Integration Of Tools Research Articles

Enhancing business performance: The role of data-driven analytics in strategic decision-making

In today’s highly competitive business landscape, organizations are increasingly turning to data-driven analytics to enhance performance and inform strategic decision-making. This approach leverages vast amounts of data, transforming it into actionable insights that drive efficiency, innovation, and growth. The role of data-driven analytics is multifaceted, encompassing predictive, prescriptive, and descriptive analytics, each contributing uniquely to the decision-making process. Predictive analytics forecasts future trends and behaviors, enabling proactive strategies. Prescriptive analytics provides recommendations for optimal actions based on various scenarios, while descriptive analytics offers a clear view of past performance, helping to identify patterns and opportunities for improvement. The integration of advanced data analytics tools and techniques has revolutionized how businesses operate, particularly in areas such as marketing, finance, supply chain management, and customer relationship management. By utilizing data analytics, companies can identify market trends, understand customer preferences, optimize operations, and reduce costs. For example, in marketing, data-driven insights allow for targeted campaigns and personalized customer experiences, leading to higher conversion rates and customer loyalty. In supply chain management, analytics improve demand forecasting and inventory management, enhancing overall efficiency. Furthermore, the adoption of data-driven analytics fosters a culture of evidence-based decision-making within organizations. Leaders can make informed decisions quickly, reducing the risks associated with gut-feel approaches and ensuring that strategies are aligned with market realities and organizational goals. This shift towards analytics-based decision-making is supported by advancements in artificial intelligence and machine learning, which enhance the accuracy and depth of insights generated from data. However, the journey to becoming a data-driven organization is not without challenges. It requires significant investments in technology, talent, and change management. Organizations must also address data privacy and security concerns to maintain stakeholder trust. Despite these challenges, the benefits of data-driven analytics in strategic decision-making are undeniable. It empowers businesses to navigate complexities, seize opportunities, and achieve sustainable competitive advantages. In conclusion, data-driven analytics is a critical enabler of enhanced business performance. Its strategic application in decision-making processes not only drives operational excellence but also positions organizations for long-term success in a dynamic market environment. Keywords: Decision-Making, Data-Driven, Analytics, Business Performance, Strategic.

‘I was content just staying within my own circle’: interrogating the relationship between sport and acculturation among Chinese international students at a Canadian university

ABSTRACT The aim of this study is to understand the sport and physical activity experiences of Chinese international students within a large Canadian public university in Toronto, Canada. This aim was addressed by conducting semi-structured interviews with nine Chinese international students enrolled at this university and analyzing the data through acculturation theory. The study found that sport was primarily utilized for helping participants maintain connections to their culture; an outcome of the acculturation process classified as separation (Berry, 2005), by helping them build a sense of community with fellow Chinese international students. We argue that creating such spaces for sport participation was instrumental to helping the participants navigate the dominant campus culture. Although there was also some evidence that sport created opportunities for the participants to interact with Canadian students, they experienced institutional and cultural challenges that prevented them utilizing sport to connect with, and feel a part of the broader university community. Based on these findings, we argue that sport can potentially be a useful tool for international students to maintain connections to their cultural community, though this is dependent on the demographics of such a community within each individual university campus. And while sport is often promoted as an effective tool for integration amongst international students, engaging in sport or physical activity with domestic students does not, on its own, result in the achievement of such goals and is insufficient without accounting for broader cultural challenges and barriers.

An Intelligent Transport System Using Vehicular Network for Smart Cities

Aims and background: The integration of communication tools has allowed for effective decision-making in smart cities and Internet-of-Things (IoT). One major issue that people who commute to cities everyday encounter is traffic congestion. Thanks to the progress and backing of ICTs, transportation solutions have been designed and implemented, leading to the development of ITSs and the provision of numerous innovative services. Objectives and Methods: These services include ones that guarantee safety, provide drivers with useful information, enable greater street movement, and avoid congestion, among many others. In industrialized nations, traffic data is collected by specialized sensors that can anticipate future patterns. Commuters are kept informed of any traffic updates through the Internet. When there is little or no physical infrastructure and Internet connection, these methods become unworkable. Internet access is still a problem in rural regions, and there are no roadside units in underdeveloped nations. This article presents an architecture for smart cities' intelligent vehicular networks that can impromptu accept data from nearby vehicles in real time and use it to choose routes. As embedded devices in vehicles, we utilized Android-based smartphones with Wi-Fi Direct capabilities. To set up our smart transportation system, we utilized a vehicular ad hoc network. Results: Data was collected and processed using separate methods between two major cities in a developing nation. Resource utilization, transmission delay, packet loss, and total trip time were measured against several fixed- and dynamic-route-selection algorithms to assess the framework's performance. Conclusions: When equated to a conventional fixed-route-selection procedure, our results reveal a 33.3% reduction in trip times.

An Object Based Decision Support Protocol for Multi- Constraint Agile Digital Forensics Investigation

The recent scenarios expressing live digital forensics and its applications in security domain with multiple constraint-based analysis. Many digital forensic tools available in the market helps to make a good analysis of various digital forensic situations but the awareness of the situation-based decision making is still an unsolvable issue in many critical forensic cases. As the digital forensic domain increases its scope to various business and computer related industries, leading market investors and the employees are much aware about the digital crimes and its prevention. Here in this article, we proposed a new protocol which can help the decision making of sensitive digital forensic cases by the involvement of various constraint-based evaluation. A multi constraint operational system proposed here for live digital forensic and the decision support system takes an object parameter while the decision-making procedure is implemented. Thus, the protocol introduced here may carry a set of rules where the object formation and decision support constraints bind together and it may reply on the digital forensic tool integration. The characteristics of this protocol is mainly distributed for situation awareness criterion evaluation and modelled to limit the amount of data retrieval and its acquisition. categorical acquisition of digital evidences through time, duration, devices involved, wings of affect, cause of the target, hacker/attacker/affected user, kind of attack are the constraints to be solved through this prototype. The focus of this prototype is for dynamic digital forensics instead classic forensics conducted on switched off devices. This protocol supports the standard organizational formats to follow the forensic procedures in which a detailed recommendations and documentation from various organizations. The standardized formats of National Institute of Standards and Technology (NIST), International Standards Organization (ISO), Global Professional Information Community (AIIM) and American Society for Testing and Materials (now ASTM International) into considered while developing this protocol.

Digital Measures Development: Lessons Learned from an Expert Workshop Addressing Cross-Therapeutic Area Measures of Sleep

Introduction: The Digital Measures Development: Core Measures of Sleep project, led by the Digital Medicine Society (DiMe), emphasizes the importance of sleep as a cornerstone of health and the need for standardized measurements of sleep and its disturbances outside the laboratory. This initiative recognizes the complex relationship between sleep and overall health, addressing it as both a symptom of underlying conditions and a consequence of therapeutic interventions. It aims to fill a crucial gap in healthcare by promoting the development of accessible, nonintrusive, and cost-effective digital tools for sleep assessment, focusing on factors important to patients, caregivers, and clinicians. Methods: A central feature of this project was an expert workshop conducted on April 19th, 2023. The workshop convened stakeholders from diverse backgrounds, including regulatory, payer, industry, academic, and patient groups, to deliberate on the project’s direction. This gathering focused on discussing the challenges and necessities of measuring sleep across various therapeutic areas, aiming to identify broad areas for initial focus while considering the feasibility of generalizing these measures where applicable. The methodological emphasis was on leveraging expert consensus to guide the project’s approach to digital sleep measurement. Results: The workshop resulted in the identification of seven key themes that will direct the DiMe Core Digital Measures of Sleep project and the broader field of sleep research moving forward. These themes underscore the project’s innovative approach to sleep health, highlighting the complexity of omni-therapeutic sleep measurement and identifying potential areas for targeted research and development. The discussions and outcomes of the workshop serve as a roadmap for enhancing digital sleep measurement tools, ensuring they are relevant, accurate, and capable of addressing the nuanced needs of diverse patient populations. Conclusion: The Digital Medicine Society’s Core Measures of Sleep project represents a pivotal effort to advance sleep health through digital innovation. By focusing on the development of standardized, patient-centric, and clinically relevant digital sleep assessment tools, the project addresses a significant need in healthcare. The expert workshop’s outcomes underscore the importance of collaborative, multi-stakeholder engagement in identifying and overcoming the challenges of sleep measurement. This initiative sets a new precedent for the integration of digital tools into sleep health research and practice, promising to improve outcomes for patients worldwide by enhancing our understanding and measurement of sleep.

Impact of Digital Transformation on Strategic Marketing Practices in Germany

Purpose: The aim of the study was to examine the impact of digital transformation on strategic marketing practices in Germany. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The impact of digital transformation on strategic marketing practices in Germany has been profound and multifaceted, driving significant changes in how businesses engage with their customers and operate in the market. The adoption of advanced digital technologies such as artificial intelligence, big data analytics, and social media platforms has enabled companies to develop more personalized, efficient, and data-driven marketing strategies. This transformation has facilitated improved customer targeting, enhanced engagement, and greater insights into consumer behavior, leading to more effective and responsive marketing campaigns. Moreover, digital transformation has encouraged a shift from traditional marketing methods to innovative, omnichannel approaches, allowing German businesses to reach a broader audience and provide a seamless customer experience. The integration of digital tools has also streamlined marketing operations, reduced costs, and increased the agility and adaptability of marketing teams in responding to market changes and consumer demands. Unique Contribution to Theory, Practice and Policy: Resource-Based View Theory, Diffusion of Innovations Theory & Dynamic Capability Theory may be used to anchor future studies on Impact of Digital Transformation on Strategic Marketing Practices in Germany. Organizations are encouraged to invest in continuous learning and development programs aimed at enhancing digital marketing capabilities among employees. This entails providing training, workshops, and access to resources that enable marketers to stay abreast of the latest digital trends, tools, and techniques. There is a need to advocate for the development of policies and regulations that foster innovation, competition, and consumer protection in digital marketing environments. This involves engaging policymakers and stakeholders to create an enabling regulatory framework that encourages digital innovation while addressing concerns related to data privacy, cybersecurity, and ethical marketing practices.

Introduction on tritium transport analysis model for HCCP breeding blanket system

In the context of implementing the He-cooled breeding blanket system for a fusion reactor, several crucial aspects need consideration. Among these, the development of a tritium transport model plays a pivotal role in ensuring safety and effective design. Given that tritium release into the environment from the breeding blanket system poses a radioactive risk, accurate calculation and prediction area essential to prevent potential incidents in a fusion reactor. To address this challenge, a collaborative effort between the Korea Institute of Fusion Energy (KFE) and the University of California, Los Angeles (UCLA) resulted in the creation of THETA-FR (Tritium/Hydrogen Enhanced dynamic Transport Analysis Tool for Fusion Reactor). THETA-FR is an integrated analysis tool that combines Matlab Simulink and COMSOL Multiphysics. Its purpose is to model and predict the dynamic transport phenomena of H isotopes (H/D/T) within the breeding blanket system. Specifically, THETA-FR focuses on transient tritium retention, permeation, and release amounts from the breeding blanket system into the environment. In this paper, we introduce the integrated system and components of THETA-FR, shedding light on various tritium behaviors within the HCCP breeding blanket FW/BU component. By leveraging THETA-FR, researchers and engineers can make informed decisions regarding tritium control and safety in fusion reactors.

Exploring Pedagogical and Digital Practices in Vocational Education and Training: Comparing Teacher and Student Perspectives

This article investigates the pedagogical and digital practices of vocational education and training teachers, offering comparative insights from both teacher and student perspectives. To this end, two questionnaires were constructed for teachers and students and then validated by means of inter-judge procedures and expert review. The questionnaires were administered to representative samples of 158 teachers and 309 students in intermediate and higher vocational education and training programs in the Spanish region of Catalonia. Thanks to institutional collaboration, it was possible to obtain both samples by a non-probability sampling technique. The results obtained from the quantitative analyses performed show that while most vocational education and training teachers utilize a combination of active and lecture-based methods, less than a third incorporate online tools, prioritizing institutional platforms. Although both teachers and students acknowledge a diversity of teaching strategies, the latter, particularly female students and those in advanced years or on dual programs, tend to be more critical of the effectiveness of the teaching strategies employed compared to their teachers. The findings underscore the need for vocational education and training programs to enhance digital tool integration and address the varying perceptions of teaching effectiveness to better meet students’ expectations and needs.

Empirical insights into AI-assisted game development: A case study on the integration of generative AI tools in creative pipelines

<p>This study conducts an empirical exploration of generative Artificial Intelligence (AI) tools across the game development pipeline, from concept art creation to 3D model integration in a game engine. Employing AI generators like Leonardo AI, Scenario AI, Alpha 3D, and Luma AI, the research investigates their application in generating game assets. The process, documented in a diary-like format, ranges from producing concept art using fantasy game prompts to optimizing 3D models in Blender and applying them in Unreal Engine 5. The findings highlight the potential of AI to enhance the conceptualization phase and identify challenges in producing optimized, high-quality 3D models suitable for game development. This study reveals the current limitations and ethical considerations of AI in game design, suggesting that while generative AI tools hold significant promise for transforming game development, their full integration depends on overcoming these hurdles and gaining broader industry acceptance.<strong></strong></p>

Generalized nonlinearity in animal ecology: Research, review, and recommendations.

Generalized linear models (GLMs) are an integral tool in ecology. Like general linear models, GLMs assume linearity, which entails a linear relationship between independent and dependent variables. However, because this assumption acts on the link rather than the natural scale in GLMs, it is more easily overlooked. We reviewed recent ecological literature to quantify the use of linearity. We then used two case studies to confront the linearity assumption via two GLMs fit to empirical data. In the first case study we compared GLMs to generalized additive models (GAMs) fit to mammal relative abundance data. In the second case study we tested for linearity in occupancy models using passerine point-count data. We reviewed 162 studies published in the last 5 years in five leading ecology journals and found less than 15% reported testing for linearity. These studies used transformations and GAMs more often than they reported a linearity test. In the first case study, GAMs strongly out-performed GLMs as measured by AIC in modeling relative abundance, and GAMs helped uncover nonlinear responses of carnivore species to landscape development. In the second case study, 14% of species-specific models failed a formal statistical test for linearity. We also found that differences between linear and nonlinear (i.e., those with a transformed independent variable) model predictions were similar for some species but not for others, with implications for inference and conservation decision-making. Our review suggests that reporting tests for linearity are rare in recent studies employing GLMs. Our case studies show how formally comparing models that allow for nonlinear relationships between the dependent and independent variables has the potential to impact inference, generate new hypotheses, and alter conservation implications. We conclude by suggesting that ecological studies report tests for linearity and use formal methods to address linearity assumption violations in GLMs.

The role of digital transformation in business and its impact on sustainable development goals (sdgs) in the tourism sector

Objective: The objective of this paper is to examine the role of digital transformation in enhancing sustainability within the tourism industry and its impact on achieving the Sustainable Development Goals (SDGs). The study aims to analyze how digital technologies can foster economic growth, social inclusivity, and environmental responsibility, thereby contributing to a more sustainable and resilient tourism sector. Method: The research employs a comprehensive literature review methodology, drawing on case studies, industry reports, and empirical evidence. Key areas of focus include the integration of digital tools in tourism operations, such as online booking platforms, customer relationship management systems, data analytics, and Internet of Things (IoT) applications. The study also explores the application of augmented reality (AR), virtual reality (VR), blockchain technology, and smart destination management in promoting sustainable tourism practices. Results: The findings indicate that digital transformation significantly enhances operational efficiency, customer experience, and overall competitiveness in the tourism industry. Digital technologies enable data-driven decision-making, efficient resource management, and real-time monitoring of environmental impacts. The integration of digital platforms promotes eco-friendly practices, supports financial inclusion through digital payments, and facilitates stakeholder engagement. Moreover, virtual tourism and AR/VR technologies provide immersive experiences that reduce the environmental impact of physical travel while educating tourists about conservation and sustainability. Conclusions: Digital transformation is a powerful tool for advancing sustainability in the tourism sector. By leveraging digital technologies, businesses can align their operations with the SDGs, promoting responsible consumption, reducing environmental impact, and fostering inclusive economic growth. The study underscores the importance of a balanced approach where digital transformation drives economic benefits while ensuring social and environmental responsibility. Collaboration among stakeholders, including businesses, governments, and local communities, is essential for creating a sustainable and resilient tourism industry. The ongoing commitment to innovation and responsible practices will play a pivotal role in shaping a sustainable future for the tourism sector.

Machine learning-based prediction of clinical outcomes after traumatic brain injury: Hidden information of early physiological time series.

To assess the predictive value of early-stage physiological time-series (PTS) data and non-interrogative electronic health record (EHR) signals, collected within 24 h of ICU admission, for traumatic brain injury (TBI) patient outcomes. Using data from TBI patients in the multi-center eICU database, we focused on in-hospital mortality, neurological status based on the Glasgow Coma Score (mGCS) motor subscore at discharge, and prolonged ICU stay (PLOS). Three machine learning (ML) models were developed, utilizing EHR features, PTS signals collected 24 h after ICU admission, and their combination. External validation was performed using the MIMIC III dataset, and interpretability was enhanced using the Shapley Additive Explanations (SHAP) algorithm. The analysis included 1085 TBI patients. Compared to individual models and existing scoring systems, the combination of EHR and PTS features demonstrated comparable or even superior performance in predicting in-hospital mortality (AUROC = 0.878), neurological outcomes (AUROC = 0.877), and PLOS (AUROC = 0.835). The model's performance was validated in the MIMIC III dataset, and SHAP algorithms identified six key intervention points for EHR features related to prognostic outcomes. Moreover, the EHR results (All AUROC >0.8) were translated into online tools for clinical use. Our study highlights the importance of early-stage PTS signals in predicting TBI patient outcomes. The integration of interpretable algorithms and simplified prediction tools can support treatment decision-making, contributing to the development of accurate prediction models and timely clinical intervention.

Artificial intelligence and project management: An empirical investigation on the appropriation of generative Chatbots by project managers

The integration of generative AI tools, such as chatbots, into project management is revolutionizing the field. This paper explores how project managers are adopting and adapting these tools, specifically focusing on ChatGPT, for enhanced project management. Using Adaptive Structuration Theory, the study examines project managers' appropriation of generative AI. It considers factors like Innovation Attitude, Peer Influence, and Task-Technology Fit, employing a survey of Italian project managers. The approach adopted to analyze data is based on Partial Least Square - Structural Equation Modeling. The research confirms the significance of the hypothesized antecedents in AI tool appropriation. Innovation Attitude and Peer Influence are shown to positively impact the creative and 'unfaithful' use of AI in project management. Task-Technology Fit is crucial for effective AI integration, impacting both creative behaviour and unfaithful appropriation. The study highlights the role of an innovative mindset, peer dynamics, and task compatibility in the effective use of AI tools in project management. It suggests potential areas for future research, including exploring cultural and organizational contexts and the rapid evolution of AI technologies.

Sustainable business intelligence solutions: Integrating advanced tools for long-term business growth

In today's rapidly evolving business landscape, sustainability has become a critical factor for long-term success. Sustainable Business Intelligence (BI) solutions offer a strategic approach for organizations to integrate advanced tools that not only enhance operational efficiency but also drive long-term growth while minimizing environmental impact. This review provides an overview of the concept of Sustainable BI and outlines its key components, integration of advanced tools, case studies, best practices, challenges, and future trends. Sustainable BI entails the application of BI principles and practices to support sustainable business practices, including environmental stewardship, social responsibility, and economic viability. By collecting, analyzing, and visualizing data from diverse sources, organizations gain insights into their environmental footprint, social impact, and economic performance (Oyinkansola, 2024). Advanced tools such as predictive analytics, machine learning, IoT integration, and blockchain technology further enhance the capabilities of Sustainable BI solutions, enabling organizations to make data-driven decisions that optimize resource usage, reduce waste, and improve overall sustainability performance. Through case studies of companies implementing Sustainable BI, we highlight successful strategies and outcomes achieved through the integration of advanced tools. Best practices for Sustainable BI adoption, including leadership commitment, cross-functional collaboration, and ethical considerations, are discussed to guide organizations in their implementation journey. Additionally, we explore challenges such as data privacy, scalability, and emerging trends shaping the future of Sustainable BI. In conclusion, Sustainable BI represents a holistic approach to business intelligence that aligns with the principles of sustainability and offers immense opportunities for organizations to achieve long-term growth while contributing positively to society and the environment.

SOCIAL AND PSYCHOLOGICAL RESOURCES OF CHILDREN WITH SPECIAL EDUCATIONAL NEEDS (DIAGNOSTIC CONTEXT IN BULGARIA AND RUSSIA)

Background. The paper describes ways of diagnosing social and psychological resources of children with special needs. The authors propose to study the diagnostic capabilities of socialization of children with special needs as a unique integrative tool in the work of specialists getting in touch with children with special needs (inclusive education, special education). Two aspects of diagnostics of socialization of preschool children with special needs in Bulgaria and Russia who are provided with inclusive education according to official procedures are considered. The research hypothesis is the assumption of inclusion of multiple characteristics of socialization of preschool children, which can serve as resources for successful social adaptation of children with special needs. The purpose of the study is to structure the review based on the grouping of parameters related to the characteristics of adaptive socialization resources in the context of meeting special needs. Methodology. The study was carried out in accordance with the theoretical and methodological principles of psychology and pedagogy and the special methodology of psychological science (the principle of personal approach), the following methods were used (analysis; synthesis; abstraction and specification; comparison and generalization; method of induction and deduction, method of analogies; modeling). The results are based on introductory studies of official recommendations for research on socialization of children with special needs in earlier studies conducted in both countries. It is shown that most of the target socialization scales are the same and are reflected in the predictive research procedure. In addition, it has been proven that in Bulgaria the scales are more substantive about the child's conditions and developmental situation, while in Russia they refer to the child's personal and socio-psychological indicators. In Bulgaria, there is no tendency to emphasize the functions of socialization areas, while in Russia, a number of diagnostic tests emphasize the socialization areas of the preschool child with special needs. Practical implications. This Russian peculiarity allows us to refer to the method of prediction level measurement as an integral and unique tool which includes socialization parameters through refraction of personal characteristics of a child with special needs in various spheres of his/her relations (parents, adults, peers) and ways of organization of activity (organized and free), i.e. conditions of social development. In conclusion, in both countries it is necessary to redirect diagnostic methods to more integrative methods of work. In Bulgaria, more attention needs to be paid to comprehensiveness, and in Russia, the specificity of developmental disorders must be monitored.

Curiosity in Science Learning: A Systematic Literature Review

This review systematically examines the role of curiosity in science learning, focusing on its influence on engagement, cognitive processes, and educational outcomes. A comprehensive literature search was conducted using databases such as Scopus, resulting in the selection of 40 peer-reviewed empirical studies published between 2019 and 2024. Inclusion and exclusion criteria ensured the relevance and rigor of the selected studies. The findings reveal that curiosity significantly enhances memory encoding and consolidation, motivates learners, and supports exploratory behavior. Specifically, studies indicated that curiosity-driven learning leads to improved attention and learning processes, resulting in better educational outcomes. Pedagogical strategies that foster curiosity, including the integration of large language models and innovative teaching tools, have been shown to effectively enhance student engagement and learning in science. Quantitative data from the reviewed studies demonstrate that curiosity-driven approaches lead to a 25% increase in student engagement and a 30% improvement in learning outcomes. The review underscores the importance of systematically integrating curiosity-enhancing strategies within pedagogy to create more engaging and effective educational experiences. Future research should focus on developing comprehensive measurement tools for assessing curiosity and conducting longitudinal studies to explore its long-term impact on academic achievement and skill development. Additionally, the exploration of modern technologies in enhancing curiosity-driven learning should be prioritized. Educators are encouraged to implement pedagogical strategies that stimulate curiosity to maintain and enhance student engagement and achievement in science learning.

Unveiling Perspectives: Exploring Student Perception on Artificial Intelligence in Academics in Rawalpindi, Pakistan

Artificial intelligence characterized as computing systems that perform tasks that humans do, such as learning, adapting, and synthesizing, as well as self-correcting and utilizing data for complex processing jobs. Integration of artificial intelligence into education has received much attention because it has the potential to revolutionize current approaches to teaching and learning this study has to explore student’s perception toward integration of Artificial intelligence tools in their academic experiences and also to explore the impact on their learning outcomes. For this study quantitative research approach has been used. A sample of 200 students selected from all the departments in the faculty of social sciences of Pir Mehr Ali Shah Arid Agriculture University Rawalpindi. Systematic sampling technique was used for data collection. In order to collect the data questionnaire was used. This research has been extracted from the M.Phil Dissertation. The study revealed that students who use artificial intelligence tools perform better on research work, examinations, assignments, presentations and overall coursework. As a result, artificial intelligence tools assist students in their learning processes and critical thinking. It develops their academic abilities, research work skills, academic grades, and self-efficacy. This study demonstrates that integrating artificial intelligence tools enhances students' academic performance, and self-efficacy. The results indicated that AI tools changed the way students learned and behaved academically.

Comprehensive analysis of human and AI task allocation in the education sector: Defining futuristic roles and responsibilities

This research explores the integration of AI chatbots in higher education, examining their impact on teaching practices, learning outcomes and student engagement. AI's ability to predict and adapt offers transformative potential for educational environments, shifting skill demands and enhancing accessibility. Using the Technological Pedagogical Content Knowledge (TPACK) framework, we assess the integration of AI tools by educators. At the same time, the Technology Acceptance Model (TAM) gauges teacher’s perceptions and Constructivist Learning Theory (CLT) explores AI's support for interactive, student-centered learning. Our developed framework includes the roles and responsibilities of human educators and AI chatbots, strategies for maximizing educational benefits and addressing challenges and best practices for fostering human-AI partnerships and promoting inclusivity. The findings reveal that AI chatbots can significantly reduce administrative burdens on educators, offer personalized learning experiences, and enhance real-time feedback. This study underscores the importance of teacher training and ethical considerations in AI integration. Ultimately, we aim to contribute to the effective integration of AI in education, promoting a balanced synergy between human and technological efforts to enhance educational practices.

The Power of ZPD: Enhancing Teaching and Learning

The purpose of this research is to examine the educational implications of Vygotsky's (1978) notion of the Zone of Proximal Development, originally developed to account for the learning potential of children, and investigate ZPD applications in teaching and learning. Literature-based approach is used to gather the data. This research review represents the meaning of ZPD (Zone of Proximal Development), discusses the implications of ZPD for teaching and learning, and then classroom strategies. This paper presented the diverse applications of the ZPD in academic settings, highlighting its capacity to enhance instructional strategies and promote the development of learners. To create inclusive learning environments where every student can reach their full potential, educators are advised to use formative assessment practices, scaffolding techniques, peer collaboration promotion, differentiation of instruction, and technology tool integration.

THE HISTORY OF USING INFORMATION TECHNOLOGIES IN PHYSICAL EDUCATION AND SPORTS

This article examines the historical development and implementation of information technologies in physical education and sports. Through comprehensive analysis of scientific literature and online sources, the study explores early innovations in computer programs and software applications designed for biomechanical analysis, monitoring of physical condition, training modeling, and individualized program creation in sports and fitness. Key contributions from researchers at the National University of Physical Education and Sports of Ukraine are highlighted, including significant developments in posture analysis, foot diagnostics, and video-based motion capture systems. The paper thoroughly reviews specialized software for professional bodybuilding, aqua aerobics, family health management, and student physical education, demonstrating the breadth of applications across various domains. A detailed classification system for information technologies in physical culture and sports is presented, categorizing applications by areas such as sports training, physical education, and rehabilitation. The research demonstrates the widespread adoption of information technologies across diverse aspects of physical education and sports, enabling sophisticated biomechanical assessments, comprehensive physical state monitoring, advanced training simulations, and personalized program development. The authors conclude that information technologies have become integral tools in enhancing physical education and sports practices, with significant potential for further implementation in higher education curricula. The study also emphasizes the need for continued research and development in this rapidly evolving field, particularly in areas such as virtual reality applications and mobile technologies for promoting physical activity and health.