The Role of Artificial Intelligence (AI) Software in Education and Research: A Systematic Literature Review

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.

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing industries, reshaping the way we interact with technology, and driving innovation across multiple disciplines. Advancements in Artificial Intelligence and Machine Learning is a comprehensive exploration of the latest developments, applications, and challenges in AI and ML, offering insights into cutting-edge research and real-world implementations. This book is a collection of twelve chapters, each exploring a distinct application of Artificial Intelligence (AI) and Machine Learning (ML). It begins with an overview of AI's transformative role in Next-Gen Mechatronics, followed by a comprehensive review of key advancements and trends in the field. The book then examines AI's impact across diverse sectors, including energy, digital communication, and security, with topics such as AI-based aging analysis of power transformer oil, AI in social media management, and AI-driven human detection systems. Further chapters address sentiment analysis, visual analysis for image processing, and the integration of AI in smart grid networks. The volume also covers AI applications in hardware security for wireless sensor networks, drone robotics, and crime prevention systems. The final set of chapters highlight AI's role in healthcare and automation, including an AI-assisted system for women's safety in India and the use of EfficientNet B0 CNN architecture for brain tumor detection and classification. Together, these chapters showcase the versatility and growing influence of AI and ML across critical modern industries. Key features A multidisciplinary approach covering AI applications in robotics, cybersecurity, healthcare, and digital transformation in 12 organized chapters. A focus on contemporary challenges and solutions in AI and ML across industries. Research-driven insights from experts and practitioners in the field. Practical discussions on AI-driven automation, security, and intelligent decision-making systems.

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) 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.

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.

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 integration of artificial intelligence (AI) into healthcare, particularly in the field of gastroenterology, marks a significant advancement in the diagnosis and treatment of pancreatic disorders. This narrative review explores the application of AI in enhancing Endoscopic Ultrasound (EUS) imaging techniques for pancreatic pathologies, focusing on developments over the past decade. Through a comprehensive literature search across several scientific databases, including PubMed, Google Scholar, and Web of Science, this paper selects and analyzes 50 studies that highlight the role, benefits, precision rates, and limitations of AI in EUS. The findings suggest that AI not only improves the quality of endoscopic procedures, as acknowledged by a majority of gastroenterologists in the UK and USA, but also offers a promising future for medical diagnostics and treatment, potentially addressing the shortage of specialists and reducing morbidity and mortality rates. Despite AI’s infancy in clinical applications and the ethical concerns regarding data privacy, its integration into EUS has enhanced diagnostic accuracy and provided minimally invasive therapeutic alternatives. This review underscores the necessity for further clinical data to evaluate the applicability and reliability of AI in healthcare, advocating for a collaborative approach between physicians and AI technologies to revolutionize the traditional clinical diagnosis and expand treatment possibilities in gastroenterology.

The integration of artificial intelligence (AI) in nonoperating room anesthesia (NORA) represents a timely and significant advancement. As the demand for NORA services expands, the application of AI is poised to improve patient selection, perioperative care, and anesthesia delivery. This review examines AI's growing impact on NORA and how it can optimize our clinical practice in the near future. AI has already improved various aspects of anesthesia, including preoperative assessment, intraoperative management, and postoperative care. Studies highlight AI's role in patient risk stratification, real-time decision support, and predictive modeling for patient outcomes. Notably, AI applications can be used to target patients at risk of complications, alert clinicians to the upcoming occurrence of an intraoperative adverse event such as hypotension or hypoxemia, or predict their tolerance of anesthesia after the procedure. Despite these advances, challenges persist, including ethical considerations, algorithmic bias, data security, and the need for transparent decision-making processes within AI systems. The findings underscore the substantial benefits of AI in NORA, which include improved safety, efficiency, and personalized care. AI's predictive capabilities in assessing hypoxemia risk and other perioperative events, have demonstrated potential to exceed human prognostic accuracy. The implications of these findings advocate for a careful yet progressive adoption of AI in clinical practice, encouraging the development of robust ethical guidelines, continual professional training, and comprehensive data management strategies. Furthermore, AI's role in anesthesia underscores the need for multidisciplinary research to address the limitations and fully leverage AI's capabilities for patient-centered anesthesia care.

The rapid advancements in artificial intelligence (AI) have impacted various industries, including human resources (HR). This thesis aims to explore the role of AI in HR and its potential implications on organizations and employees. A comprehensive literature review was conducted to identify the various applications of AI in HR, such as recruitment, employee engagement, performance management, and training and development. The study also analyzed the potential benefits and risks associated with the integration of AI in HR, including issues related to bias, privacy, and job displacement. The findings of this study suggest that AI can enhance HR practices by improving efficiency, accuracy, and objectivity. However, the risks associated with AI adoption must be carefully considered and managed to ensure ethical and responsible use. This study provides insights into the current state of AI in HR and its future potential, offering recommendations for organizations and policymakers to maximize the benefits and minimize the risks of AI integration in the HR function. The use of artificial intelligence (AI) in human resources (HR) has become increasingly popular in recent years. AI has the potential to transform HR practices by enabling organizations to automate routine tasks, make more data-driven decisions, and improve the employee experience. However, the use of AI in HR also raises important ethical and legal considerations, such as algorithmic bias and data privacy. This thesis aims to explore the role of AI in HR and its impact on various HR functions, including recruitment and selection, employee engagement, performance management, and training and development. The study also examines the potential risks and challenges of using AI in HR and identifies strategies to mitigate these risks. The research methodology employed in this study is a mixed-methods approach, combining both qualitative and quantitative research methods. The qualitative component involves a literature review and case studies of organizations that have implemented AI in HR. The quantitative component involves a survey of HR professionals to understand their perceptions of AI in HR and their readiness to adopt AI in their organizations. The findings of this study reveal that AI has significant potential to improve HR practices, particularly in recruitment and selection, where it can reduce bias and improve the accuracy and efficiency of the hiring process. AI can also improve employee engagement by providing personalized experiences and feedback, and enhance performance management by enabling real-time monitoring and feedback. In training and development, AI can provide personalized learning experiences that meet the unique needs and preferences of individual employees. However, the study also reveals that the use of AI in HR raises important ethical and legal considerations that must be addressed. Algorithmic bias, data privacy, and the potential for job displacement are some of the key risks and challenges associated with the use of AI in HR. To mitigate these risks, organizations must adopt a proactive approach that involves regular monitoring and evaluation of AI systems, transparency in decision-making processes, and ongoing training and development for HR professionals. The study also identifies several critical success factors for the successful implementation of AI in HR, including strong leadership support, a clear understanding of business objectives, collaboration between HR and IT professionals, and a focus on employee engagement and well- being. Overall, this thesis contributes to the growing body of knowledge on the role of AI in HR and its implications for organizations and HR professionals. By identifying the potential benefits, risks, and challenges of using AI in HR, and providing strategies to mitigate these risks, this study aims to inform organizational decision-making and help HR professionals prepare for the future of work..

Artificial Intelligence (AI) is revolutionizing knowledge creation, dissemination, and application across disciplines. As AI continues to evolve, its potential to reshape industries and human cognition demands continuous scrutiny and adaptation. AI's growing impact on society demands collaboration among technologists, policymakers, and ethicists to ensure responsible and fair development. The paper argues that AI, while powerful, should complement rather than replace human expertise, fostering a balance betwee technological innovation and ethical responsibility. Future research must address the long-term implications of AI, including its effect on employment, privacy, and human creativity. Furthermore, the implications of AI-driven automation on workforce transformation and economic shifts require deeper examination. The ethical questions surrounding algorithmic accountability and decision-making authority in AI applications remain crucial areas for exploration. This research aims to provide a comprehensive understanding of AI’s role in shaping the future of human knowledge and society at large. Additionally, AI’s applications in enhancing scientific knowledge and improving automation efficiency demand continuous regulatory updates and technological refinements to ensure sustainable development. This paper explores AI's transformative role in science, healthcare, education, business, and the arts, demonstrating its interdisciplinary impact. It discusses AI-driven innovations, methodological advancements, and the ethical implications of AI's integration into human knowledge systems. By examining current research and case studies, the paper highlights both the opportunities and challenges presented by AI's rapid evolution.

The rapid advancement of digital technologies has significantly influenced the field of medical imaging, particularly through the integration of Artificial Intelligence (AI) and Augmented Reality (AR). These technologies offer transformative potential in improving diagnostic accuracy, enhancing surgical planning, and addressing the limitations of traditional radiological methods. This study aims to evaluate the roles and effectiveness of AI and AR in radiology by analyzing their applications in medical diagnosis and surgical visualization, with a focus on increasing diagnostic speed, precision, and accessibility, especially in resource-limited settings. A systematic literature review was conducted by examining 45 peer-reviewed articles published between 2017 and 2025, selected based on relevance, innovation, and applicability. Thematic analysis revealed that AI—especially models using convolutional neural networks—has demonstrated high accuracy in detecting lung disease, breast cancer, and brain tumors. Meanwhile, AR has shown potential in enhancing spatial understanding and accuracy in surgical procedures. Despite these benefits, several challenges were identified, including integration difficulties with existing hospital systems, algorithmic bias, regulatory constraints, and high costs. In conclusion, the integration of AI and AR represents a promising direction for the future of radiology. However, further research is needed to develop cost-effective systems, ensure ethical and inclusive AI training, and establish standardized protocols for implementation. This study provides a foundational overview for healthcare stakeholders aiming to adopt these technologies in pursuit of more equitable and efficient medical imaging practices.

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) in learning and teaching English is becoming more significant in today's digital era. This study aims to analyze the use of AI in English Language Teaching (ELT) by identifying the most commonly used AI tools, their benefits, and challenges in implementation. Using a systematic review approach guided by PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), this study systematically collected, selected and analyzed literature from academic databases such as Google Scholar, ScienceDirect, ERIC, DOAJ, and Mendeley. Articles were screened based on relevance, recency (published within the last five years), and accessibility (open access, English language). The results show that AI technologies such as chatbots, grammar correction tools, speech recognition systems, and machine translation platforms are widely used to improve students' pronunciation, listening comprehension, vocabulary acquisition, and writing skills. In addition, AI also facilitates personalized learning, reduces teacher workload, and promotes inclusive education. However, challenges such as limited accessibility, algorithmic bias, and ethical concerns must be addressed to ensure AI integration remains fair and effective in learning or teaching. This study provides valuable insights for educators and researchers in optimizing AI applications to improve the quality of English language learning or teaching while mitigating potential challenges. ABSTRAK Peran kecerdasan buatan dalam pembelajaran dan pengajaran bahasa Inggris menjadi semakin signifikan di era digital saat ini. Penelitian ini bertujuan untuk menganalisis penggunaan AI dalam Pengajaran Bahasa Inggris (ELT) dengan mengidentifikasi perangkat AI yang paling umum digunakan, manfaatnya, dan tantangan dalam implementasinya. Dengan menggunakan pendekatan tinjauan sistematis yang dipandu oleh PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), penelitian ini secara sistematis mengumpulkan, memilih, dan menganalisis literatur dari basis data akademis seperti Google Scholar, ScienceDirect, ERIC, DOAJ, dan Mendeley. Artikel disaring berdasarkan beberapa kriteria seperti relevansi, kemutakhiran (diterbitkan dalam lima tahun terakhir), dan aksesibilitas (akses terbuka, bahasa Inggris). Hasil penelitian menunjukkan bahwa teknologi AI seperti chatbots, alat koreksi tata bahasa, sistem pengenalan suara, dan platform penerjemahan mesin digunakan secara luas untuk meningkatkan pelafalan, pemahaman mendengarkan, akuisisi kosakata, dan keterampilan menulis siswa. Selain itu, AI juga memfasilitasi pembelajaran yang dipersonalisasi, mengurangi beban kerja guru, dan mempromosikan pendidikan inklusif. Namun, tantangan seperti aksesibilitas yang terbatas, bias algoritmik, dan masalah etika harus diatasi untuk memastikan integrasi AI tetap adil dan efektif dalam pembelajaran atau pengajaran. Studi ini memberikan wawasan yang berharga bagi para pendidik dan peneliti dalam mengoptimalkan aplikasi AI untuk meningkatkan kualitas pembelajaran atau pengajaran bahasa Inggris sekaligus mengurangi potensi tantangan yang ada.

This paper explores the relationship between Artificial Intelligence (AI) integration in the workplace, cultural orientation, and its impact on job autonomy and creative self-efficacy. Our study employs a mixed-method experimental design across 480 individuals from different cultural backgrounds, specifically individualistic (United Kingdom) and collectivistic (Mexico) cultures. We evaluate how they perceive AI's role in their professional lives. We focus on two key aspects: job autonomy, the level of control and discretion employees have over their tasks, and creative self-efficacy, the confidence in one's ability to generate innovative ideas. Our findings revealed a significant increase in job autonomy following AI integration across all participants. Interestingly, this increase was more pronounced in the individualistic participants. Regarding creative self-efficacy, we found gender-specific impacts, with male participants experiencing a decrease, contrary to our expectations. Finally, our results supported the hypothesis that cultural orientation influences perceptions of AI, with collectivistic participants being more receptive to AI integration. These findings have significant implications for organizations integrating AI in multicultural environments. They highlight the importance of considering cultural differences in AI deployment strategies and suggest a need for culturally sensitive AI systems. The study also opens avenues for future research, particularly in exploring the role of other cultural dimensions, conducting longitudinal studies, and investigating ethical and bias-related aspects of AI in the workplace.

This study explores the transformative integration of Artificial Intelligence (AI) into sustainable finance, highlighting its potential to redefine financial practices in alignment with Environmental, Social, and Governance (ESG) criteria. Through a systematic review of current practices and an analysis of AI's applications, challenges, and strategic frameworks, the research elucidates AI's role in enhancing financial operations' efficiency, accuracy, and sustainability. Findings indicate that AI technologies, such as the Financial Maximally Filtered Graph (FMFG) algorithm, significantly improve the processing and analysis of vast datasets, facilitating sustainable investment decisions. However, the integration of AI into sustainable finance is accompanied by ethical, regulatory, and technological challenges. The study proposes strategic recommendations for overcoming these barriers, emphasizing the development of robust policy frameworks, industry best practices, and a balanced approach to AI integration. The conclusion underscores the promise of AI in advancing sustainable finance, offering insights for stakeholders on navigating the complexities of this integration to achieve a more sustainable and resilient financial system.