The Kyrgyz National University, named after Jusup Balasagyn (Kyrgyz: Жусуп Баласагын атындагы Кыргыз Улуттук Университети; Russian: Киргизский национальный университет имени Жусупа Баласагына), is a national university in Kyrgyzstan.Kyrgyz National University is located in the capital city of Bishkek. It is the oldest and the largest Higher Education Institution of the Kyrgyz Republic. The institution was established on 25 October 1925 as the Kyrgyz Institute of Education. Recently Kyrgyz National University started its Faculty of Medicine in collaboration with LN University, India, and Kyrgyz National University becomes the first University in Central Asia to start a Faculty of Medicine campus in association with an Indian University.
Despite extensive research on the internationalization of firms in emerging economies, why family firms under similar institutional conditions follow divergent global expansion paths remains poorly understood. This study examines how industrial diversification affects the scale and scope of internationalization in family firms and the moderating role of family governance structures. Using panel data from Chinese-listed manufacturing family firms from 2013 to 2020, we reveal that industrial diversification generally impedes both the scale and scope of internationalization. We address this research gap by proposing a novel theoretical distinction between “restrictive” and “broad” family governance structures. This distinction elucidates why family firms in similar institutional environments exhibit divergent internationalization paths, thereby bridging the gap in the literature on family firm strategy interactions. Specifically, we demonstrate that restrictive governance structures limit strategic flexibility and exacerbate the negative effect of diversification on internationalization, whereas broad governance structures mitigate this effect by enabling more adaptive strategic behavior. Furthermore, heterogeneity analysis further shows that the constraining effect is stronger in “natural” family firms. These findings advance our understanding of governance heterogeneity in family firms and offer practical strategic insights for family firms seeking to balance diversification and internationalization through governance reforms.
To remain competitive, authorities had to apply artificial intelligence, especially in logistics. This study's aim was to examine how the digital transformation and artificial intelligence create new opportunities for economic growth in the logistics industry, with a particular focus on Kyrgyzstan. Implementation remains low. Policies aim to develop the sector but are insufficient for international competitiveness. The AI can address logistical challenges, optimize routes, improve management, and reduce costs. The key issues - low digitalization, shortage of specialists, and weak infrastructure - can be mitigated through sound policies. The results provide insights for policymakers and industry stakeholders on leveraging AI and digital transformation to boost economic performance in logistics.
The aim of the study was to identify the main types of machine learning (ML) models that help adapt the educational environment to the individual needs of students in universities. The methodology combined a systematic analysis of approaches to teaching using ML algorithms with a comparative analysis of the digital transformation of higher education in Kyrgyzstan, Azerbaijan, Poland, and Ukraine. The analysis of ML approaches and the comparative analysis of the digitalisation of the examined educational institutions revealed key mechanisms for personalising the learning process. The comparison of practices showed that Poland and Ukraine prefer reinforcement learning models and academic performance prediction algorithms; Azerbaijan used clustering methods to group students by learning styles; and Kyrgyzstan implemented recommendation systems to improve motivation and feedback. The analysis suggests that the implementation of these technologies depends on four key factors: the level of digital competence of teachers, the availability and quality of educational data, the integration of adaptive modules into learning platforms, and the presence of state policies regulating the use of artificial intelligence (AI) in education. Additionally, it was found that personalised ML modules contribute to increasing students' intrinsic motivation, the development of self-learning skills, and active interaction within learning groups. The practical value of the work lies in the recommendations for deploying adaptive ML modules in higher education institutions in the countries under study, which will help improve the quality of education, student motivation, and reduce the gap in academic achievements.
The article examines teacher preparation for working with gifted learners in inclusive education by positioning differentiated instruction as a structuring principle of professional readiness. The study aimed to develop and pilot a theoretically grounded preparation model that connects inclusive pedagogy, gifted education, and differentiated instructional design within a unified framework of teacher education. The empirical part of the study used a one-group pretest–posttest pilot design with future teachers from two universities in Kazakhstan and Kyrgyzstan. The final matched sample included 79 participants who completed a semester-based module focused on differentiated support for gifted learners in heterogeneous classrooms. The module addressed giftedness as a dynamic and context-sensitive developmental phenomenon and included work with learner profiles, cognitively complex tasks, flexible pacing, enrichment, formative assessment, and socio-emotional responsiveness. The results showed statistically significant within-group increases in self-reported professional readiness across the measured domains. The most pronounced gains were observed in gifted-specific readiness, particularly in identifying and developing gifted potential, designing cognitively complex tasks, and managing differentiated instruction and assessment. Regression analysis indicated that gains in gifted-specific readiness were most strongly associated with improvement in differentiated instructional readiness. Within the limits of a one-group self-report pilot design, the findings suggest that preparation for inclusive gifted education may need to move beyond general inclusive attitudes toward a more differentiated pedagogical repertoire. Such preparation may support future teachers in connecting participation, challenge, support, and socio-emotional responsiveness in mixed-ability classrooms. The study contributes to teacher education research by offering preliminary evidence on differentiation as an operational link between inclusive education and gifted education. Its conclusions remain cautious, as the study relied on self-report measures and did not include a control group, random assignment, observed teaching practice, or direct learner-level outcomes.
