Correction: Parental and Artificial Intelligence Perspectives on Adolescent Sexting: A Comparative Analysis.

Retraction statement We, the Editors and Publisher of the journal Knowledge Management Research & Practice (the “Journal”), have retracted the following articles that were published in the Special Issue titled “ The Impact of Artificial Intelligence on Knowledge Management and Business Strategic Planning ” guest-edited by Gunasekaran Manogaran: Fu, C., Jiang, H., & Chen, X. (2022). Modeling of an Enterprise Knowledge Management System Based on Artificial Intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1854632 Wu, H., Gu, X., Zhao, Y., & Liu, W. (2020). Research on the Relationship between Structural Hole Location, Knowledge Management and Cooperative Innovation Performance in Artificial Intelligence. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1813642 Wang, M. (2020). Financing support strategy of green manufacturer for financially constrained retailer. Knowledge Management Research & Practice, 1–15. https://doi.org/10.1080/14778238.2020.1771224 Elashkar, E., Aldeek, F., & Shoukry, A. (2020). Business predictive analysis from business insurance data using business strategic planning techniques. Knowledge Management Research & Practice, 1–10. https://doi.org/10.1080/14778238.2020.1776172 Huang, Z., He, J., & Ren, X. (2021). Application of artificial intelligence in enterprise knowledge management performance evaluation. Knowledge Management Research & Practice, 1–9. https://doi.org/10.1080/14778238.2020.1850187 Lei, Z., & Wang, L. (2020). Construction of organisational system of enterprise knowledge management networking module based on artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1831892 Li, Z., & Mo, T. (2020). Early warning of engineering project knowledge management risk based on artificial intelligence. Knowledge Management Research & Practice, 1–11. https://doi.org/10.1080/14778238.2020.1834885 Yang, J., Wen, C., & Gan, L. (2021). Model of an Enterprise Knowledge Chain Management System Based on Multiagent Technology in Distributed Artificial Intelligence. Knowledge Management Research & Practice, 1–12. https://doi.org/10.1080/14778238.2020.1850188 Bai, X., & Li, J. (2020). The best configuration of collaborative knowledge innovation management from the perspective of artificial intelligence. Knowledge Management Research & Practice, 1–13. https://doi.org/10.1080/14778238.2020.1834886 Following publication, the Publisher identified concerns regarding the editorial handling of the Special Issue and the peer review process. An investigation by the Taylor & Francis Publishing Ethics & Integrity team in full cooperation with the Editors-in-Chief concluded that the articles included in this Special Issue were not peer-reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editors-in-Chief and Publisher have decided to retract all of the articles within the above-named Special Issue. The Editors-in-Chief and the Publisher have not confirmed if the authors were aware of this compromised peer review process. The Journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’. © 2019 Informa UK Limited, trading as Taylor & Francis Group/Journal owner

In order to solve the problem that traditional scientific research management is restricted by management methods and tools, and it is difficult to realize the development of cultural industry, this paper proposes a research method that scientific research management helps the development of the regional cultural industry from the perspective of artificial intelligence. Based on a wavelet neural network based on artificial intelligence, this paper establishes the goodwill value evaluation of cultural industry development under scientific research management. Firstly, the paper uses the hierarchical structure theory to construct the theoretical analysis framework and evaluation index system of cultural value and divides the influencing factors of cultural industry value into three levels: input layer network structure, hidden layer network structure, and output layer network structure. Then the wavelet neural network method is used to analyze, and the three-layer network structure is superimposed to reflect the hierarchical relationship between the index systems. Through the combination of normative analysis and empirical analysis, it verifies that the method has unique advantages and feasibility and realizes the promotion of scientific research management to the development of the regional cultural industry.

With the evolution of the information age, artificial intelligence has become an important variable affecting future social development, and its educational value has been widely recognized. In 2018, the Ministry of Education's "Education Informatization 2.0 Action Plan" put forward higher requirements for teaching informatization. The integration of artificial intelligence and education is the only way to improve teaching quality and cultivate talents that adapt to the development of the times. The core of educational informatization is the intelligence of teaching, which makes teaching methods intelligent, educational communication intelligent, and teaching methods technological. In view of this, firstly, the basic characteristics of the teaching information model were analyzed; Secondly, the application of the Chinese language information model was analyzed; Once again, the implementation of the Chinese language information model was analyzed to explore the opportunities and challenges faced by improving the application of artificial intelligence; Finally, from the perspectives of schools and teachers, strategies and suggestions are proposed to improve Chinese language education and teaching from the perspective of artificial intelligence.

With the rapid development of Artificial Intelligence (AI), especially for the progress of foreign-related pharmaceutical enterprises, the demand for English interpreting practitioners is increasing. Based on analysis of the perspective of economic development in the Guangdong-HongKong-Macao Greater Bay Area, the study has discussed the training situation of English interpreting learners of higher education from the perspective of AI, while analyzed characteristics on the learning and training in current English interpreting. Moreover, it also puts forward the suitable teaching strategies for the course of English interpreting in higher education, so as to further meet the rising goals of foreign-related enterprises in the Greater Bay Area.

The rapid development of technology has made an earth-shattering innovation take the world by storm: Artificial Intelligence (AI). This study aims to determine the perceptions and practices of teachers and students in using AI in the classroom. Four university teachers and 30 students were sent questionnaires, and four teachers and 23 students were given online structured interviews. The results stated that in the student group, as many as 24 respondents (80%) had a good perception of AI, and six respondents (20%) had an excellent perception of AI. Meanwhile, in the teacher group, three respondents (75.0%) had a favorable perception, and one teacher respondent (25.0%) had a very favorable perception of AI. This result is also reinforced by the practice of students' and teachers' perspectives of AI, which state that AI is beneficial in eliciting students’ responses, attracting students’ attention, and increasing their motivation and creativity. Nevertheless, AI is just an intelligent tool; no matter how excellent and optimal the tool is, how much potential humans can utilize depends on the use and the user himself.

This special issue of the Scientific Journal of Artificial Intelligence and Blockchain Technologies (SJAIBT) presents the proceedings of the Second International Seminar on “Bridging Tradition and Technology: AI Perspectives through the Lens of Indian Knowledge Systems,” an interdisciplinary forum dedicated to the convergence of artificial intelligence (AI) and Indian Knowledge Systems (IKS). Across 67 peer-reviewed papers, the issue explores how AI intersects with diverse fields—including education, ethics, environmental sustainability, digital literacy, gender studies, public policy, and cultural heritage—to address contemporary challenges of sustainability, equity, and cultural preservation. An overarching theme of the seminar is the interdisciplinary bridging of traditional wisdom and modern technology. The collected studies emphasize that aligning AI innovation with indigenous knowledge and ethical principles is key to developing responsible AI systems and fostering inclusive technological progress. Key focal areas include the transformation of education through AI-enhanced learning aligned with IKS perspectives, as well as strategies for women’s empowerment and digital literacy aimed at fostering an inclusive AI-driven society. In addition, several papers propose innovative teacher training models grounded in ancient pedagogical insights to prepare educators for AI-integrated classrooms. Other contributions examine holistic health frameworks that integrate AI with traditional wellness practices and explore AI-driven approaches to environmental sustainability informed by indigenous ecological wisdom. Together, these works underscore a paradigm in which cutting-edge AI applications are harmonized with cultural heritage, social values, and environmental sustainability objectives. This integrated approach illustrates a roadmap for future research and public policy initiatives that leverage both historical wisdom and modern science for societal benefit.

This paper focuses on the evolving space of digital diplomacy in light of the changes taking place from the perspective of Artificial Intelligence (AI). It evaluates the key trends that are manifesting themselves in the shifting space of Kenya’s digital diplomacy and what the future holds. The paper is guided by three research objectives: first, it examines the key developments that have taken place in Kenya’s emerging digital diplomacy; secondly, it assesses the impacts that AI has presented to this digital diplomacy space; and thirdly, it investigates possible challenges of integrating AI in Kenya’s digital diplomacy. The paper is guided by the constructivism theory as propounded by Alexander Wendt for its theoretical framework. Constructivism challenges the classical beliefs applied in the conduct of interstate relations and underscores their socially-constructed nature. It advocates for transformative shifts in international relations where state actors create and assign meaning to structures that enable them to better achieve their national interests. The paper is guided by the pragmatic philosophy for its philosophical orientation. It relies on secondary data on digital diplomacy and AI from published data sources. The paper observes that there are new trends that present themselves towards the conduct of Kenya’s digital diplomacy in an era of AI. In addition, it submits that human skills have been the bedrock of diplomacy and AI only serves to augment such skills in order to enhance efficiency and precision in diplomatic practices. Lastly, the paper holds that, as a developing country, Kenya is predisposed to a myriad of challenges that she must mitigate so as to guarantee the integrity and proficiency of her digital diplomacy space. It presents recommendations on how Kenya can leverage AI to make its digital diplomacy responsive to the emerging technological reality.

With the establishment of modern enterprise systems and the emergence of some large-scale enterprise groups formed through asset reorganization, industry alliances, and cross-industry mergers, new requirements have been put forward for the financial management mode of enterprise groups. This article mainly studies the development of the smart accounting management model architecture under the artificial intelligence perspective. On the accounting sharing service platform, the accounting sharing center does not belong to the logistics branch of any region. Through the accounting sharing center, it provides unified and standardized accounting, asset management, currency revenue and expenditure, etc., for the branches of the logistics company. At the same time, in the network environment, the financial and accounting sharing platform is not unilaterally closed. As each branch under unified management, it has the power to feedback, suggest, and supervise the financial and accounting sharing platform. When testing, one only needs to focus on the external properties of the program, without considering the internal logical structure and internal characteristics of the program at all. The system under test is regarded as a sealed black box, and the boundary value analysis method, the equivalence class division method, and causality are used. Technical methods such as the graph method and error speculation method are tested at the system interface to detect whether the function of each test can receive and output the results correctly. The implementation of the accounting management model of accounting shared services must ensure that the evaluation results of related work are objective and accurate. It is necessary to ensure that the evaluation index system has a high degree of quantification. In the specific scoring, the full score system can be used to score points by experts to ensure the objectivity of the evaluation results and accuracy while improving the operability and enforceability of the evaluation work. The average value of the operating net profit margin is 0.088731, and the median value is 0.082263. The results show that artificial intelligence technology has greatly promoted the development of the smart accounting management model architecture.

Future prospects of computer-aided design (CAD) – A review from the perspective of artificial intelligence (AI), extended reality, and 3D printing

With the change of the fourth generation of information technology represented by artificial intelligence, higher education is gradually stepping into the stage of intelligent development, and leisure sports majors as one of the majors that are booming, which puts forward higher requirements on the teaching power of teachers of leisure sports majors. Therefore, studying the current situation of talent cultivation of leisure sports majors under artificial intelligence plays a good role in promoting the overall improvement of the teaching quality of leisure sports majors. In this paper, the current situation of talent training and classroom teaching of leisure sports majors is studied based on a convolutional neural network in the artificial intelligence perspective, and the framework of artificial intelligence technology for leisure sports education is constructed. It can be seen that the attitude of leisure sports students towards theory class is better; 40.93% think theory class is very important, 38.13% think theory class is more important, and 19.27% think the importance of theory class is average. Students’ attitudes toward practical classes were better than those toward theoretical classes, with 63.71% considering practical classes very important and 26.85% considering them more important. From the analysis results, we can see that there are still problems of weak basic theory research, confusion of cultivation goals, lack of science in curriculum setting, and lack of teachers in cultivating talents in leisure sports majors. In summary, this paper argues that colleges and universities and college teachers should take the initiative to apply the technical framework of artificial intelligence in leisure sports education, strengthen theoretical research, make up for the shortcomings of teaching materials, as well as integrate training objectives, improve curriculum settings, enhance faculty strength, investigate the talent specification needs of the market in-depth, proactively meet the arrival of the era of artificial intelligence, and contribute to the cultivation of talents in leisure sports majors.

To better promote the healthy and long-term development of corporate financial management, the basement is established on the perspective of artificial intelligence (AI). Initially, based on the theories of modern mobile payment (MP) and corporate financial leverage, the corresponding data set is obtained through the questionnaire method as the research data. The reliability coefficients obtained after the test are all above 0.65, indicating that the reliability and stability of the entire data are relatively good. Besides, it is also found from the data of the questionnaire that some residents believe that MP will bring harm such as information leakage. Next, a new multilevel evaluation analysis method is introduced. After evaluating the financial management risk, operation risk, and network security risk existing in enterprise MP, it is found that the financial management risk accounts for the largest proportion of the three, with a risk weight of about 0.54, and the capital risk occupies the main position in the financial management risk. Finally, through the analysis of the risks existing in the whole operation process of the enterprise, it is found that about 50% of the financial management risk of the enterprise in the market belongs to the advanced risk, about 30% of the operational operation risk belongs to the low risk, and about 20% of the network security risk belongs to the advanced risk, which indicates that the financial management risk and network security risk are the top priority of the enterprise MP risk. Although the operational operation risk belongs to the low risk, it cannot be ignored. Subsequently, feasible suggestions and opinions are put forward on these phenomena from the perspectives of the government, enterprises, and residents. Therefore, there is great reference significance for the current financial risk assessment of enterprises based on MP.

The application of artificial intelligence technology has become increasingly widespread and has penetrated into all areas of life. Based on artificial intelligence technology, based on the high-quality development connotation of macro-economy, enterprises and banking, this article maps the high-quality development of the three levels of macro, medium and micro to commercial banks, and explains the connotation and levels of high-quality development of commercial banks. Based on the obtained logical framework for the high-quality development of commercial banks, construct a high-quality development evaluation system for commercial banks. Research shows that the rising trend in recent years is because commercial banks have improved their own efficiency and innovation capabilities, and strengthened their shared functions with society, shareholders, and employees. Different types of commercial banks have different high-quality development status. Joint-stock banks are the best, followed by state-owned banks. City commercial banks and rural commercial banks have a tendency to differentiate, and regional banks in economically developed areas have developed better. Chinese commercial banks need to grasp the two key points of innovation and sharing, and use financial technology and sharing mechanisms as the starting point to promote the improvement of commercial banks' innovation capabilities and efficiency levels, enhance their ability to serve the real economy, and achieve high-quality development of themselves and the macro economy.KeywordsArtificial intelligenceCommercial banksHigh-quality developmentEvaluation system

Evaluation method of singing pronunciation quality based on artificial intelligence technology

Controversy about pets such as dogs is also growing, and the public health controversy is the most obvious. In order to solve the public health problem of pets outdoors, meet the needs of pet family pets for outdoor hygiene, and make up for the lack of smart pet public toilets in the market and with the development of artificial intelligence technology, the authors propose that “pet toilets” have been put into use in many cities and the emergence of this new thing is mainly aimed at the substantial increase in the number of pets such as dogs in cities, hoping to better solve the problem of pet dogs urinating in public places in the community and provide a more convenient dog service measure for urban residents. Through a questionnaire survey of 100 random volunteers, it was found that 69 people believed that pet toilets were very necessary for society; therefore, from the perspective of artificial intelligence, the development of smart pet toilets and self-service payment systems has been focused on.

In recent decades, several forecasting methods have been proposed so as to aid in selecting from all optimal alternatives in the demand of emergency resources. Academic research in the field of emergency management has increasingly focused on artificial intelligence. However, more attention has been paid to attempts at simulating the human brain, with little focus on addressing intelligent information processing techniques based on machine learning, big data and smart devices. In this paper, a comprehensive literature review is presented in order to classify and interpret current research on demand forecasting methodologies and applications. A total of 1235 academic papers from 1980 to 2018 in the SpringerLink and Elsevier ScienceDirect databases are categorized as follows: time series analysis, case-based reasoning (CBR), mathematical models, information technology, literature reviews, and discussion and analysis. Application areas from business source premier include papers on the topics of emergency management, decision-making, decision relief, logistics, fuzzy sets and other topics. Academic publications are classified by (1) year of publication, (2) journal of publication, (3) database source, (4) methodology and (5) research discipline. The results of this literature review show that, despite forecasting methods such as ARIMA, CBR and mathematical models appearing to play a pivotal role in promoting prediction performance, there is a need to explore more real-time forecasting approaches based on intelligent information processing techniques so as to achieve appropriate dynamic demand prediction that is adaptable to emergency and rescue situations. The intention for this paper is to be a useful reference point for those with research needs in forecasting methodologies and the applications of emergency resources.