AI-driven knowledge management system reconstruction: the CPOS four-dimensional collaborative framework

With the gradual maturity of artificial intelligence (AI) technology, it is urgent to change and innovate the traditional media communication paradigm. Current research and application areas include language processing, image recognition, AI systems, intelligent video technology, and text recognition. With the rapid development of artificial intelligence technology, it is used in all walks of life in society. All sectors of society have also set off the exploration and application of artificial intelligence. In the field of news communication, artificial intelligence technology brings opportunities and challenges to the news industry in publishing news content forms and organizations. Exploring the new development trends and countermeasures of the news industry in the era of artificial intelligence has become a key topic for the news communication industry and academia. With the support of the huge communication network, the information transmission of short video news is more abundant. While people obtain information, it can also enhance the interactivity of the news. In a fast-paced society, short video news is undoubtedly closer to life. However, due to some reasons, human factors lead to incomplete access to information. In this paper, an intelligent neural network is proposed to be applied to an intelligent robot or intelligent system so that it can better obtain information to edit short video news and disseminate it, and compare it with the existing such machines or systems on the market today. This paper analyzes the development of short video news dissemination based on artificial intelligence and proposes improvement strategies based on specific examples.

Objectives: The objective of this scientific paper is to shed light on the challenges facing the legislative industry in the field of artificial intelligence (AI) technologies. Specifically, it aims to anticipate and predict the difficulties related to creating an independent legal personality for AI tools, enabling them to assume rights and obligations, as well as establishing criminal responsibility and punishment in cases where AI technologies commit crimes punishable by law. Methods: To achieve the stated objectives, this paper employs a theoretical and analytical approach. It involves a comprehensive review and analysis of existing literature, legal frameworks, and case studies related to the legal implications of AI technologies. Additionally, hypothetical scenarios and potential challenges are discussed to illustrate the complexities involved in creating legal frameworks that address the unique nature of AI tools and their impact on society. Results: The analysis reveals several key challenges facing the legislative industry in the field of AI technologies. These challenges include the conceptualization of an independent legal personality for AI tools, determining the rights and obligations associated with such personality, and establishing mechanisms for holding AI technologies accountable for their actions. Furthermore, the paper explores the difficulties in imposing criminal responsibility and punishment on AI technologies in cases where they commit crimes. Conclusion: In conclusion, this scientific paper highlights the complexities and challenges associated with creating legal frameworks for AI technologies. By anticipating and predicting these challenges, it provides valuable insights for legislators, policymakers, and legal professionals involved in shaping the regulatory landscape for AI. Moving forward, it is essential to address these challenges proactively and develop legal frameworks that strike a balance between promoting innovation and safeguarding societal interests in the era of artificial intelligence.

In the era of artificial intelligence (AI), physical education (PE) teachers face unprecedented challenges in integrating advanced technologies into their teaching practices. This study aims to identify the specific challenges PE teachers encounter in the AI era and propose practical strategies to enhance the quality and relevance of school-based physical education. This study employed a multi-method approach, including literature review, mathematical statistics, questionnaire surveys, and expert interviews. The participants were 120 PE teachers from urban and rural schools across different regions. The study began with a literature review to establish a theoretical foundation. Questionnaires were used to gather teachers' perceptions of challenges related to AI integration, student engagement, and professional development. In-depth interviews with 25 PE experts provided deeper insights into their experiences and coping strategies. Data were analyzed using SPSS software. The results showed that 80% of PE teachers reported difficulties in integrating AI tools into their lessons, while 70% struggled with engaging students in technology-enhanced activities. Analysis of the questionnaire and interview data revealed three main challenges: (1) limited access to AI resources, (2) lack of training in AI applications, and (3) concerns about the impact of AI on traditional teaching methods. Teachers emphasized the need for continuous professional development and support to effectively utilize AI in teaching. The study concludes that PE teachers face significant challenges in adapting to the AI-driven educational environment, particularly in integrating AI tools and maintaining student engagement. These findings align with previous research on educational technology integration but highlight the unique concerns of PE teachers regarding the impact of AI on traditional teaching. This study proposes the following strategies: (1) increasing investment in AI tools and establishing resource-sharing platforms to improve access to AI resources; (2) providing continuous professional training in AI applications and integrating AI education into teacher development programs; (3) offering technical support for AI integration and encouraging teachers to use AI tools to optimize traditional teaching methods.

Artificial intelligence (AI) technology has brought unprecedented opportunities and challenges for innovation to college English teaching. This study focuses on the application of AI in digital college English teaching at the university level, analyzing the advantages of AI technology in personalized instruction, intelligent assessment and feedback, and immersive learning experience. It also examines pressing issues such as the integration of technology and pedagogy, as well as data privacy and security. Based on this analysis, the study proposes strategies to promote the innovative development AI-empowered English teaching, including constructing a new blended teaching model of “AI + Teacher”, enhancing digital literacy of teachers, and improving AI teaching management systems, so as to provide enlightenment for exploring the reform of college English teaching in the era of artificial intelligence.

In the present dynamic and knowledge-driven business environment, Knowledge Management (KM) has emerged as a critical factor for enhancing organizational performance, fostering innovation, and ensuring long-term sustainability. Organizations increasingly rely on effective KM practices to systematically create, store, share, and apply knowledge, thereby improving decision-making processes, strengthening collaboration, and achieving strategic objectives. KM serves as a catalyst for managing intellectual assets and transforming them into valuable organizational outcomes, contributing significantly to competitiveness and growth . Despite its growing importance, the implementation of effective KM practices remains a significant challenge across organizations. Various barriers such as cultural resistance, technological limitations, lack of resources, and inadequate organizational support hinder the successful adoption of KM systems. Cultural dimensions, including organizational hierarchy, power distance, and communication patterns, play a crucial role in influencing knowledge-sharing behavior and the overall effectiveness of KM practices. In many cases, rigid organizational structures and resistance to change restrict open communication and limit the free flow of knowledge within organizations . Furthermore, the increasing emphasis on sustainability and digital transformation has highlighted the need for robust and adaptive KM frameworks. Emerging concepts such as Green Knowledge Management (GKM) emphasize the role of KM in achieving environmental, social, and economic sustainability. Organizations are increasingly integrating KM practices with sustainability goals to promote resource efficiency, environmental responsibility, and long-term development. At the same time, advancements in digital technologies, including artificial intelligence (AI), data analytics, and information systems, are transforming traditional KM processes by enhancing knowledge creation, storage, and dissemination capabilities . Although significant progress has been made in KM research, several gaps still exist in understanding its application across diverse organizational and industrial contexts. Existing KM frameworks often fail to adequately address cultural and contextual differences, which are critical for successful implementation. Moreover, while KM has been extensively studied in large organizations, its application in small and medium-sized enterprises (SMEs) remains relatively underexplored. SMEs face unique challenges such as limited resources, lack of structured KM systems, and informal management practices, which affect the effectiveness of KM initiatives . In addition, there is a need for more empirical and longitudinal studies to evaluate the long-term impact of KM practices on organizational performance, innovation, and sustainability. Many existing studies are conceptual in nature and lack practical validation across different industries and cultural settings. The integration of KM with emerging technologies and its role in supporting innovation and decision-making processes also require further exploration . This study adopts a literature review-based approach to examine the current landscape of Knowledge Management research, with a focus on its interrelationship with culture, innovation, and sustainability. By synthesizing findings from various studies, the research aims to provide a comprehensive understanding of KM practices, identify key challenges and research gaps, and suggest directions for future research. The study contributes to the existing body of knowledge by highlighting the importance of context-specific KM frameworks and the need for integrating technological, cultural, and sustainability dimensions in KM practices.

Editorial on the Research Topic Data-intensive medicine and healthcare: ethical and social implications in the era of artificial intelligence and automated decision-making Medical "big data" and artificial intelligence (AI) are a hyped duo. Promises include developing more personalised treatments, delegating medical decision-making to tireless and seemingly objective algorithms, improving preventive screening, and providing healthcare more efficiently through predictive risk scores. AI and big data, however, do not automatically transform into improved health outcomes. The practical and functional uses of AI in big data environments require integrating and interpreting a wide variety of medical data (e.g., from genomics or other omics, imaging, biomarker analyses) and other personal data. As a result, AI-driven technology bears various new challenges and risks at the societal, algorithmic, organizational, expert, and individual levels. Scholarship on the ethical, legal, and social issues of using AI in data-intensive medicine and healthcare has highlighted numerous areas of contention, including regulation, explainability, privacy, data sharing and protection, trust, and biases, as well as how AI might affect the patient-doctor relationship and support interdisciplinary expert teams in their decisions. Aiming to extend this perspective, this Research Topic focuses on AI applications in various areas of innovative data-intensive medicine, such as genomics, neuroscience, and child and elderly care. The contributions explore how ethical and social considerations can/should be part of medical AI by considering issues of diversity, the significance of datafication and automation, public and patient participation, developing deliberative or open science approaches (such as open codes), and how to ensure interoperability among developers and users while preventing misuse, hacking, or manipulation. The Research Topic comprises 10 articles dealing with various aspects of

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 recent years, the application of artificial intelligence (AI) technology in the field of online short drama creation has experienced explosive growth. Online short dramas, with their narrative characteristics of "short, concise, and fast" and plot design of high-frequency reversals, cater to the fragmented consumption needs of the public. AI screenwriting, relying on its efficient content generation capability, has become a core tool for the large-scale production of the industry. However, while improving efficiency, AI technology has significantly exhibited the problem of "plot routine dependence"—the content it generates mostly replicates the narrative framework of existing hit short dramas, and high-frequency reversals have fallen into the dilemma of homogenization. This not only restricts the improvement of the artistic value of online short dramas but also poses potential risks to the long-term development of the industry. Combining literature analysis and industrial practice observation, this paper systematically analyzes the manifestations and formation mechanism of the plot routine dependence of AI screenwriting, clarifies the multi-dimensional risks of homogenization in high-frequency reversals, and proposes breakthrough paths from three dimensions: technological optimization, industrial collaboration, and policy guarantee. It aims to provide theoretical reference and practical guidance for the high-quality development of online short dramas in the AI era.

In the current era of rapid development of artificial intelligence (AI) technology, the field of education is undergoing profound transformations[1]. As a crucial component of the Japanese language teaching system, the traditional teaching model of "Japanese Audio-Visual-Speaking" faces numerous challenges in cultivating students' comprehensive language application abilities. Against the backdrop of the AI era, this paper delves into the practice and application of AI technology in the teaching of "Japanese Audio-Visual-Speaking." By analyzing the current applications of AI technology in Japanese speech recognition, personalized learning, and virtual simulation scenario construction, combined with specific teaching practice cases, this paper elaborates on the innovative breakthroughs brought by AI technology to "Japanese Audio-Visual-Speaking" teaching, including advantages such as improved learning efficiency, enhanced interactivity, and personalized instruction. At the same time, it objectively examines the challenges faced during the application of AI technology, such as technological dependence, the transformation of teachers' roles, and data security, and provides an outlook on the future development trends of AI technology in "Japanese Audio-Visual-Speaking" teaching, offering references for promoting the modernization of Japanese audio-visual-speaking instruction.

This comprehensive article explores the importance and role of education, knowledge and key human-centric skills in the era of Artificial Intelligence (AI). AI can be best described as an emerging technology and branch of computer science that relies on machine and deep learning in its attempt to simulate human intelligence by applying special software-encoded heuristic solutions, based on a comprehensive learning process through training data. The modern era is accompanied by numerous challenges in the application of AI, especially from the aspect of education, knowledge and key human skills that are necessary for its ethical and safe application. At its beginning, the article provides basic explanations and the history of the origin of this term, as well as the usual risks and threats of using this new technology. After that, the article is focused towards portraying the role of education and knowledge in the ubiquitous era of the application of Artificial Intelligence. In recent years, contemporary society is facing the unexpected rise of AI in its business, economic, legal, cultural, educational, economic, technological, and many other fields. It has deeply penetrated the sphere of knowledge, education and research and development (R&D) and over time it has been shown that AI has the ability to solve some of the burning problems in the education process, to introduce innovations in teaching practices, to ensure inclusive and equal quality education for all, as well as to revolutionize teaching and learning methods and scientific research processes. However, in an era of pervasive concern about whether or not AI can replace human work and existing jobs, the article concludes that while it can perform some routine tasks and procedures, it cannot replace the systems thinking, humane knowledge and key human skills, otherwise necessary for solving problems and achieving deep scientific truths, knowledge and discoveries. Finally, the article deals with an extensive analysis of key human-centric skills (creativity, critical thinking, communication and cooperation) otherwise crucial for the safe and ethical application of Artificial Intelligence systems.

In the era of artificial intelligence, traditional physical education methods can no longer meet the needs of contemporary college physical education, and it is even more difficult to adapt to the development needs of physical education in the new era. And through the new era of human artificial intelligence technology, physical education can be gradually reformed, and artificial intelligence technology can be reasonably used to construct an intelligent robot system for physical education. The content, scenes, methods and processes of physical education can be continuously innovated and diversified. The evaluation system can better adapt to the individual development needs of contemporary college students. Artificial intelligence makes physical education more intelligent, curriculum construction more scientific, and teaching methods more innovative and future.

This article focuses on analyzing fathers' communication patterns in shaping early childhood science skills in the era of Artificial Intelligence (AI). The main problem identified is the changing dynamics of communication between fathers and children, and its potential impact on the development of children's science skills. The purpose of this study is to identify effective father communication patterns in stimulating children's interest and science skills in the AI era. The research method was qualitative case study of five fathers. Data were collected through interviews, observations, and content analysis of father-child interactions in the context of science learning. Then analyzed using the Miles Huberman model and then data validity with data triangulation. The results showed that father's communication focused on open-ended questions, presenting information with a creative approach, and using AI technology as a learning tool can improve children's science skills. Fathers' understanding and support of technological development is also a key factor in building children's science literacy in the AI era. The findings contribute to practical understanding for parents, educators and policy makers to shape a communication environment that supports the development of children's science skills. The practical implications can be applied in designing family education programs and science learning strategies for early childhood that are appropriate to the development of AI technology.

Knowledge Management Research & Practice (2009) 7, 192–195. doi:10.1057/kmrp.2009.20 This special issue on Knowledge Management and e-Research Technologies aims to provide a distinctive perspective on the role of technology in knowledge work. We begin with the assumption that today’s knowledge work and research practice is increasingly mediated by a diversity of tools which may not traditionally be labelled as knowledge management technologies (Hendriks, 2001; Butler, 2002), but which are embedded within knowledge work practices. Although many have criticised the idea of formal knowledge management technology (Swan et al., 1999; Galliers & Newell, 2001; Hislop, 2002), it is highly apparent that contemporary knowledge work is facilitated using information and communications technologies (ICTs) of various types from the ‘macro’ knowledge management systems that have been heavily criticised to the more ‘micro’ emerging technologies that we may not traditionally associate with the term knowledge management technology. This special issue, therefore, returns to the debate on the role of technology within knowledge creation, dissemination and use, but explores this with a specific focus on the how new, flexible ICTs are emerging, which blur the boundary between technology and social knowledge practices. Within knowledge management theory, it is possible to observe two broad and enduring perspectives towards technology (elaborated persuasively and famously by Hansen et al. (1999) but also by Alavi & Leidner (2001); Schultze & Stabell (2004)) between those who seek to catalogue and capture knowledge within computer systems, believing knowledge as capable of objective representation and is therefore capable of codification (Schultze, 1998), and those who believe knowledge to be inherently tacit and social, and who argue technology’s role is to support collaboration (Schultze, 1998). Both perspectives seem much evident in literature on Knowledge Management Systems, which frequently adopts a view of ‘knowledge management’ through the management of either catalogued, codified, data or of collaborative, social relationships. These two perspectives of codification or collaboration have, however, created, in our opinion, an unhelpful dichotomy and instrumentalisation of ‘knowledge management technology’ (Mitev & Venters, 2009) leading to polarised literature of either codification technology or collaborative systems. This special issue thus seeks to present articles that will stimulate the debate on the interrelationship and connection between technology aiming to codify knowledge and that aimed at collaboration. Our argument is founded on the observation that human action is mediated by tools, in a myriad of forms, which have a materiality to us and form part of our ongoing practice (Orlikowski, 2007; Orlikowski & Scott, 2008). That knowledge is socially constructed through social interaction is Knowledge Management Research & Practice (2009) 7, 192–195 & 2009 Operational Research Society. All rights reserved 1477–8238/09

This introductory article opens the “Applications of AI in the Semiconductor Industry” section by giving a holistic overview of the development of artificial intelligence (AI) technologies applied to the industry. Historically, the semiconductor industry has utilised complex automation for many tasks and areas, especially in repetitive work and uniform processes. The high need for flexibility in manufacturing, increased diversification of products, complexity, and demand for more autonomous operations, including human-machine interaction, have led to a strong push towards using AI technologies in semiconductor manufacturing. AI technologies are applied in semiconductor product development, digitised product definition (DPD), knowledge management system for risk assessment and root cause analysis, image recognition for inspection and defect classification in front end (FE) and back end (BE) applications for anomaly detection in process chains. Deep learning (DL) and Machine Learning (ML) techniques have given a new stimulus to semiconductor industry research to address the unique challenges for semiconductor manufacturing as the technologies nods are evolving and the number of process parameters to be controlled is increasing. In the end, 106the article introduces the four contributions to this section, highlighting the use of AI, computer vision, neural networks (NNs) in various use cases in semiconductor manufacturing processes.

he paper purposes to explore the association between AI and KM by analyzing how AI can contribution companies in handling and using information to improve knowledge processes efficiently. An electronic bibliographic database search and the reference lists of pertinent review articles were used to conduct a systematic review. To guarantee a thorough and organized approach to examining the literature, the study utilized the PRISMA criteria and the PICO model. The results found that the strong relationship between AI and KM. Knowledge management (KM) offers a structure for organizing and managing knowledge, while artificial intelligence (AI) enables machines to learn and process enormous amounts of data. This similarity between AI capabilities and KM framework is crucial for improving organizational information management procedures. Factors including organizational structure, rewards/incentives, and communication, trust, and information systems all play a role in how firms share their knowledge. Organizational performance is enhanced through optimized knowledge management and flow when AI is integrated into KM systems. This leads to more efficient knowledge resource distribution, providing organizations with a competitive edge. Future research should explore the potential of smart assistant systems, which offer social welfares and strengthen competitive advantage. The study also emphasizes the importance of understanding leadership traits and organizational structures that are essential for consistent performance through effective KM practices