A Review of Research on Artificial Intelligence Life Cycle Based on Bibliometrics

The article is devoted to the study of the application of artificial intelligence technologies in law enforcement activities in Ukraine and the world. The purpose of the article is to analyze the international and domestic experience of using digital technologies in law enforcement activities, to identify the shortcomings of the legal regulation of the use of artificial intelligence in this area, to provide proposals for its improvement in Ukraine. It was found that one of the countries that is actively expanding the use of artificial intelligence and successfully introducing relevant technologies into public administration is China. The artificial intelligence system which is used in China called «Zero Trust». The purpose of using this system is to prevent the commission of corruption offenses. It is emphasized, that the disadvantage of the «Zero Trust» system is that artificial intelligence, identifying the cause of corruption, does not indicate the algorithms and principles of this process. At the same time, the experience of using artificial intelligence in the field of preventing and combating corruption in Ukraine is considered when carrying out automated verification of persons declarations of authorized to perform state and local government functions. Ways of improving legal regulation when conducting financial control of declarations using artificial intelligence technologies are proposed. It has been found that artificial intelligence is widely used in the USA to predict the prevalence and state of crime. It was emphasized that Ukraine also has experience in the application of artificial intelligence technologies in law enforcement activities. Artificial intelligence is used in the development and implementation of departmental specialized intelligent software, the implementation of operative and investigative actions (for the recognition of video objects observation by comparing images in social networks, etc). It was concluded that artificial intelligence and digital technologies help to quickly process large volumes of information in the investigation and prevention of criminal activity. Modern developments in the field of artificial intelligence and advanced digital technologies, which are implemented in foreign countries to prevent and fight crime, are of great importance for the improvement of legal regulation in this field in Ukraine.

Art education has undergone significant transformation in the era of digitalization. With the advancement of Artificial Intelligence (AI) technology, there is tremendous potential to enhance the art learning experience. This research aims to elucidate how AI technology can be effectively utilized to improve art education in the context of digitalization. The study aims to introduce AI technology and explore its potential applications in enhancing the art learning experience. In the context of art learning experience, AI technology can provide broader access to digital art collections, online galleries, and virtual museums. Students can explore various artworks and cultures through digital platforms, thereby enriching their understanding and appreciation of art. The research findings reveal that AI technology can offer personalized guidance to students in developing their artistic skills. Intelligent AI systems can analyze students' artworks, provide tailored suggestions, and assist in enhancing their technical and creative abilities. In conclusion, the integration of AI technology in art education holds immense potential to elevate students' learning experiences. By expanding access, providing personalized guidance, and creating interactive experiences, AI technology enriches art learning in the era of digitalization. This is expected to provide greater insights and understanding of how AI technology can be harnessed to enhance the art learning experience in the context of digitalization. By optimizing the potential of AI, art education can become more inclusive, personalized, and engaging.

PurposeThe introduction of artificial intelligence (AI) technology has had a substantial influence on the retail industry. However, AI adoption entails considerable responsibilities and risks for senior managers. In this study, the authors developed an evaluation and selection mechanism for successful AI technology adoption in the retail industry. The multifaceted measurement and identification of critical factors (CFs) can enable retailers to adopt AI technology effectively and maintain a sustainable competitive advantage.Design/methodology/approachThe evaluation and adoption of organisational AI technology involve multifaceted decision-making for management. Therefore, the authors used the analytic network process to develop an AI evaluation framework for calculating the weight and importance of each consideration. An expert questionnaire survey was distributed to senior retail managers and 17 valid responses were obtained. Finally, the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method was used to identify CFs for AI adoption.FindingsThe results revealed five CFs for AI adoption in the retail industry. The findings indicated that after AI adoption, top retail management is most concerned with factors pertaining to business performance and minor concerned about the internal system's functional efficiency. Retailers pay more attention to technology and organisation context, which are matters under the retailers' control, than to external uncontrollable environmental factors.Originality/valueThe authors developed an evaluation framework and identified CFs for AI technology adoption in the retail industry. In terms of practical application, the results of this study can help AI service providers understand the CFs of retailers when adopting AI. Moreover, retailers can use the proposed multifaceted evaluation framework to guide their adoption of AI technology.

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.

This paper explores the use of big data to measure company culture, good and bad, including toxic culture. Culture is a central factor driving employee experiences and contributing to the “great resignation”. Harnessing the key Artificial Intelligence (AI) technology of neural networks using deep learning methodology for NLP provides the capability to extract cultural meanings from a diverse array of organizational information and cultural artefacts ( texts, images, speech and video) available online. Using big data and AI provides a predictive capability surpassing the value of employee survey instruments of the last century providing a rear view of insights. Big data helps break free from the paradigm of only thinking about culture moving at a glacial pace. An innovative methodology and AI technologies help measure and visually plot the organizational culture trajectory within a company cultural landscape. Cultural values, inclusive of toxicity, have the potential for detection across all forms of communications media. A non-invasive approach using a broad range of open data sources overcomes limitations of the traditional survey instruments and approaches for achieving a culture read. The benefits of the approach and the AI technology are the real-time ingestion of ongoing executive and managerial feedback while entirely sidestepping the issues of survey biases and viable samples. The methodology under study for reading a culture moves well beyond traditional text-centric searches, content analyses, dictionaries and text mining, delivering an understanding of the meanings of words, phrases, sentences or even concepts comprising company culture. Embeddings are an ideal neural network breakthrough technology enabling the computation of text as data through creating a meaningful space in which similar word meanings exist in close proximity. Vector algebra in a multidimensional space helps unpack the cultural nuances and biases pent up within the unstructured information flowing through and from organizations, from tweets to text-centric corporate communications, including annual reports. This modelling enables predictions about an organizational future culture based on communication data existing across internal and external digital platforms. The variety of communications represents the twenty-first century culture requiring exploration and discovery. Visualizations of the traces of multiple dimensional cultures make current-state and culture predictions for an organization and competitive organizations in the same or adjacent industries within a company cultural landscape.

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.

In addition to creativity, modern landscape design also needs some auxiliary tools to predict the design effect, so as to ensure the design effect before the completion of the landscape. With the development of computer network and electronic information technology, human society has entered the information age, and artificial intelligence technology has been applied to all walks of life. With the continuous development of artificial intelligence, artificial intelligence has existed in all aspects of our lives and has a great impact on our lives. At the same time, through the achievements in recent years, artificial intelligence gradually affects other fields such as design. Therefore, combined with the current situation of landscape design auxiliary system in China, this paper introduces artificial intelligence and information technology into it, and makes the landscape design auxiliary system get further development with the help of artificial intelligence and information technology. This paper first introduces artificial intelligence, analyzes the advantages and development trend of automatic informatization of landscape design auxiliary system, and finally discusses the application of artificial intelligence technology in landscape design auxiliary system.

Artificial Intelligence (AI) technologies are becoming increasingly pervasive throughout the business industry. The rapid diffusion of this technology in organizations is due to the intrinsic benefits that can be realized by those who successfully adopt them into their business practices. Those organizations that fail to adopt AI technologies will be at a competitive disadvantage over those that do. As can be found throughout IS literature, successful adoption depends on users’ attitudes and behavioral intentions toward AI technology. Therefore, given the rapid diffusion of AI and the need for organizational success, it is important to research and understand the determinants of users’ attitudes and behavioral intentions toward adopting AI technologies.This current research proposed an extended Technology Acceptance Model (TAM) that includes variables identified within literature to be determinants of users’ attitudes and behavioral intentions toward adopting AI technologies. The proposed model (PM) was then tested by conducting a cross-sectional survey of students in a business college. The survey results were analyzed to determine which of the proposed variables increase TAM’s ability to explain attitudes and predict behavioral intentions toward adopting AI technologies. Further analysis also identified which of the proposed variables significantly influence business students’ attitudes and behavioral intentions to adopt AI technologies.The study found support that perceived usefulness was a significant determinant of students’ attitudes towards AI technologies. However, contrary to previous findings, the study found perceived ease of use negatively influenced attitudes towards AI technologies. From these findings, this study sought to provide insights that can inform educators and practitioners of curriculum and training design topics that will increase the likelihood of AI technology adoption. Specifically, the results suggested that educators and curriculum and instructional design practitioners should focus on usefulness and not on simplicity of use. The study also recommended that future research extend the current model with moderators and mediators such as innovation resistance and perceived risk.<br>

The acceptance of technology at the higher educational level has been a significant discussion, with little attention on the gender dynamics on the acceptance of artificial intelligence (AI ) tools by senior lecturers. This study delved into a detailed analysis of the gender dynamics in the discussion of technology acceptance mainly AI tools, in foreign language (FL) education. Quantitative study approach was adopted in the process, and survey design was implemented. Data was collected using structured digital questionnaire, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. A total of ninety-five (95) male senior lecturers and one hundred and three (103) female senior lecturers participated in the study. Analysis was conducted using relevant statistical measures. The results showed disparities in the attitudes and views of senior lecturers towards artificial intelligence (AI) technologies in the context of FL education, greatly influenced by gender. In relation to usage, male senior lecturers have higher positive reactions (61.06%) in comparison to their female counterparts (46.6%). However, in relation to the assumption that AI technologies improve the performance of learners, 69% of male senior lecturers agree with this notion, but a substantially greater percentage of 72.81% of female senior lecturers hold the same perspective. Moreover, there exists a little disparity in the level of proficiency in using AI technologies across genders. Specifically, 56.84% of male senior lecturers see it as uncomplicated, while 61.16% of their female counterparts share the same sentiment. The gender discrepancy that is most notable pertains to the perceived level of ease in using artificial intelligence (AI) technologies during foreign language (FL) lessons. The data reveals that a majority of male senior lecturers, calculated as 69.48%, see the use of these tools very easy. In contrast, a much higher proportion of female senior lecturers, 86.41%, share the same perception. This discrepancy highlights a notable disparity in confidence levels between the two genders. These results together emphasise the changing gender dynamics in the acceptance of technology, interrogating conventional assumptions and underscoring the need for customised support systems to guarantee fair and efficient integration of artificial intelligence (AI) technologies in foreign language instruction among senior lecturers.

The development of artificial intelligence (AI) technology is often seen as the domain of engineers, but ultimately, it is the everyday consumer who uses it. This means that AI is not just an engineering challenge; it must also be understood from the perspectives of marketing and consumer behavior. In this study, we first use the CiteSpace knowledge mapping tool to analyze research trends and their evolution in the field of AI from a macro perspective. Then, from a micro perspective, we carefully select and review key literature to define the concept of AI, and further provide a comprehensive overview of AI applications in marketing. This includes (1) AI technology and product acceptance factors, (2) Algorithm avoiding and doubts about AI, (3) Industry-spcific application of AI, and (4) Consumer behavior and psychological on AI. Finally, based on existing research findings, we propose future research topics and directions that are necessary for the continued exploration of AI in this context. The insights from this study can inspire new research ideas related to AI for marketing and consumer scholars. Furthermore, it offers an opportunity for business marketers and government policymakers to consider AI not just as a technological issue, but from the perspective of the consumers who will ultimately adopt and interact with these technologies.

This research aims to identify effective ways of integrating Artificial Intelligence (AI) technology with innovative learning methods to improve students' creative thinking skills in various educational contexts. The bibliometric method, a quantitative and statistical approach to bibliographic data such as publications and citations, serves as the research method. Data were collected on July 2024, through the SCOPUS database using the keywords "creative thinking" OR "creative thinking skills" AND "creativity.". Data analysis was conducted in three stages: data collection, visualization, and analysis using software such as VosViewer. The results showed research trends, knowledge gaps, and potential new research areas relevant to technology integration in education. This research contributes to developing more effective and inclusive education policies and practices by ensuring all students have equal opportunities to develop essential skills in the digital age. Limitations of this study include limited access to technology in some areas and a need for teacher training in integrating AI into the learning process. Future research recommendations include focusing more on improving access to technology and teacher training to effectively use AI technology in education.

Artificial intelligence (AI) technology, as one of the most sophisticated science and technology in today's world, is increasingly being used to production and life, particularly in the manufacturing business. it demonstrates how artificial intelligence technology is used in mechanical manufacturing, namely in defect detection, quality inspection, enhancing workplace safety, and other areas. Artificial intelligence technology is becoming increasingly important in people's lives as it becomes more widely used in people's daily lives, such as the widespread use of smart dishwashers and smart sweepers, which are the products of the fusion of artificial intelligence and the mechanical manufacturing industry. Indeed, artificial intelligence technology has been widely utilized in the mechanical manufacturing business, which not only ensures production precision, but also enhances job productivity and workplace safety. The rise of artificial intelligence has caused significant changes in the manufacturing industry as a whole. Without exception, the manufacturing industry must rely on AI technology to accomplish automation and intelligent development, as well as to improve productivity. Using artificial intelligence to categorize mechanical components, we may propose parts from a based solely on an image or CAD model. To find a necessary component in a machines we must currently browse through a catalogue and be able to discern which part you want based on the available possibilities and your understanding of the catalogue. There are serial numbers to memorize since a single digit or character change might indicate a different sort of part. The algorithm will choose which sections are the best and will significantly facilitate our search.

In an era where artificial intelligence (AI) technology is increasingly penetrating various aspects of life, including education, it is important to understand its impact on students' social relationships. These changes raise questions about how interactions between students and teachers are affected by AI Technology. This research aims to investigate the impact or artificial intelligence on students' social relationships in an educational environment. The focus is on understanding changes in interactions between students, student-teacher interaction, as well as broader implications for social dynamics inside the classroom and outside the classroom. The research method used is a literature review which uses data collections sources that are relevant to this research, which can be in books, megazines and other print media, or can be obtained from photos and videos. The research results show that artificial intelligence technology has a complex impact on students' social relationships. Although it can improve efficiency and learning outcomes, AI technology can also reduce social inequality, and create excessive dependence on technology. Educators and policymakers need to carefully consider the use of AI technology in education to ensure that positive impacts are maximized, while negative impacts are minimized.

This study investigates the transformative role of Artificial Intelligence (AI) technologies in the agricultural sector, specifically focusing on their applications in enhancing animal welfare, health monitoring, and productivity. Over the last decade, advancements in AI, including machine learning, Internet of Things (IoT) systems, computer vision, and sensor technologies, have significantly enhanced livestock management across various species, including poultry, pigs, dairy cows, sheep and rodents. The study synthesizes findings from literature published between 2010 and 2022, highlighting how AI solutions facilitate comprehensive environmental monitoring, enable early disease detection, and support behavioral analysis, thereby optimizing production metrics and promoting better animal welfare practices. By employing the Technology Readiness Levels (TRL) framework, the review categorizes these technologies according to their developmental stages, from prototype to operational use, underscoring their increasing integration into commercial farming. Furthermore, the paper discusses the broader implications of AI adoption, including economic benefits, compliance with welfare standards, and sustainability objectives. The insights presented emphasize the need for continued research and innovation to fully harness AI’s potential in advancing animal management strategies and addressing the growing global demand for animal products. Overall, the findings confirm that AI technologies have demonstrated effectiveness in improving animal welfare, health monitoring, and productivity across diverse species and farming contexts.

The article discusses the essence and features of modern Internet marketing based on the use of artificial intelligence (AI) technologies. The article examines how AI can help online marketers collect and analyze consumer data, segment and personalize audiences, create and distribute effective content, optimize and automate advertising campaigns, and measure and improve marketing results. The article also analyzes the latest trends in the use of AI technologies by Ukrainian and foreign companies and identifies their advantages.The investigation identified effective AI tools in online marketing, such as chatbots, recommender systems, text generators, sentiment analysers, face and speech recognition systems, etc. The article presents a comparison of AI tools in online marketing of Ukrainian and foreign companies. Foreign companies use modern products that have no analogues in Ukraine. This is due to the popularity of the trend of using AI technologies in foreign companies. In Ukraine, this trend is developing and we can see it in the use of chatbots and CRM systems. They have been implemented by such companies as PrivatBank and Promodo. Despite this, the development of foreign companies is progressive. Previously, only large corporations could afford to use AI technologies, but now young startups are catching up with the trend. AI combined with human capabilities can actually turn business into a universal tool for profit. However, this path is not without obstacles, so the article highlights the challenges of AI that may affect the economic, legal, ethical, and social spheres of life. Scenarios for preventing possible problems allow for the effective use of AI's potential. The article suggests measures that should help society adapt to changes in the socio-economic environment associated with the displacement of humans by AI. It is important to understand the advantages and disadvantages of modern online marketing tools to ensure effective solutions to business problems.