Artificial Intelligence Concepts Research Articles

Curriculum Frameworks and Educational Programs in AI for Medical Students, Residents, and Practicing Physicians: Scoping Review.

The successful integration of artificial intelligence (AI) into clinical practice is contingent upon physicians' comprehension of AI principles and its applications. Therefore, it is essential for medical education curricula to incorporate AI topics and concepts, providing future physicians with the foundational knowledge and skills needed. However, there is a knowledge gap in the current understanding and availability of structured AI curriculum frameworks tailored for medical education, which serve as vital guides for instructing and facilitating the learning process. The overall aim of this study is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of AI for medical students, residents, and practicing physicians. We followed a validated framework and the Joanna Briggs Institute methodological guidance for scoping reviews. An information specialist performed a comprehensive search from 2000 to May 2023 in the following bibliographic databases: MEDLINE (Ovid), Embase (Ovid), CENTRAL (Cochrane Library), CINAHL (EBSCOhost), and Scopus as well as the gray literature. Papers were limited to English and French languages. This review included papers that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of papers and study designs were included, except conference abstracts and protocols. Two reviewers independently screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results. Of the 5104 papers screened, 21 papers relevant to our eligibility criteria were identified. In total, 90% (19/21) of the papers altogether described 30 current or previously offered educational programs, and 10% (2/21) of the papers described elements of a curriculum framework. One framework describes a general approach to integrating AI curricula throughout the medical learning continuum and another describes a core curriculum for AI in ophthalmology. No papers described a theory, pedagogy, or framework that guided the educational programs. This review synthesizes recent advancements in AI curriculum frameworks and educational programs within the domain of medical education. To build on this foundation, future researchers are encouraged to engage in a multidisciplinary approach to curriculum redesign. In addition, it is encouraged to initiate dialogues on the integration of AI into medical curriculum planning and to investigate the development, deployment, and appraisal of these innovative educational programs. RR2-10.11124/JBIES-22-00374.

Artificial intelligence co-piloted auditing

This paper proposes the concept of artificial intelligence co-piloted auditing, emphasizing the collaborative potential of auditors and foundation models in the auditing domain. The paper discusses the future relationship and interactions of human auditors and AI, imagining an audit setup where auditors’ capabilities are enhanced through artificial intelligence across a variety of audit tasks. To exemplify the potential of this co-piloted audit paradigm, we illustrate a systematic fine-tuning approach to foundation models using Chain-of-Thought prompting. This study showcases how foundation models can work as collaborators flexibly with auditors, enabling the model to accurately identify transactions from instructions. This study provides a detailed description of the formulated prompt protocols and the corresponding responses generated by ChatGPT, ensuring reproducibility. We envision this work as an initial step towards the widespread implementation of co-piloted auditing, paving the way for more efficient, accurate, and insightful audit procedures.

Integrating Artificial Intelligence into Biomedical Science Curricula: Advancing Healthcare Education

The integration of artificial intelligence (AI) into healthcare practice has improved patient management and care. Many clinical laboratory specialties have already integrated AI in diagnostic specialties such as radiology and pathology, where it can assist in image analysis, diagnosis, and clinical reporting. As AI technologies continue to advance, it is crucial for biomedical science students to receive comprehensive education and training in AI concepts and applications and to understand the ethical consequences for such development. This review focus on the importance of integrating AI into biomedical science curricula and proposes strategies to enhance curricula for different specialties to prepare future healthcare workers. Improving the curriculum can be achieved by introducing specific subjects related to AI such as informatics, data sciences, and digital health. However, there are many challenges to enhancing the curriculum with AI. In this narrative review, we discuss these challenges and suggest mitigation strategies.

Views of veterinary faculty students on the concept of Artificial Intelligence and its use in Veterinary Medicine practices: An example of Ankara University Faculty of Veterinary Medicine

The study was carried out to determine the knowledge levels of the students of Ankara University Faculty of Veterinary Medicine, on the concept of artificial intelligence and its use in veterinary practices. For this purpose, an online questionnaire was applied to a total of 529 students in the study, covering all grades of the faculty. The questionnaire consists of two parts. In the first part, there are 10 questions including demographics, knowledge about the concept of artificial intelligence, etc. The second part consists of 26 5-point Likert-type questions to determine students' thoughts on artificial intelligence applications. Data were analyzed using statistical tests. Consequently, the students participating in the study are partially knowledgeable about artificial intelligence (52.9%). They know the importance of following the developments in artificial intelligence for the profession (45.2%). They think that artificial intelligence applications will improve their professional skills (53.5%). They have the opinion that a robot cannot replace a veterinary surgeon (36.7%) and artificial intelligence cannot cause unemployment in veterinary medicine in the future (35.3%). In addition, they believe that artificial intelligence can cause ethical problems (39.3%) and that applications made with this technology should be developed in an ethical sense (42.4%). As a result, while the students think that artificial intelligence will have positive effects in the field of veterinary medicine, they also think that artificial intelligence can bring negative ethical implications. It can be concluded that including elective courses on artificial intelligence applications in veterinary faculties and conducting further research on the subject would be beneficial.

Educational Approaches with ΑΙ in Primary School Settings: A Systematic Review of the Literature Available in Scopus

As artificial intelligence (AI) becomes increasingly prevalent, it has become a topic of interest in education. The use of AI in education poses complex issues, not only in terms of its impact on teaching and learning outcomes but also in terms of the ethical considerations regarding personal data and the individual needs of each student. Our study systematically analyzed empirical research on the use of AI in primary education, specifically for children aged 4–12 years old. We reviewed 35 articles indexed in SCOPUS, filtered them according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzed them, and categorized the findings. The research focused on the studies’ objectives, learning content, learning outcomes, learning activities, and the pedagogy of activities or the AI tools. Our categorization resulted in three main categories of research objectives regarding the creation, implementation, and evaluation of AI tools and five categories for learning content: AI and ML (machine learning) concepts in STEM and STEAM, language learning, mathematics, arts, and various other subjects. The learning activities were split into four categories: apply, engage, interact, use; project-based learning with multiple activities; experience and practice; and students as tutors. The learning outcomes were split into three levels: cognitive, affective, and psychomotor. The pedagogy of AI tools falls into four categories: constructivism, experiential learning, AI-assisted learning, and project-based learning. The implications for teacher professional development are discussed.

Educational Approaches with ΑΙ in Primary School Settings: A Systematic Review of the Literature Available in Scopus

As artificial intelligence (AI) becomes increasingly prevalent, it has become a topic of interest in education. The use of AI in education poses complex issues, not only in terms of its impact on teaching and learning outcomes but also in terms of the ethical considerations regarding personal data and the individual needs of each student. Our study systematically analyzed empirical research on the use of AI in primary education, specifically for children aged 4–12 years old. We reviewed 35 articles indexed in SCOPUS, filtered them according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzed them, and categorized the findings. The research focused on the studies’ objectives, learning content, learning outcomes, learning activities, and the pedagogy of activities or the AI tools. Our categorization resulted in three main categories of research objectives regarding the creation, implementation, and evaluation of AI tools and five categories for learning content: AI and ML (machine learning) concepts in STEM and STEAM, language learning, mathematics, arts, and various other subjects. The learning activities were split into four categories: apply, engage, interact, use; project-based learning with multiple activities; experience and practice; and students as tutors. The learning outcomes were split into three levels: cognitive, affective, and psychomotor. The pedagogy of AI tools falls into four categories: constructivism, experiential learning, AI-assisted learning, and project-based learning. The implications for teacher professional development are discussed.

Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review.

In the realm of in vitro fertilization (IVF), artificial intelligence (AI) models serve as invaluable tools for clinicians, offering predictive insights into ovarian stimulation outcomes. Predicting and understanding a patient's response to ovarian stimulation can help in personalizing doses of drugs, preventing adverse outcomes (eg, hyperstimulation), and improving the likelihood of successful fertilization and pregnancy. Given the pivotal role of accurate predictions in IVF procedures, it becomes important to investigate the landscape of AI models that are being used to predict the outcomes of ovarian stimulation. The objective of this review is to comprehensively examine the literature to explore the characteristics of AI models used for predicting ovarian stimulation outcomes in the context of IVF. A total of 6 electronic databases were searched for peer-reviewed literature published before August 2023, using the concepts of IVF and AI, along with their related terms. Records were independently screened by 2 reviewers against the eligibility criteria. The extracted data were then consolidated and presented through narrative synthesis. Upon reviewing 1348 articles, 30 met the predetermined inclusion criteria. The literature primarily focused on the number of oocytes retrieved as the main predicted outcome. Microscopy images stood out as the primary ground truth reference. The reviewed studies also highlighted that the most frequently adopted stimulation protocol was the gonadotropin-releasing hormone (GnRH) antagonist. In terms of using trigger medication, human chorionic gonadotropin (hCG) was the most commonly selected option. Among the machine learning techniques, the favored choice was the support vector machine. As for the validation of AI algorithms, the hold-out cross-validation method was the most prevalent. The area under the curve was highlighted as the primary evaluation metric. The literature exhibited a wide variation in the number of features used for AI algorithm development, ranging from 2 to 28,054 features. Data were mostly sourced from patient demographics, followed by laboratory data, specifically hormonal levels. Notably, the vast majority of studies were restricted to a single infertility clinic and exclusively relied on nonpublic data sets. These insights highlight an urgent need to diversify data sources and explore varied AI techniques for improved prediction accuracy and generalizability of AI models for the prediction of ovarian stimulation outcomes. Future research should prioritize multiclinic collaborations and consider leveraging public data sets, aiming for more precise AI-driven predictions that ultimately boost patient care and IVF success rates.

Development of an AI literacy assessment for non-technical individuals: What do teachers know?

With the exponential development and vast interest in artificial intelligence (AI), the global economic impact of AI is expected to reach $15.7 trillion by 2030. While AI has infiltrated everyday life, a lack of knowledge of what AI is and how AI works is ubiquitous across all ages and professions. Teaching AI literacy to non-technical individuals has become imperative and requires immediate attention, however, assessing AI literacy has heavily relied on subjective measurements such as qualitative assessment and self-reported surveys, which may lead to biased results. This study contributes to the field by developing and validating an assessment created based on a well-established AI literacy framework. A total of 196 responses were collected from pre-and in-service teachers in the United States, and 186 responses were included in the analysis to validate the assessment. The final assessment comprises 25 objective-based items reduced from an originally 31-item assessment. Both experts’ insights were sought, and statistical methodology was employed to ensure the validity of the assessment. The results indicate that pre-and in-service teachers have a moderate level of AI literacy and in-service teachers performed slightly better than pre-service teachers on our assessment. Inconsistent answers across various AI concepts indicate that teachers may possess an even more ambiguous understanding of certain AI concepts.

Artificial Intelligence (AI) in Early Childhood Education, Exploring Challenges, Opportunities and Future Directions: A Scoping Review

AI literacy has emerged as a crucial aspect of digital literacy research in the field of education. Currently, there are limited studies about the implications of Artificial Intelligence (AI) in Early Childhood Education (ECE). Owing to the recent development of curricula for young learners in industrialized nations, developing countries are hesitant to adopt AI at the ECE level. A scoping review was undertaken on the content of fourteen research articles published between 2016 and 2023. This scoping review evaluates and reviews the contents of fourteen papers on the knowledge and comprehension of AI in ECE, which covers curriculum design, artificial intelligence tools, instructional methodologies, research designs, evaluation methods, findings, and various types of possibilities and problems linked to AI literacy and content. Several obstacles were identified, including (1) an insufficiently designed curriculum, (2) lacking instructors' understanding, experience, and trust in AI, and (3) the lack of an instruction manual. Engaging in reading can offer educational possibilities and foster the growth of AI literacy in young learners, encompassing AI concepts, actions, and perspectives. This study recommended AI literacy for the educators and learners of ECE to be suitable for their age group and level.

Turkish Language Prospective Teachers’ Perceptions of Metaphors Regarding Artificial Intelligence

The concept of Artificial Intelligence (AI) initially emerged as a term in the field of computer science. In the subsequent years, this concept transcended its origins and became relevant across various domains of human life. Nowadays, it’s possible to encounter AI in nearly every aspect of human life. In this context, it’s considered noteworthy to examine individuals’ metaphorical perceptions of AI. Accordingly, the purpose of the research is determined as the investigation of Turkish prospective teachers’ metaphorical perceptions of AI. Phenomenology, a qualitative research method, is employed in the study. The study group consists of 115 voluntary Turkish prospective teachers, 94 of whom are female and 21 are male, studying at the Faculty of Education at Artvin Çoruh University. Among the prospective teachers, 36 are first-year students, 37 are second-year students, 26 are third-year students, and 16 are fourth-year students. Data for the research are collected through semi-structured interview forms prepared by the researchers. Content analysis method is used for data analysis, and the data are presented by tabulating them along with frequency values. The analysis reveals that 115 Turkish language prospective teachers produced a total of 110 metaphors. Among these metaphors, 21 belong to male prospective teachers, while 89 belong to female prospective teachers. Metaphors such as human (f19), robot (f10), future (f4), ocean (f3), storm (f2), spring (f2), assistant (f4) are found to be generated by both female and male prospective teachers. In addition to positive metaphors, Turkish language prospective teachers also generated negative metaphors like threat (f2), massacre (f1), monster (f1), etc., concerning AI. Consequently, although Turkish language prospective teachers developed some negative metaphors, it’s observed that their perceptions of AI are predominantly positive.

THE ROLE OF ARTIFICIAL INTELLIGENCE IN MAKING FOREIGN POLICY DECISIONS IN THE UKRAINIAN-RUSSIAN WAR

The article considers the application of the artificial intelligence systems as a new tool in making foreign policy decision. The content of the concept of artificial intelligence is studied, the scope of the use of artificial intelligence systems in everyday life and in international relations is studied. It can be noted that AI, as a progressive and rapidly developing technology, has a large set of tools that help people make decisions and increase the efficiency of their work, for example, save time and spent resources to achieve a particular result. With the development of technology, more and more machines with artificial intelligence are used in various areas of life. For example, the field of medicine, mechanical engineering, data analysis, public administration and politics - all these fields are actively developing with using of artificial intelligence technologies. Special attention is paid to how artificial intelligence affects in making decision, event forecasting, and automation of data analysis. The use of artificial intelligence systems in international diplomacy was analyzed. Attention is focused on the weak and strong sides and what risks this technology can carry for foreign policy decisions. The given statistical data show how artificial intelligence is treated in Ukraine. Based on the research, it can be concluded that for the widespread use of artificial intelligence, it is necessary to develop convenient and transparent rules and algorithms, norms for the using of technology and its interaction with people. Summarizing the discussed topic, we will come to the conclusion that artificial intelligence will soon become a powerful tool in international relations, diplomacy, and in other areas of our life, which will bring benefit and a real threat. But in order for the benefit to be greater, it is necessary that the people who will use artificial intelligence be trained and knowledgeable in the technology.

USE OF ARTIFICIAL INTELLIGENCE IN ACCOUNTING AND ANALYTICAL PROCESSES

The beginning of a new era has led to the emergence of new factors that impede the effective operation of enterprises. One of these factors is the exponential growth of the amount of data that needs to be processed and analyzed in order to make effective management decisions. This leads to the need to develop or search for a software product aimed at collecting, analyzing and systematizing a large amount of data, as well as performing relevant tasks based on them that artificial intelligence is capable of. The article is aimed at researching and revealing the potential of using artificial intelligence (AI) in the field of accounting. The main focus is on improving the efficiency of the enterprise, which is achieved by automating and optimizing various aspects of accounting work. On the basis of a thorough analysis of the works of foreign and domestic specialists, the theoretical aspects of the concept of artificial intelligence are distinguished on the basis of systemic, functional and information approaches. The directions of compliance of the accountant, which can be artificial intelligence, are identified, namely: processing of invoices, cost management, analysis of financial statements, management of payables and receivables, compliance with tax legislation and detection of fraud and risk management. Ways of using AI in processes of automation and optimization of processes, such as invoice processing, cost management, financial reporting analysis, control of accounts payable and receivable, ensuring compliance with tax legislation and fraud detection and risk management, have been identified. The artificial intelligence technologies for their purpose, which are the most used in the world, are presented. The sequence of actions in the identified needs in artificial intelligence and its outstanding volume is structured. The results of the study confirm the potential of AI to automate operations, increase productivity and accuracy in accounting, as well as reduce risks and improve audit efficiency. Key words: Artificial intelligence, accounting, analysis, accounting automation, accounting information systems, information systematization, management efficiency.

Application of artificial intelligence in retail

In the context of the contemporary Industry 4.0 concept, artificial intelligence (AI) has become an integral component of human lifeʼs success, the impact of which is increa­singly difficult to overestimate today. With the advent of AI, the everyday life of individuals has noticeably transformed. Neural networks are capable of working in an algorithmic way, adapt, learn, and even generate new inven­tions within a short span of time. Since the concept of AI appeared, fervent debates have revolved around its advantages and opportu­nities for modern society, ethical frameworks of application, risks, and the negative impact of AI on various professional spheres. However, the revolutionary changes brought about by AI, enabling individuals to delegate routine tasks and freeing up time for creative endeavors, remain indisputable. Despite the high significance of previously conducted research, the state and potential opportunities of industrial application of AI remain insufficiently explored. The purpose of the study is to determine the current trends in the development of artificial intelligence, to assess the actual state of use and to justify recommendations for the future use of AI systems in the retail sector. It has been deter­mined that the introduction of AI technologies is a modern global trend and one of Ukraineʼs priorities. It has been proven that the complex realities of domestic retail development unveil opportunities for digital transformation and the intensification of AI implementation. The current state of AI development has been examined: the leading countries in artificial intelligence implementation have been identified, and the retail sector has been ranked fourth among industries with high potential for the application of advanced technologies. To gain an understanding of AI development in retail, a survey was conducted, the results of which delineated business processes where AI techno­logies are actually applied and specified the goals of their application. The priority opera­tional processes of retail enterprises for the implementation of advanced technologies have been identified and ranked by the significance of AI systems, which are most relevant to modern retail. A list of AI systems has been sorted according to their purpose and functional capa­bilities in retail.

RAG based Chatbot using LLMs

Historically, Artificial Intelligence (AI) was used to understand and recommend information. Now, Generative AI can also help us create new content. Generative AI builds on existing technologies, like Large Language Models (LLMs) which are trained on large amounts of text and learn to predict the next word in a sentence. Generative AI can not only create new text, but also images, videos, or audio. This project focuses on the implementation of a chatbot based the concepts of Generative AI and Large Language Models which can answer any query regarding the content provided in the PDFs. The primary technologies utilized include Python libraries like LangChain, PyTorch for model training, and Hugging Face’s Transformers library for accessing pre-trained models like Llama2, GPT- 3.5 (Generative Pre-trained Transformer) architectures. The re- sponses are generated using the Retrieval Augmented Generation (RAG) approach. The project aims to develop a chatbot which can generate the sensible responses from the data in the form of PDF files. The project demonstrates the capabilities and applications of advanced Natural Language Processing (NLP) techniques in creating conversational agents that can be deployed across various platforms in the corporation, to enhance user interaction and support automated tasks. Index Terms—Generative AI, Artificial Intelligence, Natural Language Processing, Large Language Model, Llama2, Tran- formers, Document Loaders, Retrieval Augmented Generation, Vector Database, Langchain, Chainlit

On the Question of the Concept of Artificial Intelligence Technologies

Despite the existing legal definition, the concept of artificial intelligence, which has penetrated various spheres of human life, requires a more precise definition. However, in most cases, artificial intelligence is opposed to human intelligence, the so-called natural intelligence, which looks natural, but it is difficult to recognize as true. The article substantiates that the ways of understanding human intelligence and artificial intelligence (or machine intelligence) they are completely different because their nature is different. Based on the analysis of various approaches of scientists, both lawyers and engineers, as well as legislators to the definition of artificial intelligence, the author concludes that the most appropriate really understanding of artificial intelligence as a field of scientific knowledge of a complex nature. The author makes a conclusion about the ways of further application of artificial intelligence technologies in forensic activities, considering the spread of artificial intelligence technologies. The result of the study is the formulation of the definition of artificial intelligence technology.

Fostering students' AI literacy development through educational games: AI knowledge, affective and cognitive engagement

AbstractBackgroundAs the significance of artificial intelligence (AI) continues to increase, there is a need for effective scaffolding and support for novice learners. Educators have encountered challenges in effectively scaffolding novice learners AI concepts, and providing appropriate motivational support. Research evidence has shown the potential of game‐based approaches to fostering secondary school students' AI literacy and motivation to learn AI.ObjectivesThis study developed an online platform TreasureIsland to gamify ebooks and investigated whether and how students playing with it can effectively enhance their AI literacy. This study aims to contribute an empirical and theoretical basis for AI literacy education and promote the use of gamification that would be broadly applied in other schools.MethodsA quasi‐experiment was conducted to evaluate the effects of the proposed gamified approach, which included a control group using an ebook with playful resources. To triangulate the quantitative results obtained from the pre and post‐test, focus group interviews were also conducted.ResultsThe platform was effective in improving students' motivation, self‐efficacy, career interest, and understanding of AI concepts and ethics, but did not enhance their confidence of using AI, and high cognition of applying, evaluating and creating AI. TreasureIsland players demonstrated significant improvement in all affective and cognitive domains, except for the ability to apply, evaluate, and create AI. Interviews revealed that the gamified approach could promote students' AI literacy by adhering to guidelines, including (1) creating a competitive and motivating learning environment through game mechanics, (2) providing scaffolding modules and feedback, and (3) visualising complex AI concepts via simulations. Feedback collected from the study suggested adding pedagogical elements such as flipped classrooms and project‐based learning in future research to improve the instructional design, and enable students to reach a higher level of cognition.ConclusionsThis study concludes that the use of gamification can provide affective and cognitive support and an enjoyable experience for fostering learners' AI literacy. It helps instructional designers and teachers enrich the pedagogical knowledge related to gamified platform and AI literacy.

MaGENEgerZ: An Efficient Artificial Intelligence-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

Understanding breast cancer drug response mechanisms can play a crucial role in improving treatment outcomes and survival rates. Existing bioinformatics-based approaches are far from perfect and do not adopt computational methods based on advanced artificial intelligence concepts. Therefore, we introduce a novel computational framework based on an efficient support vector machine (esvm) working as follows: First, we downloaded and processed three gene expression datasets related to breast cancer responding and non-responding to treatments from the gene expression omnibus (GEO) according to the following GEO accession numbers: GSE130787, GSE140494, and GSE196093. Our method esvm is formulated as a constrained optimization problem in its dual form as a function of λ. We recover the importance of each gene as a function of λ, y, and x. Then, we select p genes out of n, which are provided as input to enrichment analysis tools, Enrichr and Metascape. Compared to existing baseline methods, including deep learning, results demonstrate the superiority and efficiency of esvm, achieving high-performance results and having more expressed genes in well-established breast cancer cell lines, including MD-MB231, MCF7, and HS578T. Moreover, esvm is able to identify (1) various drugs, including clinically approved ones (e.g., tamoxifen and erlotinib); (2) seventy-four unique genes (including tumor suppression genes such as TP53 and BRCA1); and (3) thirty-six unique TFs (including SP1 and RELA). These results have been reported to be linked to breast cancer drug response mechanisms, progression, and metastasizing. Our method is available publicly on the maGENEgerZ web server.

Artificial Intelligence in the Effective Execution Process of Construction Projects in the Future

The construction industry currently constitutes 13% of the global gross domestic product (GDP), with projections indicating an 85% increase in value to $15.5 trillion by 2030. The widespread adoption of information technology (IT) has significantly enhanced the integration of disparate data in construction project environments. Consequently, the construction sector including full construction value chain, is undergoing a transformative phase. The increasing investment in artificial intelligence (AI) makes it impossible to keep pace with its rapid advancements. Hence, this study aims to examine the role of AI in facilitating the effective execution of construction projects in the future. This research employs a document analysis approach and scrutinizes 20 relevant papers from both domestic and international scientific databases. Methodologically, this study adopts an applied research approach, and based on the method of data collection, it is considered a descriptive survey method. Therefore, a questionnaire was designed and distributed among 100 experts and practitioners familiar with AI concepts in Tehran for data collection to conduct a census-style field study. Subsequently, Smart PLS software was employed for data analysis. The findings not only validate the model's reliability, validity and fit but also present solutions and pertinent issues related to challenges concerning AI future role in enhancing project execution efficacy.

The white paper on artificial intelligence as a source for the formation of European Union legislation in the field of artificial intelligence

The article analyzes the provisions of the White Paper on artificial intelligence as a source of the formation of European Union legislation in the field of artificial intelligence. It is noted that the study of legal regulation of artificial intelligence in the European Union will contribute to the formation of the domestic source base in the field of application of artificial intelligence. It has been analyzed that in scientific circles, the provisions of the White Book on artificial intelligence are highlighted by domestic scientists in a fragmentary manner, which makes it difficult to comprehensively perceive the content and meaning of the White Book as a whole. The article states that the legislation of Ukraine needs improvement, which consists in compliance with European standards in the field of artificial intelligence. It has been established that it is advisable to enshrine provisions on the use of artificial intelligence at the legislative level. The article examines the provisions of the European Data Strategy, which recognizes artificial intelligence as an important component of the data economy. It has been studied that the provisions of the European Data Strategy provide for a combination of technology, industry, digital infrastructure and regulatory framework. It was noted that the provisions of the Strategy regarding artificial intelligence were further developed in the White Book. The article specifies the concept of artificial intelligence in accordance with the provisions of the White Book. It is noted that the Provisions of the White Book are aimed at ensuring consultation of interested parties, collection of feedback as a basis for future decision-making processes. It has been proven that the White Paper can be considered a political document containing the goals and possible actions of the European Commission regarding the regulation of artificial intelligence and contains a list of political proposals, intentions and recommendations. It is noted that the White Book is a framework for future legislative proposals on the formation of a legal framework related to artificial intelligence in the European Union. The article defines the structure of the main provisions of the White Book and establishes the fundamental principles that determine the development and implementation of artificial intelligence. Attention is focused on chapter 5 of the White Paper, which is devoted to the «Ecosystem of Trust: Regulatory Framework for Artificial Intelligence», which is devoted to the key elements of the regulatory framework for artificial intelligence in Europe.

Problematic aspects of the implementation of artificial intelligence in the field of jurisprudence

The article is devoted to highlighting problematic aspects of the implementation of artificial intelligence in the field of jurisprudence, in particular, in certain branches of law. Due to the rapid development of neural network capabilities, artificial intelligence is a tool for quickly completing tasks and processing information in various spheres of society, including in jurisprudence. Therefore, for a comprehensive analysis, it is necessary to identify problem areas for the implementation of such a phenomenon. It is noted that today there is no unified approach to defining the concept of artificial intelligence. The authors provide several definitions of this concept for the most complete understanding of the essence of artificial intelligence. The legislation does not contain a normative legal act dedicated to the issue of the essence and procedure for using artificial intelligence, which gives rise to such dilemmas. Despite the widespread use of artificial intelligence capabilities among citizens, there is a lack of interest, funding and development of educational methods for the effective use of artificial intelligence in professional activities on the part of government authorities. It is also stated that the introduction of artificial intelligence into professional activities requires staffing with specialists who can service the software. The problems of introducing artificial intelligence in the field of legal proceedings are analyzed. The use of artificial intelligence by courts risks undermining the autonomy of courts and the independence of judges. Artificial intelligence is not able to comprehensively evaluate evidence and make decisions based on moral standards. The issues of transparency of courts, guaranteeing fundamental human rights and freedoms, and software reliability remain open. The legal status of artificial intelligence in civil law is considered. Since artificial intelligence is capable of creating a work, questions remain regarding the protection of copyrights using artificial intelligence and legal liability in case of copyright infringement. A debatable and controversial topic is the definition of artificial intelligence as a subject or object in a civil legal relationship. It has been determined that the introduction of artificial intelligence in criminal proceedings can become a factor in increasing cyber attacks on the neural network with the aim of stealing, destroying, and making data public.