap_net, Cap_online, Cap_offline, Cap_social, Cap_device⟩ (1) Аутентификация процесі пайдаланушы U, жүйе S және хабар алмасу арнасы Ch арқылы жүретін протокол ретінде қарастырылады. Схеманың мақсат функциясы — заңды пайдаланушы үшін қабылдау ықтималдығын жоғары ұстап, заңсыз субъект үшін қабылдау ықтималдығын өте төмен деңгейге жеткізу. Auth: (U, C) → R, R ∈ {0,1} (2) Мұндағы C - аутентификациялық деректер векторы (факторлар жиыны). Егер C = (C₁, …, Cₙ) болса, онда көп факторлы схема қарастырылады. Осы жерде маңызды қауіпсіздік қасиеттерін анықтаймыз: • Completeness: заңды пайдаланушының қабылдануы; • Soundness: заңсыз субъектінің қабылданбауы; • Replay‑resistance: қайталау шабуылына төзімділік; • Phishing‑resistance: фишингке төзімділік (арналық/контексттік байлау). Бір факторлы аутентификацияның (SFA) сандық талдауы SFA көбіне пароль P арқылы іске асады. Пароль кеңістігінің қуаты N пароль ұзындығы l және алфавит өлшемі a арқылы анықталады. Бұл (3) формула пароль күші мен brute‑force қауіпінің негізгі байланысын береді. N = a^l (3) Егер шабуылдаушы k әрекет жасаса (онлайн немесе офлайн), онда парольді табудың ықтималдығы (4) формуламен беріледі. P_bf = k / N = k / a^l (4) Ғылыми дәлел: (4) формуладан P_bf ≤ 1 шарты орындалатыны және l артқан сайын P_bf экспоненциалды түрде азаятыны шығады. Дәлелдеу үшін a>1 болғанда a^l монотонды өсетінін ескерсек, 1/a^l монотонды кемиді; сондықтан P_bf(k тұрақты) кемиді. Бұл нәтижені практикалық саясатқа айналдыру үшін rate‑limit және lockout енгізу арқылы k‑ны шектеу қажет [5]. Ақпарат теориясы бойынша парольдің белгісіздігі мин‑энтропиямен бағалануы мүмкін. Егер пароль тең ықтималды таңдалса, онда мин‑энтропия: H_∞(P) = -log₂(max_p Pr[P=p]) = log₂ N (5) Дәлел: тең ықтималды жағдайда max_p Pr[P=p]=1/N, сондықтан H_∞(P)=−log₂(1/N)=log₂N. Бұл (5) формула пароль кеңістігі ұлғайған сайын шабуылдаушының «ең жақсы» болжамының табыс мүмкіндігі төмендейтінін формальды көрсетеді. Екі факторлы аутентификация (2FA): тәуелсіздік және шектер 2FA кезінде C=(C₁,C₂) және екі тәуелсіз фактор талап етіледі: мысалы, пароль + OTP. Тәуелсіздік жорамалында шабуылдың сәтті болу ықтималдығы көбейтінді түрінде бағаланады. P_attack^{2FA} = Pr[break C₁ ∧ break C₂] = P_{C₁} · P_{C₂} (6) Proceedings of the 12th International Scientific Conference 256 including artificial intelligence, the platform economy, the use of big data, and other related areas,” the President stated [1]. Global experience demonstrates that the rapid development of artificial intelligence has compelled many states to establish regulatory frameworks capable of reconciling technological progress with fundamental social imperatives. Kazakhstan, as a key actor in Central Asia with a rapidly developing digital economy, adopted its first AI Law, positioning itself as a regional pioneer in digital transformation. The new legislation, consisting of 31 articles, establishes core principles such as transparency, fairness, and human-centeredness, while prohibiting manipulative AI practices and mandating state oversight in high-risk domains affecting the protection of citizens’ interests, including the judiciary, prosecutorial bodies, and other sensitive sectors. However, as a transition economy overcoming its post-Soviet legacy, Kazakhstan’s AI policy represents not merely a technical regulatory framework, but also a reflection of deeper social, cultural, economic, and demographic processes. At the same time, active discussions are already underway within Kazakhstan’s political institutions concerning the ethical and security dimensions of AI deployment, which require heightened attention. According to the Chairman of the Mazhilis of the Parliament of the Republic of Kazakhstan, Erlan Koshanov, alongside the benefits AI brings to society, there is growing concern that artificial intelligence may substitute genuine knowledge and cognitive engagement. Public opinion surveys indicate that Kazakhstani citizens are increasingly aware of both the opportunities and risks associated with the integration of AI into education. As noted by parliamentary representatives, the precise boundaries of potential threats posed by artificial intelligence have not yet been clearly defined. To counteract the misuse of AI technologies, various regulatory approaches are being proposed. For instance, beginning in September 2025, China required national social media platforms to implement mandatory labeling of AI-generated content, and Kazakhstan has adopted a similar practice. Despite these measures, the Speaker of the lower house of Parliament emphasized that, according to public opinion polls, 40.5% of Kazakhstan’s population assesses the impact of artificial intelligence positively, while 37% tend to perceive it negatively, arguing that AI oversimplifies learning processes and contributes to superficial knowledge acquisition. In contrast, the Deputy Prime Minister and Minister of Artificial Intelligence and Digital Development of the Republic of Kazakhstan, Zhaslan Madiyev, argues that AI should not be viewed primarily as an object of concern, but rather as a strategic instrument for addressing key national challenges. One of the government’s central objectives for the coming years is to train one million people in AI-related skills over a five-year period. According to him, these educational initiatives target school students, university students, civil servants, businesses, and other relevant stakeholders. In turn, the Minister of Science and Higher Education of the Republic of Kazakhstan, Sayasat Nurbek, stated that the government is systematically supporting AI-oriented research through targeted and grant-based funding programs. Currently, 27 universities and six research institutes across 11 regions are actively engaged in artificial intelligence research, involving a total of 479 researchers. The highest concentration of projects is observed in Almaty and Astana. “Twenty-six projects are being implemented by young scientists under the age of 40, covering seven priority scientific areas. AI research is primarily focused on advanced manufacturing processes, digital technologies, and the aerospace sector. Our key task today is to adapt higher education to the requirements of the AI era. Tangible results have already been achieved: at present, 30 higher education institutions offer 38 educational programs in artificial intelligence. Starting from 2025, AI competencies will be integrated into all educational programs,” he emphasized during parliamentary hearings on the development and regulation of artificial intelligence in Kazakhstan held on September 26, 2025, in the Mazhilis [2]. As of today, Kazakhstan is implementing 62 artificial intelligence projects with a total value of 9.7 billion tenge (approximately USD 17 million), and these figures are expected to grow further amid rapidly [Document text truncated for crawler view.]