globusjournal

The rapid advancement of Artificial Intelligence (AI) is fundamentally reshaping contemporary education by transforming teaching practices, learning experiences, and assessment systems. AI-enabled technologies such as intelligent tutoring systems, learning analytics, adaptive learning platforms, and automated assessment tools are increasingly integrated into educational environments to enhance personalization, efficiency, and learning effectiveness. This study examines the role of Artificial Intelligence in education with a focus on its transformative impact on teaching, learning, and assessment processes. Drawing on technology-enhanced learning and data-driven education frameworks, the study explores how AI supports instructional decision-making, facilitates learner-centered and adaptive learning pathways, and enables continuous, formative, and scalable assessment practices. Using a conceptual and empirical synthesis approach, the study analyzes existing evidence on AI-driven pedagogical innovations, learner engagement, and performance monitoring mechanisms. The study contributes to the growing discourse on AI in education by offering an integrated perspective on its pedagogical potential and implementation challenges. The findings provide practical implications for educators, institutional leaders, and policymakers seeking to harness AI responsibly to improve educational quality, equity, and learning outcomes.

Keywords: Artificial Intelligence, Educational Technology, Adaptive Learning, Intelligent Tutoring Systems, Learning Analytics, AI-Based Assessment, Teaching Innovation, Personalized Learning.

Short-video platforms (e.g., Douyin, Kuaishou) have become central to media use among China’s “silver-haired” users. While algorithmic personalization increases perceived relevance and enjoyment, it may also encourage excessive use. This study proposes and tests a mediated-moderated model in which immersion (flow), mood repair/loneliness alleviation, and cue-reactivity mediate the association between perceived algorithmic personalization and excessive short-video use, while digital literacy, family support/joint media engagement, and urban-rural context moderate these pathways. Using a multi-site survey of Chinese older adults (≥60; N ≈ 800) with device-logged usage and partial least squares structural equation modeling (PLS-SEM), we estimate direct, indirect, and conditional effects; qualitative interviews (n ≈ 40) deepen interpretation. We expect personalization to predict excessive use predominantly via immersion and mood repair, with buffering by higher digital literacy and stronger family support. Findings aim to inform dignity-preserving, family-integrated interventions that rebalance use without sacrificing valued social functions.

Keywords: Older Adults, China, Algorithmic Personalization, Immersion, Loneliness, Digital Literacy, Family Support, PLS-SEM.

The rapid evolution of Artificial Intelligence (AI) has transformed the landscape of consumer engagement, enabling businesses to deliver highly personalized experiences based on rich data insights. This study explores the strategic role of AI in bridging the gap between data analytics and enhanced consumer experiences, focusing on its implications for personalization, customer loyalty, and competitive advantage. Drawing from an interdisciplinary framework that integrates marketing psychology, consumer behavior theory, and AI-driven data modeling, the research examines how machine learning algorithms, natural language processing, and predictive analytics translate vast volumes of consumer data into tailored marketing interventions. The study highlights the dual benefits of AI-powered personalization: improved customer satisfaction through relevant, timely, and context-specific interactions, and enhanced organizational performance through data-informed decision-making. It addresses critical challenges including data privacy, algorithmic transparency, and ethical considerations in personalization practices. This research underscores that the strategic integration of AI in personalization is not merely a technological upgrade but a paradigm shifts in the way organizations connect with and retain their customers in the digital economy.

Keywords: Artificial Intelligence (AI), Personalization, Data Analytics, Consumer Experience, Predictive Analytics, Customer Engagement, Marketing Strategy, Consumer Behavior.

Habitat fragmentation in tropical rainforests presents complex effects on insect communities and ecosystem function. We review experimental and observational studies -including the Amazon’s Biological Dynamics of Forest Fragments Project – highlighting negative impacts at local scales (α-diversity decline, disrupted trophic and pollination networks), while noting that fragmentation can, under certain conditions, increase β- and γ-diversity across landscapes. We discuss functional ecology, edge effects, disturbance regimes, and ecosystem services. We conclude by proposing integrated conservation strategies that reconcile patch size, connectivity, and functional diversity to protect insect-mediated processes.

Keywords: Habitat Fragmentation, Tropical Rainforests, Ecosystem.

With the rapid growth of cloud computing, efficient data management has become a critical factor in ensuring optimal performance, scalability, and cost efficiency. Organizations increasingly rely on cloud-based solutions to store, process, and analyze vast amounts of data, requiring innovative strategies to balance computational power with resource allocation. This study explores key methodologies for optimizing cloud data management, including data partitioning, automated load balancing, storage tiering, and resource orchestration. It examines the impact of distributed storage architectures, real-time data processing, and AI-driven automation on performance improvements. Furthermore, the research highlights cost optimization techniques, such as pay-as-you-go pricing models, deduplication, and compression strategies, to reduce operational expenses without compromising efficiency. By analyzing case studies and industry best practices, this study provides insights into how businesses can enhance their cloud infrastructure while maintaining flexibility, security, and cost-effectiveness.

Keywords: Cloud Data Management, Performance Optimization, Scalability, Cost Efficiency, Distributed Storage, Load Balancing, Resource Orchestration, Data Partitioning, AI in Cloud Computing, Cloud Cost Optimization.

Blended learning, the combination of traditional face-to-face instruction with digital technologies, has gained widespread adoption in educational settings globally. This approach allows for flexibility and enhanced learning opportunities, integrating technological resources with classroom instruction. However, the availability and effectiveness of these resources significantly impact the success of blended learning models. This paper assesses the availability and effectiveness of technological resources in blended learning environments, considering factors such as access to technology, teacher and student preparedness, infrastructure, and the role of institutional support. Through a review of existing literature and data from case studies, this paper aims to provide insights into how technological resources are leveraged in blended learning and the challenges that affect their effective use.

Keywords: Blended Learning, Enhanced Learning Opportunities, Integrating Technological Resources, Classroom Instruction.

The transition from traditional classroom settings to virtual learning environments (VLEs) has necessitated a rethinking of effective teaching practices and engagement strategies. This study explores various pedagogical approaches and strategies that impact student engagement and learning outcomes in VLEs. Utilizing data, including survey results and performance metrics, we identify key factors that enhance virtual teaching and provide practical recommendations for educators to optimize virtual teaching practices.

Keywords: Virtual Learning Environment, Teaching Practices and Engagement Strategies.

The growing reliance on battery-powered systems in electric vehicles (EVs), renewable energy storage, and portable electronics necessitates efficient and reliable battery management. Fault detection in batteries is a critical aspect of ensuring safety, performance, and longevity. Traditional diagnostic methods often fall short in real-time adaptability and accuracy. This paper presents an intelligent approach to battery fault detection using machine learning (ML) techniques. By analyzing real-time data such as voltage, current, temperature, and state of charge (SoC), machine learning algorithms can learn complex patterns and accurately classify various types of faults, including overcharging, thermal runaway, and internal short circuits. The study evaluates supervised and unsupervised ML models, such as Support Vector Machines (SVM), Random Forests, and Neural Networks, for their effectiveness in early fault diagnosis. The proposed method enhances predictive maintenance strategies, reduces operational risks, and contributes to the advancement of smart Battery Management Systems (BMS). This work underscores the potential of integrating intelligent analytics with energy storage systems for safer and more efficient energy solutions.

Keywords: Battery Management System, BMS, Machine Learning, ML, Fault Detection, Predictive Maintenance, Intelligent Diagnostics, Energy Storage, Electric Vehicles.

This study examines the comparative impact of traditional and AI-powered teaching methods on motor skill development in physical education. The research evaluates how AI-based interventions, including real-time feedback and personalized training regimens, compare with conventional instruction in developing key physical attributes such as strength, agility, and coordination. A mixed-methods approach, incorporating both quantitative performance assessments and qualitative feedback from students and instructors, was employed. The findings suggest that AI-assisted programs provide significant advantages in agility and coordination, whereas traditional training retains superiority in fostering strength and motivational aspects. The results highlight the potential for an integrated approach, combining the benefits of both methodologies to optimize physical education outcomes.

Keywords: Physical Education, AI-Assisted Programs, Motor Skill Development, Traditional Teaching Methods.

The construction industry plays a critical role in global environmental sustainability, yet it is also a major contributor to resource depletion and waste generation. This study explores the integration of recycling materials and sustainable construction techniques to mitigate environmental impacts and promote eco-friendly practices. By utilizing recycled materials such as reclaimed wood, recycled concrete, and industrial by-products, the industry can significantly reduce waste and conserve natural resources. Additionally, sustainable construction methods such as modular building, green roofing, and energy-efficient design contribute to minimizing carbon footprints and enhancing building longevity. The paper highlights case studies of successful implementation and discusses the economic, social, and ecological benefits of these approaches. Emphasizing the importance of innovation and adherence to sustainable practices, this study aims to provide insights into creating a greener and more resource-efficient construction sector.

Keywords: Sustainable Construction, Materials Recycling, Green Building Techniques, Eco-Friendly Practices, Environmental Impact, Construction Sustainability.