AI-related Research Research Articles

Research on Whether Artificial Intelligence Affects Industrial Carbon Emission Intensity Based on the Perspective of Industrial Structure and Government Intervention

Artificial intelligence serves as the fundamental catalyst for a new wave of technological innovation and industrial transformation. It holds vital importance in reaching carbon reduction targets and the objectives of “carbon peak and neutrality”. This factor contributes significantly to the reduction in carbon emissions in the industrial domain. This article utilizes panel data from 30 provinces in China, covering the years 2013 to 2021, to develop an evaluation framework for assessing the progress of artificial intelligence development. Through the use of double fixed-effect models, mediation effect models, and threshold effect models, the empirical analysis examines the industrial carbon reduction effects of artificial intelligence and its operating mechanisms. Research indicates that the advancement of AI can significantly reduce carbon emission intensity within the industrial sector. This conclusion remains valid following comprehensive robustness tests. Furthermore, there exists temporal and regional variability in AI’s impact on industrial carbon reduction, particularly more pronounced after 2016 and in central and western regions. AI influences carbon emission reduction in China’s industrial sector through the advancement and optimization of industrial structures. Here, the increase in senior-level operations acts as a partial masking effect, while optimization serves as a partial mediator. The relationship between AI and industrial carbon emission intensity is non-linear, being influenced by the threshold of government intervention; minimal intervention weakens AI’s effect on carbon intensity reduction. These findings enhance our understanding of the factors influencing industrial carbon emissions and contribute to AI-related research. They also lay a solid empirical groundwork for promoting carbon emission reduction in the industrial domain via AI. Additionally, the results offer valuable insights for formulating policies aimed at the green transformation of industry.

유아교육 분야의 인공지능 관련 국내연구동향 분석

This study was conducted to analyze the trends of AI-related papers published in the field of early childhood education. Using the keywords ‘artificial intelligence’, ‘early childhood’, ‘education’, and ‘robot’ in combination, academic journal articles and dissertations published in Korea after 2023 were retrieved from RISS. Based on the selection criteria, the number of papers by year, research subjects, research methods, and topics were calculated, and the results were analyzed by subject. In 2023, 24 papers were published, and by June 2024, 15 papers had been published. The main research subjects were in-service teachers, followed by pre-service teachers, and children, with relatively fewer studies targeting parents. Studies on program and scale development, as well as theoretical reviews, were also published. Quantitative research was the most common research method, with survey research being the most frequently conducted among the quantitative studies. By topic, there were many studies on perceptions of artificial intelligence, followed by program application and theoretical reviews. The results were analyzed in detail by research topic. This study is significant in that it provides basic data for AI education in the educational field by examining trends in AI-related research in the field of early childhood education published since 2023, and offers insights on future research topics needed in the academic field.

The global evolution and impact of systems biology and artificial intelligence in stem cell research and therapeutics development: a scoping review.

Advanced bioinformatics analysis, such as systems biology (SysBio) and artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL), is increasingly present in stem cell (SC) research. An approximate timeline on these developments and their global impact is still lacking. We conducted a scoping review on the contribution of SysBio and AI analysis to SC research and therapy development based on literature published in PubMed between 2000 and 2024. We identified an 8 to 10-fold increase in research output related to all 3 search terms between 2000 and 2021, with a 10-fold increase in AI-related production since 2010. Use of SysBio and AI still predominates in preclinical basic research with increasing use in clinically oriented translational medicine since 2010. SysBio- and AI-related research was found all over the globe, with SysBio output led by the (US, n = 1487), (UK, n = 1094), Germany (n = 355), The Netherlands (n = 339), Russia (n = 215), and France (n = 149), while for AI-related research the US (n = 853) and UK (n = 258) take a strong lead, followed by Switzerland (n = 69), The Netherlands (n = 37), and Germany (n = 19). The US and UK are most active in SCs publications related to AI/ML and AI/DL. The prominent use of SysBio in ESC research was recently overtaken by prominent use of AI in iPSC and MSC research. This study reveals the global evolution and growing intersection among AI, SysBio, and SC research over the past 2 decades, with substantial growth in all 3 fields and exponential increases in AI-related research in the past decade.

Trend of Artificial Intelligence in Nursing from 2004 to 2024: A Bibliometric Analysis Based on Web of Science

This study aims to conduct a bibliometric analysis of studies related to artificial intelligence (AI) in the field of nursing, accessed from the Web of Science database. The search was conducted using the keywords "artificial intelligence OR ChatGPT OR Chatbot) and (nursing OR nursing care) and (practice OR innovation OR machine learning OR deep learning)" between January 01-20, 2024. A total of 164 studies related to artificial intelligence in nursing were identified through the search. It was found that 65.85% of these studies were research articles, with the majority being published in the Journal of Nursing Management (nine studies), and the highest number of studies being published in 2023. The most prolific author, with seven studies, was identified as Rozzano Locsin, while the United States was determined to be the country with the highest number of publications, and Florida Atlantic University and Tokushima University were the institutions with the most studies. The most frequently used keyword was "artificial intelligence," with a total citation count of 1010 and an h-index of 20. The study indicates an increasing interest in AI-related research in nursing, particularly in recent years, with a trend towards quantitative growth.

Application of Artificial Intelligence in rehabilitation science: A scientometric investigation Utilizing Citespace

This study presents a scientometric analysis of the intersection between rehabilitation science and artificial intelligence (AI) technologies, using data from the Web of Science (WOS) database from 2002 to 2022. The analysis employed a comprehensive search query with key AI-related terms, focusing on a wide range of publications in rehabilitation science. Utilizing the Citespace tool, the study visualizes and quantifies the relationships between key terms, identifies research trends, and assesses the impact of AI technologies in rehabilitation science. Findings reveal a significant increase in AI-related research in this field, particularly from 2017 onwards, peaking in 2021. The United States has been a leading contributor, followed by countries like England, Australia, Germany, and Canada. Major institutional contributions come from Harvard University and the Pennsylvania Commonwealth System of Higher Education, among others. A keyword co-occurrence network constructed through Citespace identifies nine distinct hot topics and various research frontiers, highlighting evolving focus areas within the field. Burst analysis of keywords indicates a shift from performance and injury-related research to an increasing emphasis on AI and deep learning in recent years. The study also predicts the potential impact of papers, spotlighting works by Kunze KN and others as significantly influencing future research directions. Additionally, it examines the evolution of knowledge bases in AI-related rehabilitation science research, revealing a multidisciplinary core that includes neurology, rehabilitation, and ophthalmology, extending to complementary fields such as medicine and social sciences. This scientometric analysis provides a comprehensive overview of AI's application in rehabilitation science, offering insights into its evolution, impact, and emerging trends over the past two decades. The findings suggest strategic directions for future research, policy-making, and interdisciplinary collaboration in rehabilitation science and AI.

The influence of artificial intelligence in higher education based on four thematic axes: a bibliometric study

The article presents a structured bibliometric study examining the impact of artificial intelligence (AI) in higher education across four thematic axes: AI in higher education, AI in education review, AI in the teaching-learning process, and AI tools applied to higher education. Research productivity and impact indicators are analyzed using data from major databases like Scopus, Web of Science, and ScienceDirect. Results reveal a significant increase in AI-related research output, particularly in machine learning, data mining, and learning analytics. The study highlights China and the United States as leading contributors to AI research in higher education. The findings highlight AI's evolving role in transforming higher education and the need for multidisciplinary research approaches to address emerging challenges and opportunities. However, limitations include the reliance on quantitative measures, the narrow temporal scope, and the limited focus on high-production countries. Future research should incorporate qualitative methods to explore practical applications and social impacts more comprehensively, consider a broader range of geographic contexts, and discuss ethical considerations around integrating AI into higher education.

Application of AI in Sepsis: Citation Network Analysis and Evidence Synthesis.

Artificial intelligence (AI) has garnered considerable attention in the context of sepsis research, particularly in personalized diagnosis and treatment. Conducting a bibliometric analysis of existing publications can offer a broad overview of the field and identify current research trends and future research directions. The objective of this study is to leverage bibliometric data to provide a comprehensive overview of the application of AI in sepsis. We conducted a search in the Web of Science Core Collection database to identify relevant articles published in English until August 31, 2023. A predefined search strategy was used, evaluating titles, abstracts, and full texts as needed. We used the Bibliometrix and VOSviewer tools to visualize networks showcasing the co-occurrence of authors, research institutions, countries, citations, and keywords. A total of 259 relevant articles published between 2014 and 2023 (until August) were identified. Over the past decade, the annual publication count has consistently risen. Leading journals in this domain include Critical Care Medicine (17/259, 6.6%), Frontiers in Medicine (17/259, 6.6%), and Scientific Reports (11/259, 4.2%). The United States (103/259, 39.8%), China (83/259, 32%), United Kingdom (14/259, 5.4%), and Taiwan (12/259, 4.6%) emerged as the most prolific countries in terms of publications. Notable institutions in this field include the University of California System, Emory University, and Harvard University. The key researchers working in this area include Ritankar Das, Chris Barton, and Rishikesan Kamaleswaran. Although the initial period witnessed a relatively low number of articles focused on AI applications for sepsis, there has been a significant surge in research within this area in recent years (2014-2023). This comprehensive analysis provides valuable insights into AI-related research conducted in the field of sepsis, aiding health care policy makers and researchers in understanding the potential of AI and formulating effective research plans. Such analysis serves as a valuable resource for determining the advantages, sustainability, scope, and potential impact of AI models in sepsis.

Embracing the Era of Artificial Intelligence: Innovation and Preparation in Nursing Education

Rapid recent advances in information technology have opened the door for artificial intelligence (AI)-related technologies to be applied extensively across many industries. The Ministry of Education has emphasized the importance of cultivating advanced-level professionals in diverse fields, particularly in smart machinery, the Asia-Silicon Valley sector, green energy technology, biotechnology, national defense, new agricultural, and circular economy industries, to enhance innovation and promote industrial competitiveness (Kuo, 2019). While interdisciplinary talent in AI and digital innovation is being actively developed elsewhere, nursing education remains in the exploratory phase of AI and digital technology talent cultivation. Although AI is now a well-known term, the competencies required for its application in nursing remain unclear. Moreover, most nursing professionals are unfamiliar with how to best integrate AI into nursing expertise or practice settings. With the application of AI in the healthcare industry now unstoppable, it is vital to consider how to help nursing students adapt to healthcare's new technology landscape (Huang et al., 2021). AI facilitates the digital simulation of human thought patterns, logic, and behaviors with the goal of assisting human users solve problems, especially those that are time-consuming and require repetitive processing. The development of AI requires interdisciplinary collaboration among domain experts, data scientists, software engineers, robotics experts, and computer programmers. Such collaboration is essential to developing products able to meet the demands of the times and to help students become competent future nursing professionals (Murray, 2018). Nurses spend the most time interacting with patients and are thus best able to understand the perceptions and challenges of patients and their families. Collaborating with professionals from interdisciplinary fields is the best strategy for achieving optimal healthcare outcomes. However, nursing schools have yet to provide a clear response to the impact of AI on nursing education. Nursing educational institutions must enable nursing students to comprehend the concepts and principles of AI and equip them with AI literacy to allow them to unleash their potential, continuously innovate, and stay abreast with the times (Ng et al., 2021). In this issue, experts and scholars currently engaged in AI-related research in the nursing discipline share their research findings in the realms of machine learning, deep learning, emotional recognition, and natural language application. These articles offer insights into the implications of AI, suggest how nursing education may best respond to emerging AI trends, and provide the authors' perspectives on nursing education reform. The editor hopes readers will be inspired to explore new concepts, gain a deeper understanding of the application and significance of AI, and apply AI to address clinical and educational challenges to foster competent nursing professionals for tomorrow.

Authorship gender among articles about artificial intelligence in breast imaging

Rationale and ObjectivesThe purpose of this study is to investigate the variance of women authors, specifically first and senior authorship among peer-reviewed artificial intelligence-related articles with a specific focus in breast imaging. Materials and MethodsA strategic search was conducted in July 2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to capture all existing and publicly available peer-reviewed articles intersecting AI and breast imaging. Primary outcomes were first and senior authors’ gender, which were assigned with the aid of an emailed self-declaration survey. Secondary outcomes included country of article, journal impact factor, and year of publication. Comparisons were made using logistic regression models and analysis of variances. Results115 studies were included in the analysis. Women authors represented 35.7% (41/115) and 37.4% (43/115) of first and senior authors, respectively. Logistic regression modelling showed a significant increase in women senior authors over time but no changes in women first authors. Impact factor was not associated with female authorship and certain countries had women authorship reach over 50%. ConclusionThis study demonstrates that there is a significant authorship gender gap in artificial intelligence breast imaging research. An increasing temporal trend of senior authors in breast imaging AI-related research is a promising prognosis for more women voices in this field. Further study needs to be done to understand the reasons behind this gap and any potential implications.

DIGITAL BUSINESS TRANSFORMATION: ANALYSIS OF THE EFFECT ARTIFICIAL INTELLIGENCE IN E-COMMERCE’S PRODUCT RECOMMENDATION

The purpose of this study is to determine whether artificial intelligence used in E-Commerce influences product recommendations for users. This study explains how much influence artificial intelligence on product recommendations supplied by E-commerce in terms of consumer behavior in making purchasing decisions. Research methods. This research used bibliometric analysis to find the mapping of this topic with articles period 2017 to 2023 from Scopus database. Of the 103 articles were showed by keyword and analyzing the articles according to the relate of the content about 29 articles were finally obtained. The research result is Artificial Intelligence has influence for E-commerce, recommendation system, decision support system, customer behaviour’s, and customer trust. Product recommendations have an impact on E-Commerce. Conclusion. However, from the literature review, founded that there are still a few journals discussing related to considerations to the implementation regarding the use of AI in e-commerce "Consumer behaviour", "Customer Trust", "Purchasing decisions". This study is also useful to generate additional AI-related research in e-commerce and unquestionably for a fresh subject will be covered especially in context of product recommendations on E-commerce.

Artificial intelligence applied in cardiovascular disease: a bibliometric and visual analysis.

With the rapid development of technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis prediction of a variety of diseases, including cardiovascular disease. Facts have proved that AI has broad application prospects in rapid and accurate diagnosis. This study mainly summarizes the research on the application of AI in the field of cardiovascular disease through bibliometric analysis and explores possible future research hotpots. The articles and reviews regarding application of AI in cardiovascular disease between 2000 and 2023 were selected from Web of Science Core Collection on 30 December 2023. Microsoft Excel 2019 was applied to analyze the targeted variables. VOSviewer (version 1.6.16), Citespace (version 6.2.R2), and a widely used online bibliometric platform were used to conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field. A total of 4,611 articles were selected in this study. AI-related research on cardiovascular disease increased exponentially in recent years, of which the USA was the most productive country with 1,360 publications, and had close cooperation with many countries. The most productive institutions and researchers were the Cedar sinai medical center and Acharya, Ur. However, the cooperation among most institutions or researchers was not close even if the high research outputs. Circulation is the journal with the largest number of publications in this field. The most important keywords are "classification", "diagnosis", and "risk". Meanwhile, the current research hotpots were "late gadolinium enhancement" and "carotid ultrasound". AI has broad application prospects in cardiovascular disease, and a growing number of scholars are devoted to AI-related research on cardiovascular disease. Cardiovascular imaging techniques and the selection of appropriate algorithms represent the most extensively studied areas, and a considerable boost in these areas is predicted in the coming years.

The global research of artificial intelligence in lung cancer: a 20-year bibliometric analysis.

Lung cancer (LC) is the second-highest incidence and the first-highest mortality cancer worldwide. Early screening and precise treatment of LC have been the research hotspots in this field. Artificial intelligence (AI) technology has advantages in many aspects of LC and widely used such as LC early diagnosis, LC differential classification, treatment and prognosis prediction. This study aims to analyze and visualize the research history, current status, current hotspots, and development trends of artificial intelligence in the field of lung cancer using bibliometric methods, and predict future research directions and cutting-edge hotspots. A total of 2931 articles published between 2003 and 2023 were included, contributed by 15,848 authors from 92 countries/regions. Among them, China (40%) with 1173 papers,USA (24.80%) with 727 papers and the India(10.2%) with 299 papers have made outstanding contributions in this field, accounting for 75% of the total publications. The primary research institutions were Shanghai Jiaotong University(n=66),Chinese Academy of Sciences (n=63) and Harvard Medical School (n=52).Professor Qian Wei(n=20) from Northeastern University in China were ranked first in the top 10 authors while Armato SG(n=458 citations) was the most co-cited authors. Frontiers in Oncology(121 publications; IF 2022,4.7; Q2) was the most published journal. while Radiology (3003 citations; IF 2022, 19.7; Q1) was the most co-cited journal. different countries and institutions should further strengthen cooperation between each other. The most common keywords were lung cancer, classification, cancer, machine learning and deep learning. Meanwhile, The most cited papers was Nicolas Coudray etal.2018.NAT MED(1196 Total Citations). Research related to AI in lung cancer has significant application prospects, and the number of scholars dedicated to AI-related research on lung cancer is continually growing. It is foreseeable that non-invasive diagnosis and precise minimally invasive treatment through deep learning and machine learning will remain a central focus in the future. Simultaneously, there is a need to enhance collaboration not only among various countries and institutions but also between high-quality medical and industrial entities.

Navigating the AI frontiers in cardiovascular research: a bibliometric exploration and topic modeling.

Artificial intelligence (AI) has emerged as a promising field in cardiovascular disease (CVD) research, offering innovative approaches to enhance diagnosis, treatment, and patient outcomes. In this study, we conducted bibliometric analysis combined with topic modeling to provide a comprehensive overview of the AI research landscape in CVD. Our analysis included 23,846 studies from Web of Science and PubMed, capturing the latest advancements and trends in this rapidly evolving field. By employing LDA (Latent Dirichlet Allocation) we identified key research themes, trends, and collaborations within the AI-CVD domain. The findings revealed the exponential growth of AI-related research in CVD, underscoring its immense potential to revolutionize cardiovascular healthcare. The annual scientific publication of machine learning papers in CVD increases continuously and significantly since 2016, with an overall annual growth rate of 22.8%. Almost half (46.2%) of the growth happened in the last 5 years. USA, China, India, UK and Korea were the top five productive countries in number of publications. UK, Germany and Australia were the most collaborative countries with a multiple country publication (MCP) value of 42.8%, 40.3% and 40.0% respectively. We observed the emergence of twenty-two distinct research topics, including "stroke and robotic rehabilitation therapy," "robotic-assisted cardiac surgery," and "cardiac image analysis," which persisted as major topics throughout the years. Other topics, such as "retinal image analysis and CVD" and "biomarker and wearable signal analyses," have recently emerged as dominant areas of research in cardiovascular medicine. Convolutional neural network appears to be the most mentioned algorithm followed by LSTM (Long Short-Term Memory) and KNN (K-Nearest Neighbours). This indicates that the future direction of AI cardiovascular research is predominantly directing toward neural networks and image analysis. As AI continues to shape the landscape of CVD research, our study serves as a comprehensive guide for researchers, practitioners, and policymakers, providing valuable insights into the current state of AI in CVD research. This study offers a deep understanding of research trends and paves the way for future directions to maximiz the potential of AI to effectively combat cardiovascular diseases.

Artificial Intelligence in manufacturing: State of the art, perspectives, and future directions

Inspired by the natural intelligence of humans and bio-evolution, Artificial Intelligence (AI) has seen accelerated growth since the beginning of the 21st century. Successful AI applications have been broadly reported, with Industry 4.0 providing a thematic platform for AI-related research and development in manufacturing. This paper highlights applications of AI in manufacturing, ranging from production system design and planning to process modeling, optimization, quality assurance, maintenance, automated assembly and disassembly. In addition, the paper presents an overview of representative manufacturing problems and matching AI solutions, and a perspective of future research to leverage AI towards the realization of smart manufacturing.

Evolução do uso de agentes conversacionais na educação empresarial: Passado, presente e futuro

Abstract Purpose: The primary objective of this article is to answer the proposed research questions by analyzing the publications on artificial intelligence (AI), education, business, and conversational agents (CAs) or chatbots within the Scopus database to identify the most frequent terms used by scholars over the years, as well as to classify the research evolution based on the most important thematic trends. Originality/value: The study contributes to the literature by offering insights into the thematic landscape of AI-related research across multiple domains. By employing a comprehensive methodology, the article provides a nuanced understanding of the intersection between AI, education, business, and CAs. Design/methodology/approach: The methodology comprises two main components. Firstly, topic modeling is applied by the year of publication to summaries of articles meeting the search criteria. Secondly, grounded theory coding was used to categorize generated themes into more meaningful classifications. This dual approach ensures a rigorous data analysis and facilitates the identification of overarching thematic trends. Findings: The results reveal five thematic trends investigated in the analyzed publications: 1. student-centered learning in higher education; 2. interactive methods using the natural language processing (NLP) approach; 3. technological solutions and ChatGPT in a university context; 4. enhancing education through intelligent platforms; and 5. challenges for social and academic integration of AI tools. Additionally, the study proposes a research agenda with some questions as future avenues of inquiry.

Measuring and Fostering Diversity in Affective Computing Research

This work presents a longitudinal study of diversity among the Affective Computing research community members. We explore several dimensions of diversity, including gender, geography, institutional types of affiliations and selected combinations of dimensions. We cover the last 10 years of the IEEE Transactions on Affective Computing (TAFFC) journal and the International Conference on Affective Computing and Intelligent Interaction (ACII), the primary sources of publications in Affective Computing. We also present an analysis of diversity among the members of the Association for the Advancement of Affective Computing (AAAC). Our findings reveal a “leaky pipeline” in the field, with a low –albeit slowly increasing over the years– representation of women. They also show that academic institutions clearly dominate publications, ahead of industry and governmental centres. In terms of geography, most publications come from the USA, contributions from Latin America or Africa being almost non-existent. Lastly, we find that diversity in the characteristics of researchers (gender and geographic location) influences diversity in the topics. To conclude, we analyse initiatives that have been undertaken in other AI-related research communities to foster diversity, and recommend a set of initiatives that could be applied to the Affective Computing field to increase diversity in its different facets. The diversity data collected in this work are publicly available, ensuring strict personal data protection and governance rules.

Attitude, Awareness, and Understanding of Artificial Intelligence AI among Medical and Dental Students in Jordan: A Cross-Sectional Study

BACKGROUND: The rapid growth of AI has transformed various sectors, including healthcare. AI aids medical diagnoses and treatment planning, but challenges such as privacy and ethical concerns exist. Understanding AI is crucial for medical and dental students in Jordan to enhance patient care. This study examines students' attitudes, awareness, and understanding of AI to inform effective education and better healthcare practices. METHODS: A cross-sectional study was conducted in Jordan among medical and dental students from Nov 2022 to Mar 2023. Data were analyzed using descriptive statistics, Mann-Whitney U test, Kruskal-Wallis tests, and Spearman's correlation. Ethical approval was obtained, and SPSS was used for analysis. RESULTS: The study involved 800 respondents, predominantly female (54.6%) and with an average age of 19.81 years. Most participants were medical students (64.6%), had a national diploma (75.0%), and were in their first or second year of education (73.1%). The majority were single (97.9%), and 75.1% resided in the central region. In terms of technology background, 38.8% had family members in the technology sector, and half used electronic devices for 3-6 hours daily. Over 75% used electronic devices for studying, while 44.1% had taken technology courses, with only 23.5% having specifically studied Artificial Intelligence (AI). Additionally, 46.0% had prior research experience, but only 18.0% had participated in AI-related research. Participants rated their overall technology knowledge at 2.91 ± 1.06 and AI knowledge at 2.43 ± 1.21. The main sources of AI information were social media (35.0%). Regarding AI applications, 28.0% believed it would enhance medical diagnosis, while 24.0% expressed concerns about hacking and cybersecurity. Only 10.0% planned to consistently incorporate AI into their future medical practice. Statistical tests revealed significant differences in awareness of AI based on study field (p-value = 0.034), income (p-value = 0.028), previous technology involvement (p-value = 0.004), ICDL course participation (p-value = 0.02), AI course participation (p-value < 0.001), and programming language learning (p-value = 0.001). Similar variations were observed in understanding AI basic principles, with additional significance related to research course participation (p-value < 0.001). Attitudes toward AI differed only among those with previous technology course involvement (p-value = 0.037). Spearman's correlation indicated a moderate positive association between awareness of AI in medicine and dentistry and understanding of AI basic principles (r: 0.491, p < 0.001). CONCLUSION: The younger generation of medical and dental professionals’ views AI as a collaborator in their practice. Strong interest and active engagement in AI-related courses highlight the importance of integrating AI education into medical curricula for innovative healthcare practices.

Topic Analysis of AI-Related Research in Public Education

Topic Analysis of AI-Related Research in Public Education

Attention-Based Hybrid Deep Learning Network for Human Activity Recognition Using WiFi Channel State Information

The recognition of human movements is a crucial aspect of AI-related research fields. Although methods using vision and sensors provide more valuable data, they come at the expense of inconvenience to users and social limitations including privacy issues. WiFi-based sensing methods are increasingly being used to collect data on human activity due to their ubiquity, versatility, and high performance. Channel state information (CSI), a characteristic of WiFi signals, can be employed to identify various human activities. Traditional machine learning approaches depend on manually designed features, so recent studies propose leveraging deep learning capabilities to automatically extract features from raw CSI data. This research introduces a versatile framework for recognizing human activities by utilizing CSI data and evaluates its effectiveness on different deep learning networks. A hybrid deep learning network called CNN-GRU-AttNet is proposed to automatically extract informative spatial-temporal features from raw CSI data and efficiently classify activities. The effectiveness of a hybrid model is assessed by comparing it with five conventional deep learning models (CNN, LSTM, BiLSTM, GRU, and BiGRU) on two widely recognized benchmark datasets (CSI-HAR and StanWiFi). The experimental results demonstrate that the CNN-GRU-AttNet model surpasses previous state-of-the-art techniques, leading to an average accuracy improvement of up to 4.62%. Therefore, the proposed hybrid model is suitable for identifying human actions using CSI data.

Primary Teachers’ Perspectives on Using Artificial Intelligence Technology in English as a Second Language Teaching and Learning: A Systematic Review

Artificial Intelligence in Education (AIEd) is an emerging education field. This paper explicitly investigates the use of AI technology in language learning specifically in English as a Second Language (ESL). As a robust cloud-computing solution, AI-powered technology can be used to aid teaching and learning processes and improve language learning. While students' perspectives on AI-related research are prevalent, teachers' perspectives are not extensively discussed. In addition, studies on the effects of AI technology in ESL primary education, are understudied. Using two databases, namely Google Scholar and the Educational Resources Information Centre (ERIC), 12 articles related to the use of AI technology in ESL teaching and learning in primary schools were extracted out of 672, from 2019 to 2022 by using the PRISMA review methodology. Teachers’ perspectives were discussed based on the effectiveness, convenience, motivation and challenges. It is found that teachers perceived the integration of AI technology positively due to its dynamic characteristics and effectiveness despite facing various challenges resulting in disadvantages associated with its use. This paper explores AI technology's impact on ESL primary education from the perspective of teachers as curriculum stakeholders. Future research should delve further into the challenges that impede the use of AI technology in ESL primary education. This is essential for sustaining the momentum of AI-powered education.