https://repository.hneu.edu.ua/handle/123456789/35031 2026-06-10T13:49:46Z https://repository.hneu.edu.ua/handle/123456789/40636 Назва: Interactive Learning in Art and Engineering: Exploring the Dunhuang Murals through Artificial Intelligence Автори: Borysenko D.; Wang Lei Короткий огляд (реферат): This study explores the integrationofartificialintelligence(AI)intointerdisciplinaryeducationthroughaproject-basedlearningmodelcenteredontheDunhuangmurals.Bridgingarthistory,computerscience,andengineering,theprojectinvolved28studentsincollaborativetaskssuchasdigitalrestora-tion,neuralstyletransfer,andmetadatavisualization.Resultsindicatesignificantimprovementinstudents’technicalcompetencies,culturalunderstanding,andethicalawareness.ByembeddingAItoolsinculturallymeaningfulcontexts,theinitiativefostereddeepengagementandcriticalreflectiononissuessuchashistor-icalauthenticity,authorship,andculturalappropriation.ThemodularframeworkproposedinthisresearchdemonstratesstrongpotentialforadaptationinSTEAMeducation,emphasizingtheimportanceofcombiningtechnicalinnovationwithresponsibleculturalinquiry.Informedbytheincreasingrelevanceoftechnol-ogyinthearts,thisresearchdrawsonthehistoricalandculturalsignificanceoftheDunhuangfrescoestoproposeneweducationalparadigms.Itfurtherinvesti-gateshowAI-drivenmethodologies—suchasgenerativeadversarialnetworksandvisualanalytics—canofferstudentsimmersiveaccesstofragileheritagematerialswhileencouraginginterdisciplinarycollaborationandethicalreflection.ThestudyprovidesempiricalevidencesupportingthedevelopmentofscalableeducationalmodelsandcontributestothebroaderdiscourseontheresponsibleuseofAIinculturalheritageeducation. 2026-01-01T00:00:00Z https://repository.hneu.edu.ua/handle/123456789/40635 Назва: Modern Potential of Machine Learning in Adaptive Interface Development Автори: Borysenko D.; Cao Songshan Короткий огляд (реферат): The rapid evolution of machine learning (ML) technologies has opened up unprecedented opportunities in the development of adaptive user interfaces that can dynamically respond to the behavior, needs and emotional state of the user. Using ML techniques such as natural language processing, image recognition and real-time data analysis, these interfaces achieve a high level of personalization and interactivity, overcoming the most problematic area for users in obtaining the desired content. This paper examines the current potential of machine learning in the development of adaptive interfaces, which has an important application in educational platforms and assistive technologies for people with disabilities. The study highlights how ML-driven adaptive interfaces can dynamically adjust content, navigation and interaction modalities according to the specific requirements of users. For the educational process, such interfaces can change teaching strategies based on real-time assessment of the student’s progress and emotional engagement. Similarly, assistive technologies can provide more intuitive and accessible solutions for people with motor, visual, or hearing impairments by recognizing gestures, voice commands, or facial expressions. Particular attention is paid to very promising tools like Google Teachable Machine, which simplify the development of adaptive systems. The evidence suggests that ML-based adaptive interfaces can improve learning outcomes and accessibility, while addressing critical issues such as data privacy and ethical implementation. Integrating such interfaces into broader technology ecosystems has the potential to improve user satisfaction, increase productivity, and promote inclusivity. Despite these benefits, the study highlights the need for robust frameworks to mitigate ethical concerns, increase algorithmic transparency, and ensure equitable access to adaptive technologies. 2026-01-01T00:00:00Z https://repository.hneu.edu.ua/handle/123456789/40634 Назва: Generation Capabilities of Artificial Intelligence: In Search of Optimization Автори: Borysenko D. Короткий огляд (реферат): Today, the development of artificial intelligence (AI) is at a turning point, characterized by its high level of generative capabilities that are transforming both industries and research. From text generation to natural language processing, from image generation to high-quality video content creation and beyond, AI models demonstrate extraordinary proficiency in generating powerful results. However, the pursuit of optimization remains a pressing challenge: whether it is accuracy, efficiency, or ethical considerations. This paper explores the current state of AI capabilities, focusing on optimization strategies, intrinsic limitations, and future trajectories. Additionally, the interplay between computational efficiency, model interpretability, and societal impact is discussed. By exploring recent research and advances, this article aims to provide a comprehensive overview of the field and identify avenues for further improvement. 2026-01-01T00:00:00Z https://repository.hneu.edu.ua/handle/123456789/40633 Назва: Social and Communication Support of the Organization: Theoretical Foundations, Approaches and Strategic Importance in the Context of Digital Transformation Автори: Borysenko D.; Wang Honghai Короткий огляд (реферат): The article examines the theoretical foundations of the social and communication support (SCS) of an organization as a multi-component and multi-level system that combines social, managerial, informational and technical aspects of managing communication processes. Various approaches to understanding social communication are investigated: Ukrainian, Western, Asian and sociological. Particular attention is paid to the analysis of social communication models, functional roles of communication processes and classification dimensions. Among the key functions of social communication, the information and organizational, identification, influence and crisis (stabilization) functions are identified. Four main criteria for classifying communications are identified: by the number of participants, direction, formality and method of transmission. A multidimensional classification of social communication is proposed, which takes into account modern trends in digitalization, multiculturalism and internationalization of business. Four main subsystems of SCS are analyzed in detail: social, informational, technical and managerial. An integrated approach to the formation of the SCS is justified, which involves combining the best practices of Ukrainian, Western, Asian and sociological schools of scientific thought. This approach allows you to create a holistic communications system that meets the modern requirements of the information society and the needs of the digital economy. 2025-01-01T00:00:00Z