The role of Artificial Intelligence in Promoting The Knowledge Of Positive Human Resource Management: A Causal Model

This study addresses a critical gap in understanding Artificial Intelligence (AI)'s role in education by empirically investigating and comparing the distinct perceptions of teachers and students regarding AI's role in a comprehensive range of social development aspects in both online and physical classroom settings. In particular, we evaluated how teachers utilize AI in their teaching methods, namely, Communicative Language Teaching (CLT), the Direct Method (DL), Task-Based Language Teaching (TBLT), Content and Language Integrated Learning (CLIL), and Community Language Learning (CLL), and students in their learning methods, namely, Communicative Learning (CL), Immersive Learning (IL), Task-Based Collaborative Learning (TBCL), Content Integrated Learning (CIL), and Community-Based Reflective Learning (CBRL), to configure their social development. We interviewed 20 teachers (10 from online and 10 from physical classes) and 40 students (20 from online and 20 from physical classes) and evaluated their perceptions regarding AI usage in teaching and learning methods towards social development. The results of our study are convincing enough to suggest that both teachers and students perceive AI usage helpful in teaching models; however, variation in their perception is observed. Notably, the divergence in the perception of teachers and students with regard to AI's role is a key observation of this study. For instance, the teachers perceived AI as a highly effective tool in fostering community building during online sessions; in contrast, the students viewed its role as being moderately effective. Likewise, the teachers perceived AI's role as a critical tool in traditional classrooms rather than in virtual ones, whereas the students associated AI with online learning-in terms of digital tools, learning opportunities, and critical discussion-by rating its impact on social confidence and verbal-nonverbal communications significantly more strongly in physical settings. On the contrary, the teachers emphasized AI's relevance to their self-confidence, emotional intelligence, and community engagement in online teaching platforms; yet, the ratings dropped to moderate in physical contexts. The students' perceptions in this regard matched those of the teachers, as they also emphasized the importance of social confidence and overall well-being in physical classrooms, where the teachers' assessment was comparatively low. These patterns provide analytical insights that are decisively valuable for designing AI-integrated pedagogical models that support social development within the educational environments.

The role of Artificial Intelligence (AI) and Generative Artificial Intelligence (Gen AI) in digital healthcare

This research paper focuses on the role of Artificial Intelligence in UI/UX design. We know that one of the most important aspect in software development is the design of the user interface ( UI ), which refers to the look and feel of the product, and user experience ( UX ), which refers to the interaction by the user.The integration of Artificial Intelligence (AI) in User Experience (UX) and User Interface (UI) design has revolutionized digital interactions by enhancing personalization, automation, predictive analytics, and accessibility. AI-driven tools enable designers to create more intuitive, adaptive, and usercentric interfaces, improving user engagement and satisfaction. This research paper explores the various applications of AI in UX/UI, including AI-powered personalization, which tailors experiences based on user behavior, automation in design, which accelerates prototyping and layout generation, and predictive analytics, which enhances decision-making through data-driven insights. Additionally, the role of conversational AI, such as chatbots and virtual assistants, in improving user interactions is examined, along with AI's contribution to inclusive and accessible UX/UI design.Despite its advantages, the implementation of AI in UX/UI presents challenges such as data privacy concerns, ethical considerations, and potential over-reliance on automation. This paper discusses these challenges and proposes solutions to ensure that AI enhances UX/UI without compromising creativity, inclusivity, or ethical standards. The study concludes that while AI is transforming UX/UI design, a balanced approach combining AI-driven efficiency with human creativity is essential for building truly user-friendly and ethical digital experiences.

PurposeThis study aims to investigate the significance of an emerging concept – green talent management (TM) and its influence on employees’ innovative work behavior, together with the moderating roles of transformational leadership and artificial intelligence within the context of higher educational institutions.Design/methodology/approachTwo hundred and thirty-five structured questionnaires were administered to the academic staff in five universities located in Northern Cyprus, and the data was analyzed using partial least square structural equation modeling with the aid of WarpPLS (7.0).FindingsThis study provides evidences that green hard and soft TM exerts significant influence on employees’ innovative work behavior. Similarly, transformational leadership and artificial intelligence were confirmed to have a significant impact on employees’ innovative work behavior. Moreover, the study found transformational leadership and artificial intelligence to significantly moderate the relationship between green hard TM and employees’ innovative work behavior.Research limitations/implicationsThe study provides theoretical and managerial implications of findings that will assist the leaders in higher educational institutions in harnessing the potential of green TM in driving their employees’ innovative work behavior toward the achievement of sustainable competitive advantage in the market where they operate.Originality/valueThe attention of researchers in the recent time has been on the way to address the challenge facing organizational leaders on how to develop and retain employee that will contribute to the sustainability of their organization toward the achievement of sustainable competitive advantage in the market they operate. Meanwhile, the studies exploring these concerns are limited. In view of this, this study investigates the significance of an emerging concept – green talent management and its influence on employees’ innovative work behavior, together with the moderating roles of transformational leadership and artificial intelligence within the context of higher educational institutions.

This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition. A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English. Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis. When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine.

This study investigates the relationship between technology quality and consumer behavior in the field of virtual reality tourism. An online survey was conducted with 403 customers in Vietnam, spanning various age groups, who have a passion for travel and have used virtual reality (VR) and augmented reality (AR) technologies in tourism. The research findings indicate that technology quality, including information quality, system quality, and usefulness, positively influences users' perceived value of VR/AR quality. This perceived VR/AR value, in turn, has a positive impact on travel intention. However, privacy concerns were found to negatively affect users' perceived value of VR/AR. The study also examines the role of Explainable Artificial Intelligence (XAI) in positively moderating the relationship between privacy concerns and perceived VR/AR value. An interesting discovery is that XAI helps alleviate users' privacy concerns when experiencing virtual tourism technologies, addressing a limitation that conventional AI has not yet resolved. Several data analysis and validation methods were applied, including reliability testing of the measurement scale (Cronbach's alpha), Exploratory Factor Analysis (EFA), and Confirmatory Factor Analysis (CFA). Hypothesis testing and model evaluation were conducted using Structural Equation Modeling (SEM). Additionally, data analysis was supported by SPSS 26 and AMOS 28 software. The results of the study provide critical insights for technology manufacturers to enhance product quality, enabling users to optimize their virtual tourism experiences. Furthermore, the findings serve as a foundation for businesses leveraging smart tourism technologies to design and implement more effective VR/AR-based tourism products. By doing so, they can attract more tourists to real-world destinations, strengthen their competitive advantage, and promote the sustainable development of the tourism industry. This study highlights the importance of integrating advanced technologies like XAI to address user concerns and improve the adoption and satisfaction of virtual tourism solutions, ultimately contributing to the growth and sustainability of the tourism sector.

Artificial intelligence (AI) is increasingly applied in clinical diagnostics, particularly in radiology, where it can assist with imaging triaging and anomaly detection. However, the integration of AI into medical education remains under researched. This study investigates the impact of an AI-focused panel discussion on medical students' perceptions, knowledge, attitudes and concerns about AI in radiology. A paired pre-post design questionnaire comprising of 13 five-point Likert scale questions was administered to 40 medical students to complete before and after an AI-focused educational panel session at the International Radiology Undergraduate Symposium in London, United Kingdom on 24th November 2024. The questionnaire assessed four domains: 'Understanding of AI,' 'Attitudes Toward AI in Radiology,' 'AI Education in Medical School,' and 'Concerns About AI in the Future.' The primary outcome was to assess the change in students' perceptions of AI's role in radiology. Differences between pre- and post-session responses were analysed using the Wilcoxon signed-rank test. The Hodges-Lehmann median difference, the effect size, r, and their corresponding 95% confidence intervals were calculated, and p-values were adjusted using the Holm-Bonferroni method. Of the 81 eligible attendees, 40 (49.4%) completed the questionnaire (39 pre-session, 40 post-session). Students demonstrated significant improvements in their understanding of AI's potential role in radiology (Z = 3.04, p = 0.002; Holm-Bonferroni = 0.029; median paired difference = 0.5, 95% CI 0.0-0.5; r = 0.49, 95% CI 0.25-0.68) and in their awareness of AI's broader clinical applications (Z = 3.65, p < 0.001; Holm-Bonferroni = 0.0035; median paired difference = 0.5, 95% CI 0.5-1.0; r = 0.60, 95% CI 0.38-0.75). Participants expressed a more positive view of AI in healthcare overall, although concerns about AI replacing radiologists and insufficient AI education persisted. Educational interventions have the potential to improve medical students' understanding and attitudes toward AI in radiology. Integrating structured AI education into undergraduate curricula may enhance AI literacy and better prepare future clinicians for an AI-enabled healthcare environment.

Drawing on a systematic literature review and bibliometric analysis, this article examines the burgeoning field of Artificial Intelligence (AI) integration into Human Resource Management (HRM) practises. By evaluating 77 selected articles from two extensive databases, Scopus and Web of Science, this study illuminates the dynamic intersection of AI technologies and HRM, encapsulating the profound implications for organisational and individual aspects of HR practises. This analysis delineates three primary thematic areas: AI's transformative role in HRM, the emerging paradigm of human-AI collaboration, and the nuanced challenges and opportunities presented by AI in HR practises. This research contributes to the academic discourse by mapping the current state of AI applications in HRM, identifying gaps and proposing directions for future research, emphasising the need for ethical frameworks and the strategic integration of AI to enhance HR practises. Through this scholarly endeavour, we aim to offer a comprehensive overview that aids practitioners and researchers in navigating the complexities of AI's role in reshaping HRM towards more efficient, ethical, and innovative practises.

The world began to change and adapt accordingly to the dynamic technological advances. As same as the application of advanced technology in the present day is known as Artificial Intelligence (AI) which is referred to as the development of computer systems that perform tasks typically involving human intelligence. AI is being observed to be applied to various fields of business, especially to Human Resources being one important wing among the list. AI interferes with learning, reasoning, problem-solving, and understanding natural language. AI is increasingly used in human resources to help drive decisions in employee hiring, retention, and development. It can also be applied to automate tasks like payroll, but it’s being also used for the rapid creation of new policies, contracts, job descriptions, interview questions, etc. This empirical study is conducted to illuminate the concepts, impact, role, and recent trends of AI in Human capital management. Hence, an overview of the scope of AI in Human capital management has been built in the study.

Artificial Intelligence (AI) has found a lot of scope in diversified applications including health care systems. Due to the rapid increase in digitization and change in the life style lot of people are facing health care issues like mental diseases. Now the days AI is use to help health care members with its analysis like tumor, cyst, cancer, dermatology issues etc. Looking towards the increasing cases there is a urgent demand of AI in medical specially in mental health care.Many electronic systems are used for the health data analysis so the combination of AI within system can help the patients. Due to the pandemic there is increase in health issues and it has pushed the limits for increase in need of mental health care system using AI. Since AI can provide services like personalize care, remote access, guiding patient, online doctor’s advice etc. AI can be used to identify the individual with high risk also it can provide intervention to treat and prevent mental illness. This work presents the comparison and role of different AI based mental healthcare analysis. As AI using electronic health record, brain imaging and other sensing system can predict the issues in individual and help to monitor patient’s progress and helps the doctor to alter treatment if needed and can help in decrease in suicidal issues. Apart from indentifying the particular issue in patient AI can help the patient to assign the right therapist as per his/her problem. Thus the patient is been given with right therapy at right time. It can also, guide the care taker to give medicine at given time. Natural language processing and Machine learning can be used to find the problem in individual along with its social media presence can be an effective tool to identify once mental health. This information can assist the healthcare practitioner to identify particular problem and guide for treatment. There is also a limitation for collecting data and training the AI based system which is discussed in this work. Along with that the technology limitation and challenges are well described.

Human resources (HR) experts can now utilize calculations and AI to smooth out their work processes, diminish their predispositions, and work on their examination and dynamic on account of advancements in artificial intelligence (computer based intelligence) innovation. Nonetheless, a few organizations are reluctant to embrace computer based intelligence for extra use cases because of the ongoing downsides and weaknesses. In human resource management, artificial intelligence (computer based intelligence) can be extremely helpful on the grounds that it can robotize drawn-out assignments and diminish predisposition in the determination cycle. Man-made intelligence can be applied to progress authoritative procedures, improve employee engagement, and support vocation development. Employee engagement can be expanded and vocation useful learning experiences can be more individualized and custom-made with the guide of computer based intelligence innovation. The monetary expenses of setting up and maintaining artificial intelligence in HR, as well as the risk of missteps and unintentional predispositions, ought to be considered by associations. Future advancements in artificial intelligence will expand personalization, mechanization, and data based decisions in human resource management. Before very long, as artificial intelligence keeps on changing the scene of human resource management, HR managers should likewise know about the challenges they might experience. Worries about simulated intelligence making work more secure and more open are normal among HR chiefs. The recruiting and preparing of new employees is a dreary undertaking for the department of human resources. Artificial intelligence has many applications that can assist with peopling who work in human resources with their manual work. Hence, the present study has been focused to give a theoretical outline on the role of Artificial Intelligence (AI) in Human Resource Management (HRM) in recent era.

Purpose: The growing sophistication of financial fraud in the banking sector has necessitated the adoption of advanced technical solutions such as artificial intelligence (AI) and robotic process automation (RPA) to enhance fraud detection and prevention. This study examines the role, effectiveness, and challenges of AI and RPA in combating financial fraud, addressing gaps left by traditional rule-based systems. Methodology: This study employs a literature review methodology, synthesizing existing research, case studies, and industry reports to evaluate the impact of AI and RPA on fraud detection. Key themes analyzed include real-time analytics, anomaly detection, predictive modeling, operational efficiency, and implementation challenges. Findings: The findings reveal that AI significantly improves fraud detection accuracy, reduces false positives, and adapts to emerging threats, while RPA enhances compliance and operational efficiency by automating repetitive tasks. However, challenges such as algorithmic bias, adversarial AI attacks, data privacy concerns, high implementation costs, and ethical dilemmas around transparency and accountability hinder widespread adoption. Despite these obstacles, financial institutions report substantial reductions in fraud-related losses after integrating AI and RPA. Unique contribution to theory, practice and policy (recommendations): This study contributes to theory by consolidating insights on AI and RPA’s transformative potential in fraud detection. For practice, it recommends investing in explainable AI, robust adversarial defense mechanisms, and cost-effective RPA integration. Policymakers should establish ethical AI governance frameworks, promote regulatory alignment, and incentivize innovation to ensure financial security and transparency. The study underscores that maximizing the benefits of AI and RPA requires continuous technological advancement, ethical oversight, and collaborative regulatory efforts.

Artificial Intelligence (AI) has an important role to play in shaping the future of software development. AI responds to complex challenges in the information technology industry and expands the scope of future possibilities, which include increased automation, personalization, and security. The research aims to identify the role of AI in education and research from various aspects of software development, and evaluate the resulting implications for information technology as a whole. The research adopted the Systematic Literature Review Method following PRISMA guidelines. A total of 320 articles were collected from Scopus, Web of Science and Google Scholar and applying predefined criteria, 42 relevant articles were included for analysis. The research findings show that the role and integration of artificial intelligence (AI) has a significant impact in improving efficiency, bringing software innovation in education, learning and research in the future. AI has proven effective in personalizing learning, adapting teaching materials and improving student learning outcomes. AI accelerates the process of analyzing big data, identifying patterns and trends that conventional methods may miss. The implications of the findings suggest that the integration of AI in education and research not only improves the efficiency and effectiveness of the process, but opens up new opportunities for innovation and development of more adaptive and data-driven learning and research methods. The challenges of AI in education and research include data privacy, potential bias in algorithms, and the need for adequate technological infrastructure to support effective and secure implementation, avoid inequality of access, and ensure accurate results.

Introduction: With the expansion of next -generation sequencing (NGS) technologies and omics data analysis, genetics education has entered a new phase characterized by large volumes of complex data. In this context, traditional teaching methods have become less effective. Utilizing artificial intelligence (AI) and bioinformatics offers an innovative approach to elevate genetics education to an interactive, data -driven, and analysis -focused level. This study responds to the growing demand for data -driven and analytical training in genetics. Given the vast amount of genomic data and the complexity of the required analyses, employing AI and bioinformatics tools can significantly enhance the quality of education and research in this field. The aim of this study is to investigate the impactful role of advanced AI and bioinformatics in improving modern genetics education . Method: This study was conducted as a narrative review. Scientific sources published in PubMed, Scopus, Web of Science, and Google Scholar between 2005 and 2025 were reviewed. Articles related to the use of AI and informatics in genetics education were selected and analyzed using content analysis . Results: The review results indicated that AI -based tools, including machine learning algorithms, genomic language models, and adaptive training systems, significantly contribute to personalizing education, simulating biological processes, and analyzing genetic variants. Furthermore, practical training in bioinformatics skills —such as working with genetic databases, analytical software, biological programming, and applied biostatistics —empowers students to analyze complex genomic data. However, the lack of digital educational resources and specialized instructors continues to pose a major challenge in data -driven education . Conclusion: The integration of AI and bioinformatics into genetics education offers an innovative approach to training specialists in modern genetics. Developing localized content, virtual training courses, and policies that align the education system with technological advancements are effective strategies for enhancing the quality of genetics education in Iran and similar countries.

Purpose This study explores how artificial intelligence (AI) impacts entrepreneurship, focusing on decision-making, idea generation and opportunity recognition, as well as ethical challenges and research gaps. It also presents a framework and future research directions to better understand AI's role in shaping entrepreneurial practices through intellectual capital, like knowledge, skills and innovation. Design/methodology/approach The research adopts a systematic literature review methodology, following the rigorous three-stage process developed by Tranfield et al. (2003). Initially, 1,443 records were identified from the Web of Science database, and after comprehensive screening and review, 19 high-quality studies were chosen for further analysis. Content and thematic analysis were conducted to answer four key research questions. Findings The review indicates that AI significantly improves entrepreneurial processes by enhancing decision-making precision, facilitating opportunity recognition and fostering effective idea generation. Moreover, AI is crucial for strengthening intellectual capital by improving knowledge management, supporting creative problem-solving and optimizing human and structural capital in entrepreneurial settings. However, it also uncovers notable gaps in understanding the ethical and societal effects of AI adoption, especially regarding small and medium-sized enterprises and startups with limited resources. Practical implications The paper reviews the current state of the literature on the topic. It also offers a framework that presents AI's potential to drive innovation, enhance operations and support sustainable growth while confronting the ethical challenges and resource limitations that entrepreneurs must address. Additionally, the study highlights the role of AI in enhancing intellectual capital, particularly by facilitating knowledge creation, cognitive tasks and decision-making, which are essential for sustained entrepreneurial success. Originality/value Through a systematic analysis of existing literature, this study contributes to the expanding dialog on AI, intellectual capital and entrepreneurship. It explores answers to the key research questions in this area for the first time, highlighting AI's vital role in entrepreneurial activities. The study also provides a comprehensive framework, practical implications and a future research agenda.