Emergence Of Artificial Intelligence Research Articles

Artificial intelligence in music: recent trends and challenges

AbstractMusic has always been an essential aspect of human culture, and the methods for its creation and analysis have evolved alongside the advancement of computational capabilities. With the emergence of artificial intelligence (AI) and one of its major goals referring to mimicking human creativity, the interest in music-related research has increased significantly. This review examines current literature from renowned journals and top-tier conferences, published between 2017 and 2023, regarding the application of AI to music-related topics. The study proposes a division of AI-in-music research into three major categories: music classification, music generation and music recommendation. Each category is segmented into smaller thematic areas, with detailed analysis of their inter- and intra-similarities and differences. The second part of the study is devoted to the presentation of the AI methods employed, with specific attention given to deep neural networks—the prevailing approach in this domain, nowadays. In addition, real-life applications and copyright aspects of generated music are outlined. We believe that a detailed presentation of the field along with pointing out possible future challenges in the area will be of some value for both the established AI-in-music researchers, as well as the new scholars entering this fascinating field.

Enhancing Conversations in Migrant Counseling Services: Designing for Trustworthy Human-AI Collaboration

Migrants often face a complex situation for navigating services and administrative processes to transition and integrate into a new environment. They rely on guidance from service advisors and informal peers like other migrants to assist them. Service advisors try to understand, explore, and resolve migrant concerns through conversational interaction, while often seeking advice from other domain experts. The emergence of artificial intelligence (AI)-mediated communication holds the potential for enhancing communication between migrants and service advisors while leveraging the expertise of service advisors and facilitating migrants' autonomy in the integration process. Our research investigates the social context, mental models, and challenges encountered in guidance counseling for migrants in Finland, while examining what role AI-mediated communication can play in supporting the process. Based on our findings, we propose design implications for improving shared understanding between service advisors and migrants through salient summarization of conversations and creating collaborative visual narratives. Our findings also indicate the crucial role of leveraging peer-based knowledge between (and within) service advisors, domain experts, and migrants, which can be facilitated by AI-based tools. Our exploratory research offers insights into human-centered AI integration in the migration process, as well as the opportunities and challenges of using AI-mediated communication to improve the migrant experience.

PENDIDIKAN AGAMA KRISTEN DALAM ERA AI: MENGGUNAKAN KECERDASAN BUATAN UNTUK PERSONALISASI PEMBELAJARAN SPIRITUAL

The digital era and the emergence of Artificial Intelligence (AI) have presented a significant shift in teaching methods, including in the realm of Christian Religious Education (PAK). This article explores the integration of AI in PAK, with a focus on personalizing spiritual learning. Using a qualitative method with a literature study approach, this study analyzes current sources to understand the potential and challenges of using AI in the context of spiritual education. The results of the study show that AI offers significant opportunities to improve the quality and effectiveness of PAK through personalized learning, deeper analysis of religious texts, and wider access to educational resources. However, the application of AI also presents ethical challenges, including concerns about data privacy, the potential erosion of relational aspects in education, and the risk of widening the digital divide. The study emphasizes the importance of a balanced approach to integrating AI, which involves training educators, developing ethical frameworks, and ongoing theological reflection. In conclusion, while AI has transformative potential in PAKs, its application must be done carefully to ensure that technology supports, not replaces, important aspects of Christian religious education, including authentic character building and spiritual growth.

The effect of gamified project-based learning with AIGC in information literacy education

ABSTRACT The emergence of artificial intelligence has necessitated a transformation in pedagogical methodologies. This study evaluated the integration of gamified project-based learning (GPBL) and artificial intelligence-generated content (AIGC) tools in enhancing information literacy education. The primary objectives were to explore the relationship among students’ learning experiences, information literacy, creativity, and engagement in an integrated learning environment. Using an explanatory sequential design with surveys and interviews, first-year university students (n = 36) from the School of Art and Design participated. Results indicated that students perceived moderately high levels of engagement(3.72) and creativity(4.11) during the GPBL activities, with positive emotions contributing significantly to motivation. Moreover, information literacy skills have substantially improved, such as in information retrieval, using AIGC tools, and understanding the ethical aspects of AI technology. A process model illustrated how positive emotions, motivation, creativity, engagement, and information literacy were linked in a GPBL-incorporating AI setting.

The role of artificial intelligence in the management of liver diseases.

Universal neonatal hepatitis B virus (HBV) vaccination and the advent of direct-acting antivirals (DAA) against hepatitis C virus (HCV) have reshaped the epidemiology of chronic liver diseases. However, some aspects of the management of chronic liver diseases remain unresolved. Nucleotide analogs can achieve sustained HBV DNA suppression but rarely lead to a functional cure. Despite the high efficacy of DAAs, successful antiviral therapy does not eliminate the risk of hepatocellular carcinoma (HCC), highlighted the need for cost-effective identification of high-risk populations for HCC surveillance and tailored HCC treatment strategies for these populations. The accessibility of high-throughput genomic data has accelerated the development of precision medicine, and the emergence of artificial intelligence (AI) has led to a new era of precision medicine. AI can learn from complex, non-linear data and identify hidden patterns within real-world datasets. The combination of AI and multi-omics approaches can facilitate disease diagnosis, biomarker discovery, and the prediction of treatment efficacy and prognosis. AI algorithms have been implemented in various aspects, including non-invasive tests, predictive models, image diagnosis, and the interpretation of histopathology findings. AI can support clinicians in decision-making, alleviate clinical burdens, and curtail healthcare expenses. In this review, we introduce the fundamental concepts of machine learning and review the role of AI in the management of chronic liver diseases.

Emergence of Artificial Intelligence on Projected U.S. Employment

The recent breakthroughs in artificial intelligence (AI) and its rapid integration into various aspects of life have become a focal point of concern, especially in regards to potential consequences on the workforce. Since AI has seen the most transformation post-2020, this study aims to analyze the role of AI exposure within an occupation on projected 10-year changes in employment using the most recent available data from 2022 (pulled from the U.S. Bureau of Labor Statistics). Through forward selection in ordinary least squares method, after controlling for task offshorability, education, work experience, and on-the-job training, it was found that a 1 standard deviation increase in AI exposure index is associated with a projected decrease of 1.043 percentage points in jobs on average within an occupation. This finding supports the growing concern of AI-driven job displacement and/or unemployment, which could heighten the burden on the government to fund welfare programs and potentially lead to recession. This paper further highlights the need for policy discussions on fostering a more balanced integration of AI into the workforce, possibly through specialized education and retraining programs.

Artificial intelligence for brain disease diagnosis using electroencephalogram signals.

Brain signals refer to electrical signals or metabolic changes that occur as a consequence of brain cell activity. Among the various non-invasive measurement methods, electroencephalogram (EEG) stands out as a widely employed technique, providing valuable insights into brain patterns. The deviations observed in EEG reading serve as indicators of abnormal brain activity, which is associated with neurological diseases. Brain‒computer interface (BCI) systems enable the direct extraction and transmission of information from the human brain, facilitating interaction with external devices. Notably, the emergence of artificial intelligence (AI) has had a profound impact on the enhancement of precision and accuracy in BCI technology, thereby broadening the scope of research in this field. AI techniques, encompassing machine learning (ML) and deep learning (DL) models, have demonstrated remarkable success in classifying and predicting various brain diseases. This comprehensive review investigates the application of AI in EEG-based brain disease diagnosis, highlighting advancements in AI algorithms.

Exploration of Teaching Reform Strategies in Higher Vocational Colleges Based on Artificial Intelligence

The emergence of artificial intelligence and its extensive application in the field of education have exerted a great influence on promoting the reform of school education, making the educational and teaching work in higher vocational colleges show a new development trend. Starting from the general background of the era of artificial intelligence, this paper analyzes the important significance of the era of artificial intelligence on the educational reform in higher vocational colleges and the challenges faced by the educational and teaching reform in higher vocational colleges. On this basis, the educational reform strategies are proposed, hoping to highlight the educational characteristics of higher vocational colleges in the era of artificial intelligence and comprehensively promote the efficient progress of educational and teaching work in higher vocational colleges.

Curriculum reform-necessity for emerging VUCA worlds to enhance pre-service teachers’ pedagogy

The economic meltdowns across countries are exacerbated by COVID-19 and its aftermath, which bear the brunt of perennial devastations characterised by abject poverty. Although somewhat peculiar, higher institutions of education (HIE) have modified learning and teaching strategies to accommodate the deleterious effects of COVID-19. Conversely, while adapting, the emergence of artificial intelligence (AI) necessitates that HIE reprioritises a plethora of factors, including pedagogies. This approach catalyses the comprehension of volatile, uncertain, complex, and ambiguous (VUCA) environments in economic sectors. Hence, this paper aims to evaluate the use of pre-service teachers’ AI skills and digital pedagogy at universities to determine the need to reform the curriculum for the purposes of navigating the VUCA world. Likewise, critical theory is utilised to understand the necessity of digital skills for pre-service teachers to enable HIE’s adaptation to VUCA environments. This mixed-method study involves purposively sampling 12 participants for interviews and randomly sampling 78 participants for surveys. Using critical discourse analysis, the data is analysed to postulate nuances of perspectives. The findings depict that, despite universities adapting to innovative methods, particularly AI, there is a paucity of AI content and infrastructural development that enables pre-service teachers to acquire the digital competencies and skills needed to teach robotics, coding, and maritime studies. Overall, digital skills are below average; hence, the paper recommends that universities reform their curricula to stimulate digital skills that reflect the requisite capabilities of digitalisation and AI to withstand the VUCA world.

Criminology and Corporate Crime: The Art of Scientific Cross-Pollination

Born of sociology while absorbing ideas and scholarship from other specialties, criminology can legitimately tout its interdisciplinary bona fides. Yet within the field, integration and cross-pollination across subject areas is, far too often, absent. Concentrating on corporate crime and summarizing the literature across a variety of different domains, I demonstrate that criminology, as a discipline, benefits from knowledge generated by corporate crime scholarship and vice versa. I discuss why it is essential to build a multidisciplinary knowledge base that informs and draws from corporate crime scholarship while also addressing critical epistemological challenges and knowledge gaps that confound integrative efforts. I conclude with potential areas of synergy ranging from the theoretical (organizational life cycle/life course and decision-making in different contexts) to new/old forms of crime and crime control associated with the emergence of artificial intelligence and machine learning.

AI-Based solutions for current challenges in regenerative medicine

The emergence of Artificial Intelligence (AI) and its usage in regenerative medicine represents a significant opportunity that holds the promise of tackling critical challenges and improving therapeutic outcomes. This article examines the ways in which AI, including machine learning and data fusion techniques, can contribute to regenerative medicine, particularly in gene therapy, stem cell therapy, and tissue engineering. In gene therapy, AI tools can boost the accuracy and safety of treatments by analyzing extensive genomic datasets to target and modify genetic material in a precise manner. In cell therapy, AI improves the characterization and optimization of cell products like mesenchymal stem cells (MSCs) by predicting their function and potency. Additionally, AI enhances advanced microscopy techniques, enabling accurate, non-invasive and quantitative analyses of live cell cultures. AI enhances tissue engineering by optimizing biomaterial and scaffold designs, predicting interactions with tissues, and streamlining development. This leads to faster and more cost-effective innovations by decreasing trial and error. The convergence of AI and regenerative medicine holds great transformative potential, promising effective treatments and innovative therapeutic strategies. This review highlights the importance of interdisciplinary collaboration and the continued integration of AI-based technologies, such as data fusion methods, to overcome current challenges and advance regenerative medicine.

Can artificial intelligence aid the urologists in detecting bladder cancer?

ABSTRACT Introduction: The emergence of artificial intelligence (AI)-based support system endoscopy, including cystoscopy, has shown promising results by training deep learning algorithms with large datasets of images and videos. This AI-aided cystoscopy has the potential to significantly transform the urological practice by assisting the urologists in identifying malignant areas, especially considering the diverse appearance of these lesions. Methods: Four databases, the PubMed, ProQuest, EBSCOHost, and ScienceDirect were searched, along with a manual hand search. Prospective and retrospective studies, experimental studies, cross-sectional studies, and case–control studies assessing the utilization of AI for the detection of bladder cancer through cystoscopy and comparing with the histopathology results as the reference standard were included. The following terms and their variants were used: “artificial intelligence,” “cystoscopy,” and “bladder cancer.” The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A random effects model was used to calculate the pooled sensitivity and specificity. The Moses–Littenberg model was used to derive the Summary Receiver Operating Characteristics (SROC) curve. Results: Five studies were selected for the analysis. Pooled sensitivity and specificity were 0.953 (95% confidence interval [CI]: 0.908–0.976) and 0.957 (95% CI: 0.923–0.977), respectively. Pooled diagnostic odd ratio was 449.79 (95% CI: 12.42–887.17). SROC curve (area under the curve: 0.988, 95% CI: 0.982–0.994) indicated a strong discriminating power of AI-aided cystoscopy in differentiation normal or benign bladder lesions from the malignant ones. Conclusions: Although the utilization of AI for aiding in the detection of bladder cancer through cystoscopy remains questionable, it has shown encouraging potential for enhancing the detection rates. Future studies should concentrate on identification of the patients groups which could derive maximum benefit from accurate identification of the bladder cancer, such as those with intermediate or high-risk invasive tumors.

Artificial Intelligence in Education Management: Opportunities, Challenges, and Solutions

Education management, as an integral part of management science, encompasses core functions such as organizing, planning, coordinating, and controlling educational resources, which closely align with the management concepts used in business administration. Whether in the field of education or business, managers need to improve organizational performance through effective strategy development, resource allocation, and decision-making support. The emergence of Artificial Intelligence (AI) has profoundly transformed several industries, and its impact on education is progressively gaining prominence.This paper explores the opportunities, challenges, and solutions associated with the integration of AI in the education sector. AI offers substantial benefits such as personalized learning experiences, automated educational management, and global access to shared educational resources, revolutionizing the traditional educational landscape. However, its application also presents significant challenges, including concerns over data privacy, the widening of the digital divide, and the evolving role of teachers.Through an analysis of both opportunities and challenges, this paper emphasizes the need for strategic measures to ensure the ethical and equitable use of AI in education. These measures include stronger policy frameworks for data protection, comprehensive teacher training programs for AI integration, and the promotion of global cooperation to bridge technological gaps in underdeveloped regions. Furthermore, the paper projects the potential future developments in AI-driven education, highlighting the importance of balancing technology with human interaction in the learning environment.By addressing both the benefits and risks associated with AI, this paper provides actionable insights for educators, policymakers, and technologists, aimed at creating a more inclusive, efficient, and innovative educational system. Future research should continue to examine AI's ethical implications and strive for solutions that ensure education remains accessible and fair in an increasingly AI-driven world.

Artificial intelligence scribe: A new era in medical documentation

The high workloads involved in clinical documentation represent one of the major factors contributing to the significant escalation of clinician burnout. The emergence of artificial intelligence (AI) has provided new avenues for relieving this burden by automating certain tasks like clinical documentation through the generation of clinical notes from a transcript of a clinical encounter. The advances in large language models (LLMs) have led to the emergence of such startups, but they come with their own set of challenges, predominantly surrounding the concerns of documentation accuracy, completeness, and data security. These can be addressed with a multi-faceted approach which could include fine-tuning the currently available models; using domain-specific models and in-house AI systems to ensure data security; and involving smaller LLMs and clinicians in the development and implementation of such systems. We can imagine a future where these systems are deeply incorporated into electronic health records, providing not only automated clinical documentation but also improving Clinical Decision Support systems, research, and patient communication.

NGHIÊN CỨU TỔNG QUAN VIỆC SỬ DỤNG ỨNG DỤNG TRÍ TUỆ NHÂN TẠO TRONG VIỆC XÂY DỰNG VIDEO HỌC LIỆU SỐ

Developing digital learning materials, especially video lectures, is becoming an important issue in education. The emergence of artificial intelligence (AI) has made the creation of videos easier compared to the traditional approach. However, the education sector has yet to benefit from these advanced technologies fully. This research explores the application of Artificial Intelligence (AI) in creating digital learning videos. The study uses 43 selected articles and the PRISMA analysis model to search, classify, and content-filter the results. The findings show a noticeable growth in using AI to create digital learning videos, with 33 topics formed from keywords and articles published in various journals. The most influential works include automatic content extraction from videos and virtual teachers, and there is significant interest in ChatGPT. The main research topics include • developing effective AI models to convert text to video, • integrating personalization and interaction features, and • applying these digital learning videos in education and training. However, many issues remain to be researched, such as enhancing the context-understanding capabilities of AI models and building frameworks to evaluate the effectiveness of videos. AI-generated videos' quality, reliability, and flexibility are significant unresolved challenges, opening up research gaps for future scholars. Keywords: Artificial Intelligence (AI); video; education; learning; PRISMA.

Empowering virtual collaboration: harnessing AI for enhanced teamwork in higher education

The emergence of Artificial Intelligence (AI) has brought about a significant change in higher education. It has led to the adoption of more digitally advanced and collaborative models. This paper examines the potential of AI in promoting dynamic virtual teamwork and improving the collective experience in the academic world. It discusses how AI tools can be integrated into various sectors of virtual teamwork, such as academic learning, group projects, communication, assessment, research collaboration, administrative efficiency, engagement strategies, and continuous feedback mechanisms. The paper provides a comprehensive analysis of AI's role in these areas, showing how AI can personalize learning, facilitate complex group tasks, streamline communication, and provide real-time feedback. Ultimately, this will prepare students for the challenges of the professional world and enhance educational efficacy. The paper evaluates the significance of AI in each sector, offering insights into how higher education institutions can use these technologies to create an environment that fosters advanced virtual collaboration. The paper argues that strategic integration of AI is crucial in equipping students with the necessary skills and competencies for the evolving digital landscape of the 21st century.

Artificial Intelligence Towards Enhancing the Risk Management Practices During the Design Process

Abstract The design process across various industries involves intricate decision-making, problem-solving, and consideration of multiple factors. However, risks during the design process can jeopardize project success and lead to costly errors. Traditional risk management approaches in design often rely on manual & computerized analysis, which is time-consuming, limited in scope, and prone to human biases. The emergence of artificial intelligence (AI) presents an opportunity to revolutionize risk management in design. AI algorithms can process vast amounts of data, identify patterns, and provide predictive insights, enhancing risk identification and mitigation. This research aims to investigate the relationship between design risks and AI towards enhancing the risk management process. Using a mixed methodology approach, a qualitative method was used through investigating previous literature to identify traditional risk management limitations, design risks and AI tools and methods. Secondly, relationship between identified risks and AI tools was proposed. This was followed by quantitative method through case studies analysis that assess the validity of the proposed relationship. The goal is to establish a new paradigm where AI and risk management converge to create a future where risks that occur during the design process can effectively be identified and managed, fostering innovation and improving project outcomes.

Uncovering the scientific landscape: A bibliometric and visualized analysis of artificial intelligence in traditional Chinese medicine

The emergence of artificial intelligence (AI) technology has presented new challenges and opportunities for Traditional Chinese Medicine (TCM), aiming to provide objective assessments and improve clinical effectiveness. However, there is a lack of comprehensive analyses on the research trajectory, key directions, current trends, and future perspectives in this field. This research aims to comprehensively update the progress of AI in TCM over the past 24 years, based on data from the Web of Science database covering January 1, 2000, to March 1, 2024. Using advanced analytical tools, we conducted detailed bibliometric and visual analyses. The results highlight China's predominant influence, contributing 54.35 % of the total publications and playing a key role in shaping research in this field. Significant productivity was observed at institutions such as the China Academy of Chinese Medical Sciences, Beijing University of Chinese Medicine, and Shanghai University of Traditional Chinese Medicine, with Wang Yu being the most prolific contributor. The journal Molecules contributed the most publications in this field. This study identified hepatocellular carcinoma, chemical and drug-induced liver injury, Papillon-Lefèvre disease, Parkinson's disease, and anorexia as the most significant disorders researched. This comprehensive bibliometric assessment benefits both seasoned researchers and newcomers, offering quick access to essential information and fostering the generation of innovative ideas in this field.

EMPOWERING PATIENTS WITH AI-DRIVEN PERSONALIZED MEDICINE: A PARADIGM SHIFT IN CHRONIC DISEASE MANAGEMENT

The emergence of artificial intelligence (AI) in healthcare has opened the door to a disruptive approach to chronic illness management, centered on individualized therapy that empowers patients. This research investigates the potential of AI-driven personalized medicine to transform the treatment and management of chronic illnesses by adapting therapeutic tactics to individual patient characteristics. Traditional chronic illness management approaches frequently rely on standardized therapies, which may fail to account for a patients unique genetic, environmental, and lifestyle characteristics. AI, with its capacity to analyze large datasets and discover patterns, provides a more sophisticated approach, allowing for the development of personalized treatment regimens that maximize efficacy while minimizing side effects.The use of AI in healthcare enables real-time monitoring and continual customization of treatment regimens depending on patient reactions, promoting a proactive approach to illness management. Patients are no longer passive beneficiaries of care, but active players in their treatment journey, armed with personalized insights that allow for informed decision-making. This trend toward patient-centered care is especially important in the case of chronic illnesses, where long-term management and adherence to treatment procedures are vital for improving results. Furthermore, AI-powered tailored medicine has the potential to save healthcare costs by eliminating trial-and-error methods and averting consequences through early intervention. However, implementing this paradigm change is not without its problems. Data privacy, ethical concerns, and the need for strong regulatory frameworks must all be addressed to enable the safe and successful use of AI in customized medicine.AI-powered personalized medicine offers a paradigm change in chronic illness treatment, providing a more effective, patient-centered approach that has the potential to enhance clinical results and empower individuals. By leveraging AI, healthcare systems may transition to a future in which therapy is personalized to the individual, improving care quality and, eventually, altering the landscape of chronic illness management.

CUTTING TOOLS AND THEIR DESIGN FEATURES: FROM THE POINT OF VIEW OF ARTIFICIAL INTELLIGENCE

Cutting tools are fundamental components in manufacturing and engineering processes which are essential for shaping, cutting, and decided materials like metal, wood and composites. The design of these tools calls for a delicate balance between choice of materials, geometry and performance criteria whose parameters are tailored. The emergence of artificial intelligence (AI) has turned this world all upside down. AI first uses machine learning algorithms to analyze large quantities of data about material properties, wear resistance, or cutting conditions leading to new tool designs for a given material. In addition, AI also allows real-time monitoring during machining and with adaptive control. It can improve precision as well as reduce tool wear by selecting the right speed when cutting, while slow down results in prevention of damage. Furthermore, AI-driven simulations have enabled engineers to simulate and optimize tool design in virtual environments prior to physical production. AI will continue to advance in the future holds out the promise of decreasing still further the cost and improving the performance characteristics of cutting tools. It is the combination of AI and cutting tool design to gain maximum benefits of this innovation while minimizing costs for now in the future.