Perspectives and Experiences of Education Stakeholders: A Quantitative Study on the Adoption of Artificial Intelligence in Executive Training Using Structural Equation Modeling

The recent increase in the use of artificial intelligence has led to fundamental changes in the development of training and teaching methods for executive education. However, the success of artificial intelligence in regional centers for teaching and training professions will depend on the acceptance of this technology by young executive trainees. This article discusses the potential benefits of adopting AI in executive training institutions in Morocco, specifically focusing on CRMEF Casablanca Settat. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003), this study proposes a model to identify the factors influencing the acceptance of artificial intelligence in regional centers for teaching professions and training in Morocco. To achieve this, a structural equation modeling approach was used to quantitatively describe the impact of each factor on AI adoption, utilizing data collected from 173 young executive trainees. The results indicate that perceived ease of use, perceived usefulness, trainer influence, and personal innovativeness influence the intention to use artificial intelligence. Our research provides managers of CRMEFs with a set of practical recommendations to enhance the implementation conditions of an artificial intelligence system. It aims to understand which factors should be considered in designing an artificial intelligence system within regional centers for teaching professions and training (CRMEFs).

A Discussion of Artificial Intelligence in Visual Art Education

Since ChatGPT emerged on November 30, 2022, Artificial Intelligence (AI) has been increasingly discussed as a radical force that will change our world. People have become used to AI in which such ubiquitous technologies as Siri, Google, and Netflix deploy AI algorithms to answer questions, impart information, and provide recommendations. However, many individuals including originators and backers of AI have recently expressed grave concerns. In this paper, the authors will assess what is occurring with AI in Visual Arts Education, outline positives and negatives, and provide recommendations addressed specifically for teachers working in the field regarding emerging AI usage from kindergarten to grade twelve levels as well as in higher education.

Revisiting Educational Issues in the Age of Generative Artificial Intelligence

The emergence of generative artificial intelligence(AI)has had a huge impact on all areas of life,including the field of education.AI can assist teachers in cultivating talents and promoting personalized learning and teaching,but it also prevents individuals from thinking independently and creatively.In the era of generative AI,the rapid development of technology and its significant impact on the field of education are inevitable.There are many educational issues related to it,such as teaching methods,student training goals,teaching philosophy and purposes,and other educational issues,that require re-conceptualization and review.

Self-supervised learning artificial intelligence noise reduction technology based on the nearest adjacent layer in ultra-low dose CT of urinary calculi

Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Methods Eighty-eight urinary calculi patients were prospectively enrolled.Low dose CT(LDCT)and ULDCT scanning were performed,and the effective dose(ED)of each scanning protocol were calculated.The patients were then randomly divided into training set(n=75)and test set(n=13),and a self-supervised deep learning AI noise reduction system based on the nearest adjacent layer constructed with ULDCT images in training set was used for reducing noise of ULDCT images in test set.In test set,the quality of ULDCT images before and after AI noise reduction were compared with LDCT images,i.e.Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)scores,image noise(SD ROI)and signal-to-noise ratio(SNR).Results The tube current,the volume CT dose index and the dose length product of abdominal ULDCT scanning protocol were all lower compared with those of LDCT scanning protocol(all P<0.05),with a decrease of ED for approximately 82.66%.For 13 patients with urinary calculi in test set,BRISQUE score showed that the quality level of ULDCT images before AI noise reduction reached 54.42%level but raised to 95.76%level of LDCT images after AI noise reduction.Both ULDCT images after AI noise reduction and LDCT images had lower SD ROI and higher SNR than ULDCT images before AI noise reduction(all adjusted P<0.05),whereas no significant difference was found between the former two(both adjusted P>0.05).Conclusion Self-supervised learning AI noise reduction technology based on the nearest adjacent layer could effectively reduce noise and improve image quality of urinary calculi ULDCT images,being conducive for clinical application of ULDCT.

Potential and limitations of ChatGPT and generative artificial intelligence in medical safety education

The primary objectives of medical safety education are to provide the public with essential knowledge about medications and to foster a scientific approach to drug usage.The era of using artificial intelligence to revolutionize medical safety education has already dawned,and ChatGPT and other generative artificial intelligence models have immense potential in this domain.Notably,they offer a wealth of knowledge,anonymity,continuous availability,and personalized services.However,the practical implementation of generative artificial intelligence models such as ChatGPT in medical safety education still faces several challenges,including concerns about the accuracy of information,legal responsibilities,and ethical obligations.Moving forward,it is crucial to intelligently upgrade ChatGPT by leveraging the strengths of existing medical practices.This task involves further integrating the model with real-life scenarios and proactively addressing ethical and security issues with the ultimate goal of providing the public with comprehensive,convenient,efficient,and personalized medical services.

Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges

Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms.To investigate advances in the use of artificial intelligence in the diagnosis,rehabilitation,and scientific examination of peripheral nerve injury,we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994–2023.We identified the following research hotspots in peripheral nerve injury and repair:(1)diagnosis,classification,and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques,such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy;(2)motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms,such as wearable devices and assisted wheelchair systems;(3)improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning,such as implantable peripheral nerve interfaces;(4)the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility,enabling them to control devices such as networked hand prostheses;(5)artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation,thereby reducing surgical risk and complications,and facilitating postoperative recovery.Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair,there are some limitations to this technology,such as the consequences of missing or imbalanced data,low data accuracy and reproducibility,and ethical issues(e.g.,privacy,data security,research transparency).Future research should address the issue of data collection,as large-scale,high-quality clinical datasets are required to establish effective artificial intelligence models.Multimodal data processing is also necessary,along with interdisciplinary collaboration,medical-industrial integration,and multicenter,large-sample clinical studies.

Toward a Learnable Climate Model in the Artificial Intelligence Era

Artificial intelligence(AI)models have significantly impacted various areas of the atmospheric sciences,reshaping our approach to climate-related challenges.Amid this AI-driven transformation,the foundational role of physics in climate science has occasionally been overlooked.Our perspective suggests that the future of climate modeling involves a synergistic partnership between AI and physics,rather than an“either/or”scenario.Scrutinizing controversies around current physical inconsistencies in large AI models,we stress the critical need for detailed dynamic diagnostics and physical constraints.Furthermore,we provide illustrative examples to guide future assessments and constraints for AI models.Regarding AI integration with numerical models,we argue that offline AI parameterization schemes may fall short of achieving global optimality,emphasizing the importance of constructing online schemes.Additionally,we highlight the significance of fostering a community culture and propose the OCR(Open,Comparable,Reproducible)principles.Through a better community culture and a deep integration of physics and AI,we contend that developing a learnable climate model,balancing AI and physics,is an achievable goal.

Artificial intelligence-driven radiomics study in cancer:the role of feature engineering and modeling

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.

ChatGPT in action:Harnessing artificial intelligence potential and addressing ethical challenges in medicine,education,and scientific research

Artificial intelligence(AI)tools,like OpenAI's Chat Generative Pre-trained Transformer(ChatGPT),hold considerable potential in healthcare,academia,and diverse industries.Evidence demonstrates its capability at a medical student level in standardized tests,suggesting utility in medical education,radiology reporting,genetics research,data optimization,and drafting repetitive texts such as discharge summaries.Nevertheless,these tools should augment,not supplant,human expertise.Despite promising applications,ChatGPT confronts limitations,including critical thinking tasks and generating false references,necessitating stringent cross-verification.Ensuing concerns,such as potential misuse,bias,blind trust,and privacy,underscore the need for transparency,accountability,and clear policies.Evaluations of AI-generated content and preservation of academic integrity are critical.With responsible use,AI can significantly improve healthcare,academia,and industry without compromising integrity and research quality.For effective and ethical AI deployment,collaboration amongst AI developers,researchers,educators,and policymakers is vital.The development of domain-specific tools,guidelines,regulations,and the facilitation of public dialogue must underpin these endeavors to responsibly harness AI's potential.

Exploration of Graduate Student Cultivation Mode of Landscape Architecture under the Background of“Artificial Intelligence+X”

Under the background of“artificial intelligence+X”,the development of landscape architecture industry ushers in new opportunities,and professional talents need to be updated to meet the social demand.This paper analyzes the cultivation demand of landscape architecture graduate students in the context of the new era,and identifies the problems by comparing the original professional graduate training mode.The new cultivation mode of graduate students in landscape architecture is proposed,including updating the target orientation of the discipline,optimizing the teaching system,building a“dualteacher”tutor team,and improving the“industry-university-research-utilization”integrated cultivation,so as to cultivate high-quality compound talents with disciplinary characteristics.

Explainable Artificial Intelligence(XAI)Model for Cancer Image Classification

The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare environments.It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms.These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence.Nevertheless,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images.This research presents an advanced investigation of XAI models to classify cancer images.It describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare applications.In addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging techniques.The proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive explanation.The proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%DOR.It will discuss the potential applications of the proposed XAI models in the smart healthcare environment.It will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.

Exploring the Significance and Path of Interdisciplinary Integration of Art Education in Primary and Secondary Schools in the Era of Artificial Intelligence

With the advent of the artificial intelligence(AI)era,there is a need to create a more flexible and humanistic educational ecosystem to adapt to the changes.Education needs to move from a unidirectional focus on skills to the cultivation of creative“whole people.”Due to the non-standardized evaluation system of the art discipline,its education has a unique advantage for the cultivation of students’creativity.At the same time,the interdisciplinary integration of fine arts points to the educational goals in the era of AI and the educational requirements for cultivating students’core qualities in China.Therefore,this paper analyzes the theoretical basis and developmental evolution of interdisciplinary integration,studies the significance of interdisciplinary integration in art education from the three levels of students,teaching,and disciplines,and explores four effective paths to realize interdisciplinary integration in art education in the era of AI.In this way,students can realize the contextualized analysis of knowledge,in-depth understanding of the content of the discipline,and accurate expression of the spiritual values embedded in art interdisciplinary learning.The ultimate goal is to cultivate students’ability to solve complex problems,promote the development of students’free personalities,and respond to the national education requirements.

Advancements in Barrett's esophagus detection:The role of artificial intelligence and its implications

Artificial intelligence(AI)is making significant strides in revolutionizing the detection of Barrett's esophagus(BE),a precursor to esophageal adenocarcinoma.In the research article by Tsai et al,researchers utilized endoscopic images to train an AI model,challenging the traditional distinction between endoscopic and histological BE.This approach yielded remarkable results,with the AI system achieving an accuracy of 94.37%,sensitivity of 94.29%,and specificity of 94.44%.The study's extensive dataset enhances the AI model's practicality,offering valuable support to endoscopists by minimizing unnecessary biopsies.However,questions about the applicability to different endoscopic systems remain.The study underscores the potential of AI in BE detection while highlighting the need for further research to assess its adaptability to diverse clinical settings.

An Examination of Computer Science and Internet Technologies in Addressing Educational Inequities and Societal Psychological Concerns:A Literature Review from the Perspectives of 5G,Artificial Intelligence,and Augmented/Virtual Reality

This study comprehensively reviews the literature to deeply explore the role of computer science and internet technologies in addressing educational inequality and socio-psychological issues,with a particular focus on applications of 5G,artificial intelligence(AI),and augmented/virtual reality(AR/VR).By analyzing how these technologies are reshaping learning and their potential to ameliorate educational disparities,the study reveals challenges present in ensuring educational equity.The research methodology includes exhaustive reviews of applications of AI and machine learning,the Internet of Things and wearable technologies integration,big data analytics and data mining,and the effects of online platforms and social media on socio-psychological issues.Besides,the study discusses applications of these technologies in educational inequality and socio-psychological problem-solving through the lens of 5G,AI,and AR/VR,while also delineating challenges faced by these emerging technologies and future outlooks.The study finds that while computer science and internet technologies hold promise to bridge academic divides and address socio-psychological problems,the complexity of technology access and infrastructure,lack of digital literacy and skills,and critical ethical and privacy issues can impact widespread adoption and efficacy.Overall,the study provides a novel perspective to understand the potential of computer science and internet technologies in ameliorating educational inequality and socio-psychological issues,while pointing to new directions for future research.It also emphasizes the importance of cooperation among educational institutions,technology vendors,policymakers and researchers,and establishing comprehensive ethical guidelines and regulations to ensure the responsible use of these technologies.

Artificial intelligence in individualized retinal disease management

Owing to the rapid development of modern computer technologies,artificial intelligence(AI)has emerged as an essential instrument for intelligent analysis across a range of fields.AI has been proven to be highly effective in ophthalmology,where it is frequently used for identifying,diagnosing,and typing retinal diseases.An increasing number of researchers have begun to comprehensively map patients’retinal diseases using AI,which has made individualized clinical prediction and treatment possible.These include prognostic improvement,risk prediction,progression assessment,and interventional therapies for retinal diseases.Researchers have used a range of input data methods to increase the accuracy and dependability of the results,including the use of tabular,textual,or image-based input data.They also combined the analyses of multiple types of input data.To give ophthalmologists access to precise,individualized,and high-quality treatment strategies that will further optimize treatment outcomes,this review summarizes the latest findings in AI research related to the prediction and guidance of clinical diagnosis and treatment of retinal diseases.

Artificial Intelligence Prediction of One-Part Geopolymer Compressive Strength for Sustainable Concrete

Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological concrete.In terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part geopolymer.However,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported.Therefore,in this study,machine learning methods such as artificial neural networks(ANN)and gene expression programming(GEP)models were developed usingMATLAB and GeneXprotools,respectively,for the prediction of compressive strength under variable input materials and content for fly ash and slag-based one-part geopolymer.The database for this study contains 171 points extracted from literature with input parameters:fly ash concentration,slag content,calcium hydroxide content,sodium oxide dose,water binder ratio,and curing temperature.The performance of the two models was evaluated under various statistical indices,namely correlation coefficient(R),mean absolute error(MAE),and rootmean square error(RMSE).In terms of the strength prediction efficacy of a one-part geopolymer,ANN outperformed GEP.Sensitivity and parametric analysis were also performed to identify the significant contributor to strength.According to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 days.The water binder ratio was shown to be directly connected to activator percentage,slag percentage,and calcium hydroxide percentage and inversely related to compressive strength at 28 days and curing temperature.

A scoping review of methodologies for applying artificial intelligence to physical activity interventions

Purpose This scoping review aimed to offer researchers and practitioners an understanding of artificial intelligence(AI)applications in physical activity(PA)interventions;introduce them to prevalent machine learning(ML),deep learning(DL),and reinforcement learning(RL)algorithms;and encourage the adoption of AI methodologies.Methods A scoping review was performed in PubMed,Web of Science,Cochrane Library,and EBSCO focusing on AI applications for promoting PA or predicting related behavioral or health outcomes.AI methodologies were summarized and categorized to identify synergies,patterns,and trends informing future research.Additionally,a concise primer on predominant AI methodologies within the realm of PA was provided to bolster understanding and broader application.Results The review included 24 studies that met the predetermined eligibility criteria.AI models were found effective in detecting significant patterns of PA behavior and associations between specific factors and intervention outcomes.Most studies comparing AI models to traditional statistical approaches reported higher prediction accuracy for AI models on test data.Comparisons of different AI models yielded mixed results,likely due to model performance being highly dependent on the dataset and task.An increasing trend of adopting state-of-the-art DL and RL models over standard ML was observed,addressing complex human–machine communication,behavior modification,and decision-making tasks.Six key areas for future AI adoption in PA interventions emerged:personalized PA interventions,real-time monitoring and adaptation,integration of multimodal data sources,evaluation of intervention effectiveness,expanding access to PA interventions,and predicting and preventing injuries.Conclusion The scoping review highlights the potential of AI methodologies for advancing PA interventions.As the field progresses,staying informed and exploring emerging AI-driven strategies is essential for achieving significant improvements in PA interventions and fostering overall well-being.

Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems

Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based algorithms.In this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and decision-making.Federated Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data effectively.In this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare systems.The experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)platform.The experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.

A quantitative study of disruptive technology policy texts:An example of China’s artificial intelligence policy

Purpose:The transformative impact of disruptive technologies on the restructuring of the times has attracted widespread global attention.This study aims to analyze the characteristics and shortcomings of China’s artificial intelligence(AI)disruptive technology policy,and to put forward suggestions for optimizing China’s AI disruptive technology policy.Design/methodology/approach:Develop a three-dimensional analytical framework for“policy tools-policy actors-policy themes”and apply policy tools,social network analysis,and LDA topic model to conduct a comprehensive analysis of the utilization of policy tools,cooperative relationships among policy actors,and the trends in policy theme settings within China’s innovative AI technology policy.Findings:We find that the collaborative relationship among the policy actors of AI disruptive technology in China is insufficiently close.Marginal subjects exhibit low participation in the cooperation network and overly rely on central subjects,forming a“center-periphery”network structure.Policy tool usage is predominantly focused on supply and environmental types,with a severe inadequacy in demand-side policy tool utilization.Policy themes are diverse,encompassing topics such as“Intelligent Services”“Talent Cultivation”“Information Security”and“Technological Innovation”,which will remain focal points.Under the themes of“Intelligent Services”and“Intelligent Governance”,policy tool usage is relatively balanced,with close collaboration among policy entities.However,the theme of“AI Theoretical System”lacks a comprehensive understanding of tool usage and necessitates enhanced cooperation with other policy entities.Research limitations:The data sources and experimental scope are subject to certain limitations,potentially introducing biases and imperfections into the research results,necessitating further validation and refinement.Practical implications:The study introduces a three-dimensional analysis framework for disruptive technology policy texts,which is significant for formulating and enhancing disruptive technology policies.Originality/value:This study utilizes text mining and content analysis techniques to quantitatively analyze disruptive technology policy texts.It systematically evaluates China’s AI policies quantitatively,focusing on policy tools,policy actors,policy themes.The study uncovers the characteristics and deficiencies of current AI policies,offering recommendations for formulating and enhancing disruptive technology policies.

An artificial intelligence diabetes management architecture based on 5G

Along with the development of 5G network and Internet of Things technologies,there has been an explosion in personalized healthcare systems.When the 5G and Artificial Intelligence(Al)is introduced into diabetes management architecture,it can increase the efficiency of existing systems and complications of diabetes can be handled more effectively by taking advantage of 5G.In this article,we propose a 5G-based Artificial Intelligence Diabetes Management architecture(AIDM),which can help physicians and patients to manage both acute complications and chronic complications.The AIDM contains five layers:the sensing layer,the transmission layer,the storage layer,the computing layer,and the application layer.We build a test bed for the transmission and application layers.Specifically,we apply a delay-aware RA optimization based on a double-queue model to improve access efficiency in smart hospital wards in the transmission layer.In application layer,we build a prediction model using a deep forest algorithm.Results on real-world data show that our AIDM can enhance the efficiency of diabetes management and improve the screening rate of diabetes as well.