Evaluating Trustworthiness of Artificial Intelligence (AI) Applications in Education

With the continuous development of science and technology, exploration in the field of life sciences has become increasingly profound, encompassing the understanding of human beings themselves and the exploration of disease etiology and treatment methods.Artificial intelligence is defined as the intelligence exhibited by artificially created systems. The application of artificial intelligence is quite extensive and can replace humans in various aspects of work, significantly enhancing efficiency in daily life and work. Artificial intelligence has a dual nature, as it could provide convenience to human society but could also bring about various adverse effects.This paper summarizes the current applications of artificial intelligence in the field of life sciences. By analyzing the applications of artificial intelligence in areas such as gene editing, drug development, and bioinformatics, it demonstrates the potential of artificial intelligence in the field of life sciences. At the same time, this paper also points out the ethical, technical, social, and various challenges that the application of artificial intelligence technology in the field of life sciences faces, with the aim of providing reference for research in related fields.

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.

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

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

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.

Artificial intelligence (AI) applications pave the way for innovations in the healthcare (HC) industry. However, their adoption in HC organizations is still nascent as organizations often face a fragmented and incomplete picture of how they can capture the value of AI applications on a managerial level. To overcome adoption hurdles, HC organizations would benefit from understanding how they can capture AI applications’ potential.We conduct a comprehensive systematic literature review and 11 semi-structured expert interviews to identify, systematize, and describe 15 business objectives that translate into six value propositions of AI applications in HC.Our results demonstrate that AI applications can have several business objectives converging into risk-reduced patient care, advanced patient care, self-management, process acceleration, resource optimization, and knowledge discovery.We contribute to the literature by extending research on value creation mechanisms of AI to the HC context and guiding HC organizations in evaluating their AI applications or those of the competition on a managerial level, to assess AI investment decisions, and to align their AI application portfolio towards an overarching strategy.

Purpose This study examines the extent to which artificial intelligence (AI) applications predict psychological flourishing and sustainable development among Fayoum University students. It further explores gender differences in AI usage, flourishing levels, and sustainability orientations, as well as the interrelationships among the three variables. Methods A quantitative correlational design was employed using validated scales measuring AI application use, psychological flourishing, and sustainable development. The sample consisted of 190 university students from Fayoum University (Egypt) there ages were between 20 and 25 years. The mean age was 22.54 years, with a standard deviation of 3.22 years. Data were analyzed using t-tests, Pearson’s correlation coefficients, and simple linear regression to test the study hypotheses. Results Findings revealed no significant gender differences in AI application use, while significant differences were found in psychological flourishing and sustainable development in favor of male students. Significant positive correlations emerged among AI use, flourishing, and sustainability. Regression analyses showed that AI application use moderately predicted psychological flourishing (R 2 = 0.098) but strongly predicted sustainable development (R 2 = 0.604), indicating that AI plays a substantial role in shaping students’ sustainability-related behaviors. Conclusions The study demonstrates that AI applications contribute meaningfully to enhancing students’ well-being and sustainability practices, with a stronger influence on sustainability outcomes. These findings underscore the growing relevance of AI-driven learning environments in promoting sustainable education and student development. Implications The results highlight the need for integrating AI-based tools into higher education policies, fostering responsible AI use, and developing institutional initiatives that enhance students’ well-being and sustainability competencies.

A 3D indicator for guiding AI applications in the energy sector

This research examines what impact the Big Data Processing Framework (BDPF) has on Artificial Intelligence (AI) applications within Corporate Marketing Communication (CMC), and thereby the research question stated is: What is the potential impact of the BDPF on AI applications within the CMC tactical and managerial functions? To fulfill the purpose of this research, a qualitative research strategy was applied, including semi-structured interviews with experts within the different fields of examination: management, AI technology and CMC. The findings were analyzed through performing a thematic analysis, where coding was conducted in two steps. AI has many useful applications within CMC, which currently mainly are of the basic form of AI, so-called rule-based systems. However, the more complicated communication systems are used in some areas. Based on these findings, the impact of the BDPF on AI applications is assessed by examining different characteristics of the processing frameworks. The BDPF initially imposes both an administrative and compliance burden on organizations within this industry, and is particularly severe when machine learning is used. These burdens foremost stem from the general restriction of processing personal data and the data erasure requirement. However, in the long term, these burdens instead contribute to a positive impact on machine learning. The timeframe until enforcement contributes to a somewhat negative impact in the short term, which is also true for the uncertainty around interpretations of the BDPF requirements. Yet, the BDPF provides flexibility in how to become compliant, which is favorable for AI applications. Finally, BDPF compliance can increase company value, and thereby incentivize investments into AI models of higher transparency. The impact of the BDPF is quite insignificant for the basic forms of AI applications, which are currently most common within CMC. However, for the more complicated applications that are used, the BDPF is found to have a more severe negative impact in the short term, while it instead has a positive impact in the long term.

With the rising demand for personalized learning and increased digital natives, the traditional education model is under pressure to reform. the rapid development of artificial intelligence (AI) provides new education solutions, and the application of AI is becoming widespread, from intelligent tutoring to automated grading to learning analytics. However, the application of AI in education is not without challenges. This literature review aims to explore the impact of AI on education, analyze its potential advantages, and propose strategies to address the challenges and provide guidance for the future development of education. Understanding the impact of AI on education is instructive for the future direction of AI in education and reveals the importance of AI for education to the extent that it is important for formulating effective educational policies and cultivating future talents in AI technology. The final results of the study show that although AI in the education industry there are various difficulties and limitations, but AI offers great potential for smarter and more efficient education levels and education systems. In today's era, computer programming education is emerging as a pivotal force in shaping the future, as it not only hones students' logical thinking and innovative capabilities but also lays a solid foundation for their career paths in the digital world. By integrating knowledge across various disciplines, programming education stimulates students' enthusiasm for applying their learning to real-world problems and enhances their sense of social engagement, enabling them to tackle challenges through technological means.

This article examines the impact of artificial intelligence (AI) applications on teaching and learning processes in primary education based on semi-structured interviews conducted with teachers and parents. Designed as a case study, the research aims to understand participants' experiences with AI-enabled educational tools and their impact on classroom practices. Findings indicate that teachers and parents generally have a positive attitude towards AI applications. Teachers stated that these applications support students' individual learning processes and enrich their teaching practices. Parents, on the other hand, reported improvements in their children's academic achievement but expressed some concerns about the increase in screen time. Both teachers and parents emphasized that AI's capacity to deliver personalized content enhances the quality of learning. Based on the research findings, the article offers recommendations for developing high-quality AI applications, expanding AI literacy training to ensure equity in education, positioning AI as a complementary tool for teachers, and enhancing teachers' digital competencies in this area. In conclusion, this study shows that we are on the threshold of a learning era in which algorithms, entering the classroom, redefine not only technology but also humanity.

Abstract. Problem. The rapid advancement of artificial intelligence (AI) has significantly impacted various domains, particularly education. AI-driven tools are increasingly being integrated into teaching methodologies, assessment frameworks, and curriculum design. However, despite the growing research interest in Artificial Intelligence in Education (AIED), the field remains fragmented, lacking a comprehensive analysis of dominant research themes and emerging trends. Understanding these trends is crucial for optimizing AI applications in education and shaping future developments in the field. Goal. The primary goal of this study is to analyze the current landscape of AIED research by identifying key thematic clusters, evaluating dominant research directions, and predicting future trajectories in AI-driven education. Methodology. This study employs network analysis, topic modeling, and global research trend evaluation to analyze scientific publications in AIED. The Latent Dirichlet Allocation (LDA) algorithm is applied to classify research articles into thematic categories, enabling the identification of primary topics shaping the field. Data is sourced from the Web of Science database, and hierarchical clustering techniques are used to determine semantic similarities between thematic models. Results. The analysis reveals five major research clusters in AIED, including AI applications in medical education, machine learning in education, adaptive learning technologies, large language models in learning, and student-centered AI-driven educational strategies. The study identifies increasing trends in the adoption of generative AI and chatbot-based learning support, while traditional AI-driven assessment methodologies show moderate growth. The results also highlight strong interconnections between AI-driven personalization and intelligent tutoring systems, emphasizing the shift toward adaptive and student-centric learning environments. Originality. This research provides a structured and systematic analysis of AIED trends, leveraging advanced topic modeling techniques. Unlike previous studies focusing on isolated AI applications in education, this study presents a holistic view of thematic structures in AIED research, offering a quantitative and data-driven approach to understanding the evolution of the field. Practical Value. The findings can serve as a strategic reference for educators, policymakers, and AI developers in shaping future educational technologies. By identifying key thematic directions, this research supports evidence-based decision-making for integrating AI into educational systems. The results can also guide further research in AIED, helping scholars explore emerging AI-driven teaching methodologies, adaptive learning models, and ethical considerations in AI-powered education.

Recent advancements in artificial intelligence (AI) applications in medicine have been significant over the past 30 years. To monitor current research developments, it is crucial to examine the latest trends in AI adoption across various medical fields. This bibliometric analysis focuses on AI applications in cardiology. Unlike existing literature reviews, this study specifically examines journal articles published in the last decade, sourced from both Scopus and Web of Science databases, to illustrate the recent trends in AI within cardiology. The bibliometric analysis involves a statistical and quantitative evaluation of the literature on AI application in cardiovascular medicine over a defined period. A comprehensive global literature review is conducted to identify key research areas, authors, and their interrelationships through published works. The leading institutions and most influential authors in research on the role of AI in cardiology were located in the United States, the United Kingdom, and China. This study also provides researchers with an overview of the evolution of research in AI and cardiology. The main contribution of this study is to highlight the prominent authors, countries, journals, institutions, keywords, and trends in the development of AI in cardiology.

This article explores the integration of artificial intelligence (AI) applications into project‐based learning (PBL) education as a means to enhance students' education. Specifically, the implementation of AI in the context of power generation is addressed, focusing on achieving power generation at minimum wind velocity and classifying turbine faults using AI techniques. The researchers have proposed a novel generating unit which is going to generate 1 KW of electric power at a specific flow rate of air and the generated power will be stored in the battery bank through the charge controller and then the load is driven from the battery through an inverter. Iron or core losses (Hysteresis, Eddy Current losses) can be acknowledged as one of the major reasons for the inefficiency of conventional generators, therefore anovel coreless model generator was proposed which also improved efficiency and reduces drag. Wind Turbine prototype was fabricated and deployed for the testing and validation of the proposed novel design. The design produced outstanding power ratings and electrical generation characteristics compared with other existing strategies at minimal air flow. Results proved that the proposed coreless axial flux generator has the capability to produce a better power rating compared with the existing wind turbine generators. Proposed Axial flux achieved 10.73 watt power at wind velocity at around 80 rpm. At a wind velocity of 10 m/s and around 800 rpm 313–330 kwh was produced by the proposed generator while the conventional generator produced around 300 kwh. The proposed generator design performed 35% better in terms of production efficiency under load and no load conditions. Moreover, faults in turbines are very common due to the various temperatures, therefore the faults have been classified using state‐of‐the‐art AI‐based classifiers. A comparison of space vector modulation (SVM) and Naive Bayes classifiers was performed in the study to classify wind turbine faults. It was found that both classifiers performed well in achieving high accuracy. SVM achieved a slightly higher accuracy of 0.9861 compared with Naive Bayes, which achieved an accuracy of 0.967. Based on the results, it can be inferred that SVM may be a more suitable classifier for wind turbine fault classification. The case study results demonstrated the potential of AI applications in PBL education, offering students a multidisciplinary learning experience that enhances their technical knowledge, problem‐solving skills, and teamwork abilities.

The recent mainstreaming of Artificial Intelligence (AI) applications and tools has significantly enhanced EFL educators’ research writing process. Nevertheless, few studies exist regarding how EFL educators understand AI applications and their integration into research writing processes and techniques. The current study aims to investigate the attitudes and perceptions of (n= 40) EFL teachers at Majmaah University concerning integrating AI tools in the Research writing process, by collecting data from mixed-methods source questionnaires and interviews. Thematic analysis was employed for qualitative data, while descriptive and inferential statistics through SPSS were used for quantitative data analysis. The questionnaire results demonstrated that most respondents have positive perceptions toward integrating AI tools, believing they can improve efficiency and quality in the research writing process. However, more familiarity with existing AI applications is needed to ensure this integration process. Moreover, the study affirms the significant impact of training and support on the effective integration of AI tools. Respondents strongly agree about the benefits of AI applications in streamlining literature reviews, aiding in data analysis, reducing errors, and enhancing overall language quality. Interview responses further emphasize the possible benefits of AI tools in the research writing process, highlighting efficiency gains, assistance in various writing tasks, and improved quality of EFL teachers’ research articles. All the participants stress the importance of ethical utilization, maintaining quality, and vigilance when integrating these tools into the research writing process.