AI – Transforming Knowledge Across Disciplines

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) 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.

The development of Artificial Intelligence (AI) is rapidly advancing, and AI tools are being integrated into many aspects of daily life, including medical care and public health. The full extent to which AI related tools can be implemented in order to strengthen health systems are a matter of continued debate. The Center for International Health at Ludwig-Maximilians-Universität's (CIHLMU) 2025 student-led symposium on "The Role of Artificial Intelligence in Health Systems Strengthening to Achieve One Health" deliberated on the transformative potential of AI in global health. The primary focus of the symposium was to explore how AI can be leveraged to strengthen health systems. Presentations were delivered by health experts on AI from Africa and Europe. The event provided a platform for experts and students to discuss how AI can be harnessed to improve healthcare delivery, disease surveillance, and research. Discussions resonated around AI health-related research, responsible AI integration, advocacy for AI-related policies, and challenges associated with AI integration in health, such as data privacy and the need for robust governance frameworks. Emphasis was placed on the importance of context-specific implementation, interdisciplinary collaboration, and robust governance to ensure AI's responsible integration into One Health approaches. The symposium concluded that AI holds immense promise to strengthen health systems by improving efficiency, equity, and responsiveness. However, ethical, infrastructural, and regulatory challenges must be addressed, particularly in low- and middle-income countries (LMICs).

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.

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.

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.

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.

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.

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 research explores the evolving landscape of literary analysis through the integration of Artificial Intelligence (AI) and traditional human scholarship. The primary objective is to assess the extent to which AI can enhance the analysis of literary texts by examining its performance in uncovering thematic and stylistic elements within William Shakespeare's "Hamlet." This study employs a mixedmethods research approach, combining qualitative and quantitative techniques to provide a comprehensive evaluation. In the digital age, AI has emerged as a promising tool for text analysis, offering efficiency and scalability. However, it raises fundamental questions about its ability to grasp the profound nuances, cultural contexts, and thematic richness inherent in literary works. Through meticulous comparative and thematic analyses, this research investigates the strengths and limitations of AI in literary analysis, juxtaposing its findings with traditional human interpretations. The results of our study reveal that AI excels in identifying patterns, themes, and stylistic markers within "Hamlet." It effectively recognizes key themes such as revenge, madness, and moral corruption. However, AI's analysis often lacks the depth and contextual understanding present in traditional critiques, particularly in interpreting abstract motifs and cultural references. Our findings underscore the complementary nature of AI and human scholarship in literary analysis. While AI offers quantitative efficiency and objectivity, human interpretation provides the depth, cultural insights, and emotional resonance necessary for a comprehensive understanding of literary works. We argue for a harmonious future where AI augments human expertise, leading to more profound insights and a richer literary scholarship. This research not only contributes to the field of literary analysis but also offers a broader perspective on the evolving relationship between technology and human creativity. As AI technologies advance, the collaborative synergy between AI's quantitative efficiency and human interpretation's qualitative depth promises to reshape the landscape of literary studies, enriching our understanding of literature across diverse genres, time periods, and cultural contexts.

The integration of Artificial Intelligence (AI) in personalized medicine has revolutionized healthcare by enabling precise, data-driven, and patient-specific treatment strategies. AI-powered algorithms, particularly those leveraging machine learning (ML) and deep learning (DL), have enhanced the ability to analyze vast datasets, uncover hidden patterns, and generate predictive models that facilitate early disease detection, drug discovery, and customized treatment regimens. AI applications in genomics, medical imaging, and electronic health records (EHRs) have significantly contributed to the advancement of precision medicine, ensuring more accurate diagnoses and effective therapies.Despite these remarkable advancements, the implementation of AI in personalized medicine presents several challenges. Data privacy and security concerns are at the forefront, as the use of AI relies heavily on patient data, which necessitates strict regulatory compliance and ethical considerations. Additionally, biases in AI algorithms due to imbalanced training datasets can lead to disparities in medical outcomes, disproportionately affecting underrepresented populations. The integration of AI into clinical workflows is another significant hurdle, as healthcare providers require specialized training to interpret AI-generated insights and incorporate them into patient care effectively. Moreover, the need for standardized protocols and regulatory frameworks remains critical to ensuring the reliability, safety, and ethical application of AI in medical practice.Opportunities for AI in personalized medicine continue to expand with advancements in computational power, data analytics, and collaborative efforts between medical researchers and AI developers. Emerging technologies such as explainable AI (XAI) aim to enhance transparency in decision-making, allowing physicians and patients to better understand AI-generated recommendations. Additionally, federated learning techniques provide a promising solution to data-sharing challenges by enabling AI models to be trained across multiple institutions while preserving patient privacy. The convergence of AI with other innovations, such as blockchain for secure data management and the Internet of Medical Things (IoMT) for real-time patient monitoring, further strengthens its role in personalized medicine.This paper explores the transformative potential of AI in personalized medicine, analyzing its key applications, limitations, and future prospects. A thorough examination of current AI-driven methodologies, case studies, and policy considerations will provide a holistic understanding of the evolving landscape. While AI holds immense promise in improving patient outcomes through tailored treatments, addressing its challenges through interdisciplinary collaboration and regulatory advancements is crucial to maximizing its benefits. As AI continues to shape the future of medicine, a balanced approach that integrates technological innovation with ethical responsibility will be essential in harnessing its full potential for personalized healthcare solutions.

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

ABSTRACTThe integration of artificial intelligence (AI) into education has the potential to revolutionize how students engage in academic activities and tasks. This research empirically analyses the influence of AI on creative ideation within educational settings to validate AI's role in enhancing human creativity since creative tasks, which inherently rely on human intuition, emotion and divergent thinking, may be stifled by systematic AI tools. The study explores whether ChatGPT can aid the creative process or inadvertently limit human creativity with a mixed‐method approach consisting of a randomized controlled experiment with third‐year engineering students in which a total of 728 ideas were obtained, along with a structured survey. The results revealed a predominantly positive perception towards AI‐assisted ideation; nevertheless, concerns were raised about AI's influence on creativity and innovation. While no significant differences in ideation outcomes were observed between the groups that used AI and those that did not, significant differences emerged between students who had prior experience with ChatGPT and those who did not. Qualitative insights provided a nuanced view of student experiences on blended human–AI ideation processes, shedding light on its advantages and disadvantages in educational practices. This research also underscores critical considerations and potential risks associated with the adoption of AI, suggesting that while AI has a place in educational settings, its role should be carefully calibrated to support rather than stifle student creativity and innovation. From the findings, the study provides practical recommendations and best practices regarding the integration of AI tools in education.

Objective: This article undertakes a comprehensive exploration of the constructivist paradigm in artificial intelligence (AI) development, aiming to uncover how constructivist perspectives shape our understanding of AI. It delves into the evolution of AI thought, emphasizing the significance of constructivist epistemology in comprehending AI's philosophical and cognitive dimensions. Method: The study employs a variety of philosophical methodologies, including historical-philosophical analysis, comparative analysis of philosophical teachings, and a system-structural dialectical approach. These methods facilitate an in-depth examination of AI's conceptual intricacies within a constructivist framework, focusing on the relationship between artificial and natural intelligence and the epistemological implications of AI. Results: The investigation reveals that the main challenge in AI research is the absence of clear problem-solving rules, highlighting the current limitations of human self-knowledge in logical and emotional intelligence. It showcases AI's vast capabilities, from extensive knowledge bases to real-time processing, and emphasizes AI's role in enhancing human cognitive processes. Conclusions: Artificial intelligence, as a construct of human intellect, mirrors the capacity for design and creativity inherent in human thought. The study underscores AI's foundational role in the epistemology of science and technology, advocating for a holistic understanding of the human brain as a dynamic system to further our grasp of AI and its cognitive potential.

Artificial intelligence (AI) is increasingly integrated into nursing education and healthcare, emphasizing its significance, applications, benefits, and challenges. AI in nursing curricula focuses on equipping students with essential skills to deliver safe and effective patient care in a rapidly evolving healthcare environment. Applications include AI-driven clinical decision support systems, simulations, virtual patients, and exploration of AI ethics. These tools enhance critical thinking, decision-making, and data analysis in healthcare contexts.This review summarizes AI's role in clinical practice, covering disease diagnosis, treatment planning, patient engagement, and ethical considerations while highlighting the need for human expertise in AI adoption.: 1. To enhances understanding of AI’s significance in Nursing Education; 2. To explore the impact of applying AI in nursing Education. 3. To promote AI usage in Nursing Institutions.This review analyzed AI's integration into healthcare and nursing education using indexed literature from PubMed, Scopus, and EMBASE. Key issues include data privacy, algorithm transparency, and biases, requiring responsible AI implementation. Effective strategies include curriculum design, faculty training, hands-on practice, industry collaboration, and continuous learning. Research highlights AI's role in improving diagnosis, treatment planning, personalized medicine, mental health support, and patient education while enhancing accuracy, reducing costs, and minimizing errors. Scholars have explored virtual simulations, faculty and student perspectives, AI competencies, and ethical concerns. Academic journals, conferences, and credible online sources provide valuable insights into AI's impact on nursing education and student outcomes.: In conclusion, integrating AI into nursing education is a developing field with great potential to enhance learning and prepare nurses for AI-driven healthcare. Research covers topics like virtual simulations, AI competencies, ethical concerns, and stakeholder perspectives. Key resources include academic journals, conferences, and online databases.AI supports disease diagnosis, personalized treatment, and clinical decision-making, aiming to improve patient care rather than just automating tasks. However, challenges like data privacy, bias, and the need for human expertise must be addressed.By tackling these challenges and promoting responsible AI use, nurse educators can equip future nurses with the skills needed for the evolving healthcare landscape.