https://opendata.uni-halle.de//handle/123456789/2 Sat, 04 Apr 2026 10:48:52 GMT 2026-04-04T10:48:52Z http://opendata.uni-halle.de:80/retrieve/d42e1469-6e68-42e4-9e23-9cfcd1ffe70e/zeitschriften3.jpg https://opendata.uni-halle.de//handle/123456789/2 https://opendata.uni-halle.de//handle/1981185920/124818 Title: AI in Education : Revolutionizing Learning and Personalized Instruction Author(s): Jumaev, Giyosjon Abstract: Artificial Intelligence (AI) is quickly reshaping industries globally, and the field of education is particularly poised for transformative innovation. Conventional instructional approaches frequently find it difficult to meet the varied needs and speeds of students, often resulting in deficiencies in both understanding and involvement. In response to these shortcomings, technology leveraging AI has been deployed to enrich educational experiences and boost learning achievements. The purpose of this research is to investigate how AI is fundamentally changing education, specifically concentrating on customized learning, smart tutoring systems, and improved efficiency in administrative tasks. The study adopted a literature review approach, analyzing current academic studies, case analyses, and AI implementations across both compulsory (K-12) and post-secondary education sectors. The results show that AI substantially raises student performance by providing customized teaching, adaptive evaluations, and immediate feedback via Intelligent Tutoring Systems (ITS). Furthermore, predictive data analysis enables instructors to proactively identify vulnerable students, while automation solutions alleviate administrative pressures, including tracking attendance and grading. AI-driven accessibility and language translation tools also promote an inclusive environment by assisting students from varied linguistic and cultural origins. Ultimately, AI exhibits considerable promise for enhancing individualized teaching, boosting educational effectiveness, and broadening access to high-quality learning. Nevertheless, critical issues like ethical dilemmas, data security, and the potential for decreased human interaction in educational settings require careful consideration. In summary, AI is a powerful resource that can supplement existing teaching methods, guiding the development of an educational system that is more adaptive, accessible, and successful. Mon, 01 Dec 2025 00:00:00 GMT https://opendata.uni-halle.de//handle/1981185920/124818 2025-12-01T00:00:00Z https://opendata.uni-halle.de//handle/1981185920/124817 Title: Balancing Career and Academic Pursuits of Young Professionals for Sustainable Career and Education Development Author(s): Iglesia, Anna Pearl B. Abstract: Young professionals aged 24 to 29 often struggle to juggle the different needs of their career and academic pursuits. This dual responsibility builds overlapping pressures that can affect their personal well-being, their academic achievement, and professional development. In this research, the authors utilized the SEM analysis to investigate the major drivers that affect this balance. An online survey questionnaire was employed to collect data among fifty participants on indicators such as balanced engagement, academic performance, career achievement, and psychological and emotional stressors. The result of the study emphasizes the importance of academic performance, professional advancement, and job contentment; moreover too, having structured routines, a good support system, well-being, and stress resilience are all very important. The findings of this study gave valuable insights to young professionals, employers, and educational institutions in creating an enabling environment for career growth and lifelong learning. The SEM findings further provide a basis for future studies aimed at enhancing support mechanisms for sustainable academic and professional growth that are aligned with broader global sustainability efforts. Mon, 01 Dec 2025 00:00:00 GMT https://opendata.uni-halle.de//handle/1981185920/124817 2025-12-01T00:00:00Z https://opendata.uni-halle.de//handle/1981185920/124816 Title: Innovation-Driven Analysis of Social Project Mechanisms for Renewable Energy Transition Author(s): Zikrillayev, Nurullo Abstract: This study focuses on the public perception and social acceptance of renewable energy projects, as well as on the analysis of mechanisms for stakeholder and community engagement. The article examines the main factors influencing public support for renewable energy initiatives, including levels of awareness, trust in project developers, environmental concerns, and cultural contexts. A comprehensive analysis of survey data, media content, and case studies of both successful and unsuccessful projects was conducted. Based on these data, models were developed to explain the interrelations among key factors, and practical recommendations were formulated to enhance community support for renewable energy initiatives. Particular attention is given to the role of communication, transparency, and community participation at all stages of project implementation. Public perception and social acceptance are interpreted as multidimensional phenomena encompassing psychological, cultural, economic, and political dimensions. Theoretical frameworks applied in this research include the diffusion of innovations theory, social capital model, trust theory, and risk perception models. The analysis demonstrates that active stakeholder involvement and transparent communication substantially increase public trust and support for renewable energy initiatives. Furthermore, the use of advanced methods of social media analytics and network analysis enables the identification of key sources of both negative and positive sentiment, which is essential for designing effective public opinion management strategies. The results confirm the need for a systemic and interdisciplinary approach to accelerate the transition toward sustainable energy systems. The study provides practical recommendations for policymakers, businesses, and research institutions aimed at enhancing social support for renewable energy projects and reducing resistance from local communities. Mon, 01 Dec 2025 00:00:00 GMT https://opendata.uni-halle.de//handle/1981185920/124816 2025-12-01T00:00:00Z https://opendata.uni-halle.de//handle/1981185920/124815 Title: Survey of Sensor-Based Arabic Sign Language Datasets Author(s): Saleh, Ahmed Abstract: Sign language is the primary means of communication for the deaf and hard-of-hearing community, representing a linguistic bridge that connects them to society and enables them to express their thoughts and feelings. However, Arabic Sign Language (ArSL) still suffers from a distinct lack of scientific research and documented digital databases. This limitation hinders the development of automated recognition systems and weakens their integration into modern artificial intelligence applications. This research aims to review and analyze previous studies related to the use of various databases and sensor technologies in Arabic Sign Language recognition. The study focuses on identifying shortcomings in previous research efforts and proposing methodological approaches to improve data collection and unify standards among researchers. This study relied on an analytical review of scientific literature to extract conclusions and evaluate the effectiveness of previously developed systems in terms of accuracy and efficiency. The results reveal that available databases lack diversity in vocabulary and grammatical structure, which reduces the ability of models to accurately recognize signs. The findings emphasize the importance of developing comprehensive and standardized databases that support the training of intelligent systems, thereby contributing to the integration of deaf individuals into society and improving their educational and social opportunities. Mon, 01 Dec 2025 00:00:00 GMT https://opendata.uni-halle.de//handle/1981185920/124815 2025-12-01T00:00:00Z