Artificial Intelligence Integration Into Education for Underserved Communities

Artificial Intelligence (AI) is currently one of the most talked-about technologies, both among scientists and in public media. Several factors have contributed to its development in recent years. The first is access to vast quantities of data, such as in the industrial field, the advent of Industry 4.0, which promotes automation and data sharing in several technologies. Another factor is the continuous improvement in computing power thanks to the development of ever more powerful processors and the optimization of algorithms. With these two limitations removed, the focus of most AI developments is on the quality of predictions. The integration of AI into the industrial domain represents an exciting new frontier for innovation. Just as AI has transformed many other sectors, its application to mechanical technologies enables significant improvements in design, manufacturing and quality control processes: from computer-aided design (CAD) to printing parameter optimization, defect detection and real-time monitoring. This type of technology requires computer systems, data with management systems and advanced algorithms which can be used by AIs. In mechanical engineering, AI offers many possibilities in mechanical construction, predictive maintenance, plant monitoring, robotics, additive manufacturing, materials, vibration, etc. Methods and Applications of Artificial Intelligence is dedicated to the methods and applications of AI in mechanical engineering. Each chapter clearly sets out the techniques used and developed and accompanies them with illustrative examples. The book is aimed at students but is also a valuable resource for practicing engineers and research lecturers.

The COVID-19 pandemic has significantly impacted global healthcare systems, revealing vulnerabilities in disease surveillance, treatment capabilities, and emergency preparedness. The rapid transmission, severity, and economic disruption caused by the pandemic emphasized the urgent need for technological interventions. Artificial Intelligence (AI) emerged as a game-changer in pandemic response, providing automated solutions for early diagnosis, patient monitoring, drug discovery, and social distancing enforcement. AI-driven solutions played a crucial role in identifying infections early using advanced imaging techniques and predictive analytics. Machine learning models trained on radiographic images improved diagnostic accuracy, reducing the dependency on human expertise in overwhelmed healthcare facilities. AI-based epidemiological modeling assisted policymakers in predicting the spread of infections, enabling timely containment measures. Additionally, AI powered surveillance systems enforced social distancing regulations in public spaces, minimizing human intervention. In the healthcare sector, AI revolutionized patient care and hospital management by optimizing treatment plans, automating administrative tasks, and enhancing robotic assisted surgeries. AI-driven chatbots provided virtual consultations, reducing the burden on frontline healthcare workers. AI powered wearable devices enabled real-time patient monitoring, ensuring early detection of health deterioration. The integration of AI in drug discovery accelerated the search for COVID-19 treatments and vaccine development. AI analyzed vast biomedical datasets to identify potential antiviral compounds and optimize clinical trials, significantly reducing drug development timelines. Despite these advancements, several challenges persist, including data privacy concerns, bias infrastructure in AI models, and limitations. Ethical considerations, such as transparency in AI decision-making and fairness in healthcare AI applications, remain crucial for widespread adoption. This study explores the multifaceted role of AI in healthcare, particularly in pandemic management, highlighting its benefits, challenges, and future directions. AI has proven to be an indispensable tool in mitigating pandemics, and continuous innovations in AI applications will play a crucial role in enhancing healthcare resilience against future outbreaks.

Camera, 208 Capitalism, scepticism of policies on education in future based on society with influx of, 42 Carnegie Learning, 251-252 Central government, 92 Challenges to inclusion, 84-85 Change of practice concept, 253 Character qualities of sets in twenty-first century, 25-33 communicative competencies, 28-31 digital competencies, 31-33 'grit' for inclusive and special education teachers, 27-28 high self-efficacy, 25-27 Children, rights and characteristics of, 4-5 Children with disabilities in rural areas in Telangana state, providing services to, 174-177 259 Children with special needs in VUCA world, preparation for education administrators to manage education for, 241-244 Chlorpyrifos, 64-65 City local government, 93-99 Civil rights, 39-40 Classic economic liberal theory, 41-42 Classrooms with assistive technology, improving efficiency of, 250-252 Climate change, 64-65 Collaboration, 180 Collaborative learning, 23-24 Communication, 180 channels, 29-30 disabilities, 211 Communicative competencies, 28-31 Community resource persons (CRPs), 175 Competency-based education, 23-24 Complexity, 80-81 Comprehensive teaching strategies, 236 Computer-assisted tools, 128 Constitution of the Republic of Indonesia (1945), 89-90 Constructive Alignment model, 21 Convention on the Rights of Persons with Disabilities (CRPD), 159 Convention on the Rights of the Child (CRC), 158-159 Conversations, feedback and recognition (CFR), 225 Core educational subjects, 179 COVID-19, 18-19, 118-119, 123-124 crisis, 7-8 inclusive learning for students with special needs during, 108-110 learning experiences during, 114-116 learning experiences of KU

Technological progress, especially in the field of information, has led to reducing the gap between science, information and knowledge. This accelerating change has forced all educational institutions to comply with this new mission by using information technology in the teaching process, as artificial intelligence has become an integral part of the educational and learning process, and a means of Effective in the teaching process, in addition to being research tools in obtaining information and knowledge, the research problem focused on (what are the attitudes of the kindergarten teacher towards using artificial intelligence applications in the effective teaching process in the early education stage in the third Rusafa Education Directorate?), where the importance of The research is considered one of the researches that deals with an important and vital topic (artificial intelligence), as it gains its importance in that it represents the role that kindergarten teachers play in the early education stage in developing the skills and abilities and refining the information of the child through their use of artificial intelligence methods and applications, as well as The positive role of such research in stimulating kindergarten teachers’ motivation to benefit from artificial intelligence applications, which increases the efficiency and effectiveness of their teaching. The aim of the research is to reveal kindergarten teachers’ attitudes towards using artificial intelligence in the effective teaching process in the early education stage, as well as to reveal the most important The challenges facing kindergarten teachers using artificial intelligence in the effective teaching process, and are there any statistically significant differences between the average kindergarten teacher’s attitudes to using artificial intelligence in the effective teaching process in the early education stage according to the research variables? The research reached several conclusions, perhaps the most important of which is the existence of differences. There is statistical significance between the average kindergarten teacher’s attitudes to using artificial intelligence in the effective teaching process in the early education stage according to the research variables. The research recommends holding many workshops and training courses for kindergarten teachers on how to use artificial intelligence in effective teaching, and including programs and rehabilitation plans for kindergarten teachers. About the use of artificial intelligence according to the plans of the Ministry of Education, which are consistent with its vision, and encouraging teachers to proceed and benefit from applications and websites for safe and available artificial intelligence applications in the teaching process

Introduction: The integration of artificial intelligence (AI) into higher education is swiftly revolutionizing pedagogical methodologies, in conjunction with learning processes and research paradigms. The interdisciplinary potential of AI within academic settings was examined in this study, employing a case study conducted at the University of Tirana. Through the utilization of bibliometric analysis and survey-based research, this study comprehensively investigates the swiftly emerging trends in AI applications, students' significant familiarity with AI technologies, as well as the substantial challenges impeding broader adoption. The bibliometric analysis highlights significant exponential growth in AI research, particularly within pivotal domains such as finance and accounting, thereby emphasizing the swiftly increasing relevance of blockchain and automation. The survey indicates a robust enthusiasm among students for AI in educational settings. More than 90 percent of students actively incorporate AI tools in their project work. Nonetheless, resource limitations and ethical considerations, including privacy, data security, and algorithmic bias, pose considerable challenges to the widespread adoption of AI, despite the prevailing enthusiasm. Objective: The aim of this research is to examine the incorporation of Artificial Intelligence (AI) within the realm of higher education. This analysis concentrates on the applications, advantages, and challenges associated with AI, with the intent of advancing interdisciplinary research and educational methodologies at the University of Tirana. Theoretical Framework: This research expands upon the principles of AI adoption within the educational sector, alongside an examination of ethical considerations and multidisciplinary collaboration. Theories pertaining to technological integration and adaptive learning systems serve as the foundational framework for comprehending the implications of AI in the realm of education. Method: The methodology employs bibliometric analysis to examine AI-related research trends utilizing data from SCOPUS and conducts a survey to assess students' familiarity with and perceptions of AI. Data collection was facilitated through bibliometric instruments and an online survey incorporating Likert-scale and open-ended questions. Results and Discussion: The results underscore an increased focus on artificial intelligence (AI) and blockchain within scholarly research, wherein students exhibit considerable engagement and interest in AI applications. Nevertheless, limitations in resources and ethical issues, including privacy and bias, persist as primary challenges. The discourse underscores the imperative for investment in infrastructure and the incorporation of ethical education. Research Implications: This research highlights the imperative for higher education institutions to integrate artificial intelligence tools, cultivate adaptive curricula, and address ethical considerations in order to adequately prepare students for a future shaped by AI. The implications of these findings also pertain to educational policy and the formulation of interdisciplinary research strategies. Originality/Value: The study contributes by delivering a comprehensive bibliometric analysis and gives insights into student engagement with artificial intelligence. Its significance resides in presenting actionable recommendations to enhance the integration of artificial intelligence in higher education.

Artificial intelligence (AI) is transforming ophthalmology by leveraging machine learning (ML) and deep learning (DL) techniques, particularly artificial neural networks (ANN) and convolutional neural networks (CNN) to mimic human brain functions and enhance accuracy through data exposure. These AI systems are particularly effective in analyzing ophthalmic images for early disease detection, improving diagnostic precision, streamlining clinical workflows, and ultimately enhancing patient outcomes. This study aims to explore the specific applications and impact of AI in the fields of glaucoma, corneal diseases, and oculoplastics. This study reviews current AI technologies in ophthalmology, examining the implementation of ML and DL techniques. It evaluates AI's role in early disease detection, diagnostic accuracy, clinical workflow enhancement, and patient outcomes. AI has significantly advanced the early detection and management of various ocular conditions. In glaucoma, AI systems provide standardized, rapid identification of disease characteristics, reducing intra- and interobserver bias and workload. For corneal diseases, AI tools enhance diagnostic methods for conditions such as keratitis and keratoconus, improving early detection and treatment planning. In oculoplastics, AI assists in the diagnosis and monitoring of eyelid and orbital diseases, facilitating precise surgical planning and postoperative management. The integration of AI in ophthalmology has revolutionized eye care by enhancing diagnostic precision, streamlining clinical workflows, and improving patient outcomes. As AI technologies continue to evolve, their applications in ophthalmology are expected to expand, offering innovative solutions for the diagnosis, monitoring, treatment, and surgical outcomes of various eye conditions.

Introduction: In todays rapidly changing technology, artificial intelligence (AI) is changing the way we live, work and create art at an unprecedented speed. As an important part of human culture, dance art has also ushered in new development opportunities and challenges in this scientific and technological revolution. Objectives: This paper aims to explore the innovative application and influence of artificial intelligence (AI) in dance creation and performance. With the rapid development of AI technology, its application is increasingly wide in the field of art, and dance art is no exception Methods: This paper first outlines the application of AI technology in dance creation, including the generation of new dance movements through algorithms, auxiliary choreography, etc. These applications provide unprecedented creative sources and tools for dance creators. Then, this paper analyzes the role of AI in dance performance, such as the accurate movement performance of dance robot, the immersive experience of virtual reality technology in dance performance, etc. Results: These technologies not only enrich the form of dance performance, but also broaden the viewing experience of the audience. In addition, this paper also discusses the application of AI in dance teaching, such as intelligent guidance system, distance dance teaching, etc., which bring convenience and personalization to dance teaching. However, the application of AI in the field of dance also faces technical challenges and art ethical issues, which need further research and discussion. Conclusions: In conclusion, this paper believes that the application of AI technology in dance creation, performance and teaching has broad prospects, but it also needs to pay attention to its potential impact and challenges to promote the harmonious coexistence of dance art and AI technology. The research of this paper provides new perspectives and thoughts in the field of dance art, Provides a reference for future research and practice.

This article explores contemporary practices in the application of artificial intelligence (AI) within sports journalism, ranging from automated news production to personalized dissemination of multimedia content. The relevance of the topic is driven by the rapid advancement of digital technologies, the growing volume of both structured and unstructured sports data, and the urgent need for timely, accurate, and audience-tailored information delivery. Increasing competition for audience attention, shifts in news consumption behavior, and the expansion of generative algorithms make the integration of AI into editorial workflows not only desirable but a strategically necessary step for the development of modern media. The object of the study is the role and functional application of artificial intelligence in the creation, adaptation, and distribution of sports content in digital environments. The analysis focuses on the impact of automation on the quality of journalistic materials, newsroom efficiency, team productivity, audience reactions to changes in authorship and content format, as well as the ethical, legal, and professional challenges arising from the digitalization of the field. The study aims to analyse current practices of AI application in sports journalism and assess their potential for the development of international sports journalism. The study employs an interdisciplinary approach, combining perspectives from journalism studies, computer science, sociology, and media ethics. The methodology includes a critical review of recent academic literature, an empirical analysis of real-world AI implementation in leading media organizations (such as ESPN, BBC, and the Associated Press), and an overview of core algorithmic functions — including classification models, neural generative systems, recommender engines, and natural language processing (NLP) tools. Additionally, international surveys of journalists and editors are examined, along with statistics on audience retention and user perception of machine-generated content. The findings demonstrate that AI integration in sports journalism serves as an effective tool for scaling content production, increasing editorial responsiveness and enhancing the personalization of informational offerings. However, the study also emphasizes that full automation entails risks of content homogenization and erosion of audience trust — especially in cases where transparency around AI authorship is lacking. The necessity of combining AI capabilities with editorial oversight, responsiveness to audience needs, and adherence to professional standards is underscored.

The integration of Artificial Intelligence (AI) into the dredging systems and dredging machinery used in "capital" and "maintenance" dredging in Bangladesh can enhance the efficiency of the machines and dredging process, enabling the operators to perform regular and repetitive dredging tasks safely in the rivers, ports, and estuaries all over the country. AI, including Big Data, Machine Learning, Internet of Thing, Blockchain and Sensors and Simulators with their catalytic potentials, can systematically compile and evaluate specific data collected from different sources, develop applications or simulators, connect the stakeholders on a virtual platform, store lakes of information without compromising their intellectual rights, predicting models to harness the challenges, minimise the cost of dredging, identify possible threats and help protect the already dredged areas by giving timely signals for further maintenance. Furthermore, the application of AI modulated dredging devices and machinery can play a significant role when monitoring aspects becomes crucial, keeping environmental impacts mitigated without affecting the quality of the human environment. This study includes the evaluation of the application of AI – its prospect and challenges in the existing dredging systems in Bangladesh against the backdrop of the challenges faced in capital and maintenance dredging in the major rivers – and assess whether such inclusion of AI is likely to minimise the cost of dredging in the rivers of Bangladesh and facilitate the materialisation of the objectives of Bangladesh Delta Plan 2100.This paper studies the organisation's infrastructural requirement for the integration of AI into dredging systems, using benchmarking such as 1- "Understanding AI Ready Approach", 2-"Strategies for Implementing AI", 3-"Data Management", 4-"Creating AI Literate Workforce and Upskilling", and 5-"Identifying Threats" concerning the management and dredging operations of Bangladesh Inland Water Transport Authority (BIWTA), under Bangladesh Ministry of Shipping and Bangladesh Water Development Board (BWDB). The paper also uses several case studies such as channel dredging to show that the use of AI can bring a significant change in the dredging operations both in reducing the cost of dredging and in terms of harnessing the barriers in adaptive management and environmental impacts.

Diabetes mellitus is a chronic metabolic disorder with significant global prevalence and associated healthcare burdens, necessitating early detection and effective management strategies. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized diabetes care, offering innovative approaches to prediction, monitoring, and personalized management. This study conducted a systematic review of 82 high-quality peer-reviewed articles, following the PRISMA guidelines, to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management. The review highlights the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines (SVM), which consistently demonstrate high accuracy and reliability in predicting diabetes risk. Ensemble learning methods, particularly Gradient Boosting, emerged as superior techniques for predictive performance, while deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), proved effective in analyzing unstructured data such as medical images and time-series glucose data. The integration of AI into wearable devices and mobile health applications has further enhanced real-time monitoring and glycemic control, bridging the gap between technological advancements and practical healthcare solutions. Despite these advancements, challenges such as data imbalance, limited external validation, and the need for explainable AI frameworks persist, underscoring the necessity for methodological rigor and standardization. This review provides critical insights into the current state, limitations, and opportunities of ML and AI in diabetes care, emphasizing their transformative potential in addressing this global health challenge.

Background. In the context of the continuous shifts in the modern labour market and the escalating competitiveness among students of maritime specialties, there is an increasing demand for the training of highly qualified specialists in the maritime industry, who are able to interact effectively with international partners at a professional level. In this respect, the use of generative artificial intelligence (AI) to improve foreign language teaching methods for students of maritime subjects is especially relevant. This is fully justified under the conditions of globalisation and the digital transformation of the educational sphere. Purpose. The purpose is to determine the effectiveness of generative AI application in the process of building foreign language professional communicative competence of maritime students by modelling realistic scenarios of job interviews in shipping companies. Materials and methods. The present study adopted a comprehensive approach, integrating theoretical and empirical methods of analysis. The theoretical basis was established through a comprehensive review of normative and educational documents, with the aim of identifying the fundamental requirements for the educational process. This was complemented by a thorough examination of scientific and educational literature, as well as methodological literature, on the application of AI in education and linguodidactics, thus enabling the identification of current trends and the potential of technologies for teaching maritime students. The empirical element of the study involved modelling, describing and analysing professional interview scenarios using generative AI models, the observation of students' learning activities, and the conduction of interviews to obtain feedback on the perception and effectiveness of the new techniques. Results. The research yielded an innovative methodology for training students of maritime specialities at Sevastopol State University for professional interviews in international shipping companies. The developed three-stage training model, combining the creation of a database of questions, simulation of interviews using generative (AI) and practising skills through business games, demonstrated high efficiency in the formation of both linguistic and professional competences of students, in addition to soft skills deemed integral for a successful career in the maritime industry. The integration of generative AI within the training framework of maritime students has been found to expand the scope of opportunities for effective preparation for professional interviewing. The implementation of this methodology has been identified as a significant contributor to the modernisation of the educational process, rendering it more personalised, effective and aligned with the precise requirements of the contemporary labour market.

This study evaluates the impact of Artificial Intelligence (AI) applications in power systems on energy security and to determine relevant policy implications. We use a mixed methods approach to analyze the benefits and risks associated with AI implementation on the European power grid, focusing on four key dimensions of energy security: availability, affordability, accessibility, and acceptability. We investigated the benefits of AI using PyPSA, a Python-based model of the European electricity system. Three AI applications were parametrized: load reduction, load shifting, and wind wake steering. We compared scenarios in which these AI applications are widely deployed against a baseline scenario without these applications to determine if AI improves energy security. The study also analyzes risks associated with AI deployment in the power grid. We developed a risk taxonomy centered around six high-level categories: cybersecurity, jurisdictional or sovereignty issues, unexplained or unexpected actions by the model, unethical or illegal decision-making, reliance and trust in decision-making, and supplier dependency and vendor lock-in. Additionally, we conducted a back-casting exercise with subject-matter experts to determine positive and negative future outcomes of AI deployment and identify actions to create positive outcomes and avoid negative ones. The paper presents a set of policy implications for AI on the European synchronous grid. We find that AI applications can improve energy security in power systems. In the scenarios we tested, behind-the-meter applications have a greater impact on energy security than front-of-meter applications. The results suggest that AI applications targeting energy consumption may significantly improve energy security metrics. Keywords: Energy Security, AI, Power Grid, European Electricity System

The effective integration of blockchain technology and artificial intelligence (AI) has the potential to change healthcare management. Two cutting-edge developments in the healthcare industry are blockchain and artificial intelligence. Blockchain, an open network for information sharing and permission, is being used in e-Health to apply artificial intelligence models. Healthcare workers will be able to view patient medical records on the blockchain. Artificial intelligence (AI) makes use of a wide range of suggested algorithms, decision-making power, and vast amounts of data. Health care services can be made more decentralized, transparent, safe, and impenetrable with the use of blockchain technology. AI needs cryptographic records to be stored, and blockchain makes this possible. Applications of artificial intelligence in healthcare management include chatbots for diagnosis and treatment, predictive analytics, personalized medicine, and more. Blockchain technology has applications in supply chain management, clinical trial management, interoperability, and data security and integrity in healthcare management. A more effective, safe, and patient-centered healthcare ecosystem can be created by integrating blockchain technology and artificial intelligence (AI) into healthcare management. It can encourage the development of a wide range of applications in radiology, cancer treatment, cardiology, dermatology, and fundoscopy that may save patients' lives.

This article provides a brief overview of the application and development of artificial intelligence (AI) in the field of education under the background of New Liberal Arts. It also highlights the importance and current status of innovative approaches in college English teaching. Furthermore, it presents the research objectives and methods. The application of artificial intelligence in education has witnessed significant growth and advancement in recent years. AI technologies, such as machine learning and natural language processing, have been utilized to enhance various aspects of education, including personalized learning, intelligent tutoring systems, and automated grading. These applications have shown promising results in improving students' learning outcomes and engagement. In the context of college English teaching, innovation is crucial to meet the evolving needs and expectations of students. Traditional teaching methods often fail to cater to individual learning styles and preferences, leading to limited effectiveness and student disengagement. Therefore, there is a pressing need to explore innovative approaches that leverage AI technologies to create more interactive and personalized learning experiences. The research aims to investigate the potential of integrating AI into college English teaching and its impact on students' learning outcomes and engagement. The study will employ a mixed-methods approach, combining quantitative analysis of students' performance data with qualitative interviews and surveys to gather comprehensive insights. By examining the effectiveness and challenges of AI integration in English teaching, this research seeks to provide valuable recommendations for educators and policymakers to enhance the quality of college English education. Above all , this study aims to explore the application of artificial intelligence in college English teaching, considering its potential to improve learning outcomes and engagement. By adopting a mixed-methods research approach, this study intends to provide valuable insights and recommendations for educators and policymakers to promote innovative and effective English teaching practices.

This study investigates the scientific development of Artificial Intelligence (AI) and Machine Learning (ML) applications in Human Resource Management (HRM) through bibliometric analysis. To this end, 522 academic publications indexed in the Scopus database between 2020 and 2024 were analyzed using the R-based bibliometrix package and VOSviewer software. Descriptive analysis, scientific productivity metrics, and content analysis techniques were employed. The findings revealed three main patterns. First, research on AI and ML applications in HRM has grown significantly—particularly between 2022 and 2024—driven by post-pandemic digital transformation. Second, India, China, and the USA lead in research output, while the UK and France demonstrate strong citation impact, indicating a globally expanding research ecosystem. Third, the thematic focus of research is shifting from technical infrastructure toward more human-centered and ethical dimensions. Additionally, keyword co-occurrence network analysis identified three major thematic clusters: HRM functions, AI applications, and machine learning analytics, highlighting the field’s interdisciplinary nature. Compared to the previous studies, this research provides a more comprehensive bibliometric analysis of AI and ML applications in HRM. It is the first extensive study to map the intellectual evolution of the field from a multidisciplinary perspective. Furthermore, it charts research trends and collaboration networks, revealing a shift from technical implementations to strategic integration. In conclusion, this analysis offers new insights to the literature by illustrating the technological evolution in HRM and highlighting the growing significance of cutting-edge approaches such as AI and ML, reaffirming the field as a timely and impactful area of research.