ARTIFICIAL INTELLIGENCE IN LINGUISTICS: MODELING UNIVERSAL PHONOLOGICAL SYSTEMS FOR SUSTAINABLE COMMUNICATION

"Background: Artificial Intelligence (AI) has become a ubiquitous phenomenon in recent times, with most organizations today attempting to maneuver their way around developing AI systems with the aim of improving the products and services they provide. However, what complicates developing AI systems is the paucity in frameworks to support organizations with AI System Development (SD). As a result, many organizations are using existing approaches which have been previously applied in earlier emerging Information Technology endeavors. This study explored how a framework can promote effective organizational AI SD. To achieve this a holistic framework for AI SD was conceptualized and examined from an organizational perspective. Method: This study has examined the conceptualized framework People-Process-Data-Technology (2PDT) in AI, through a case study research design. The empirical data was analyzed based on 12 case studies within Australia including 39 interviews with AI experts. We have applied thematic analysis to investigate requirements of organizational AI SD. Results: The results demonstrate that organizations are challenged by key factors, which inhibits their ability to effectively develop AI systems. For example, organizations are not achieving successful delivery of AI systems due to a lack of required skills. Additionally, a plethora of AI technology which is constantly evolving, poor data quality, and the paucity of AI SD frameworks are all contributing to unpredictable delivery outcomes. Conclusion: This paper investigated requirements for effective organizational AI SD by examining the 2PDT framework. The results contribute to AI phenomenon by developing the requirements for AI SD, in terms of people, process, data and technology. It contributes to theory by evaluating and developing AI requirements for effective AI systems. The examination of the framework and case study approach added valuable knowledge to the AI domain. In addition, we contributed to practice by identifying requirements that organizations should consider in achieving better AI SD outcomes."

Artificial intelligence (AI), manifested in the forms of technologies, systems, tools, and applications, has advanced rapidly, especially in recent years. It has permeated many aspects of human behavior and nearly all sectors of society, such as healthcare and education. In the context of early childhood education (ECE), AI has afforded valuable opportunities that directly and indirectly enhance children’s learning and development. While there are already two existing reviews of the literature on AI in ECE, they show either a lack of descriptive information concerning selected studies or inconsistencies between inclusion/exclusion criteria and selected studies, thereby raising concerns about their rigor. Representing a more methodologically rigorous effort and a significant contribution to the field of AI in ECE, this scoping study aimed to achieve three main goals: (1) “mapping” the global landscape of the current extent, range, and nature of relevant studies on the affordances of AI for use in ECE, (2) identifying potential research gaps, and (3) charting future research directions. Specifically, it addressed this overarching research question: What is the global landscape of the current state of knowledge concerning the affordances of AI for use in ECE? Specifically, the state of knowledge here refers to three aspects: (1) extent, (2) range, and (3) nature. First, regarding the extent aspect, the empirical knowledge was derived from 18 research articles in 11 countries and 16 peer-reviewed academic journals between 2005 and 2023, with 14 of these articles published in the past four years (2020–2023). Second, with respect to the range of study populations, it covered 15,081 children in early childhood (ages 2 to 8 years) across these 11 countries. Third, thematic analysis of these studies revealed four areas of AI affordances: (1) AI as tangible and intangible tools for interactive learning and information retrieval, (2) AI as technology for predicting/classifying children’s conditions, (3) AI as the object for learning by adapting to and personalizing children’s learning, and (4) AI as the subject for children's learning about it. Based on these findings, this scoping review identified three research gaps for future studies: (1) interviewing and/or surveying education stakeholders (parents, educators, policymakers) to explore the affordances of appropriate AI for use with, by, and for children bearing ethical considerations; (2) conducting group comparisons to investigate contextual factors contributing to the “AI divide” among children from different socioeconomic backgrounds; and (3) comparing sociocultural influences on AI use in ECE across cultures.

Every day Artificial Intelligence (AI) technologies are more deeply inte-grated into education in general and into the process of a foreign language teaching in particular. Methodologists are developing language-specific methods of teaching lin-guistic university students foreign language aspects, types of speech activities, culture and translation based on specific AI tools. Most of these studies have a narrow focus and are aimed at solving individual and sometimes discrete pedagogical tasks. Such integration of AI into the educational process allows us to create additional conditions for extracurricular language practice for students and contributes to the further devel-opment of their foreign language communicative competence in all the diversity of its components. At the same time, the majority of methodological studies have a narrow focus and are oriented towards solving individual and sometimes discrete educational problems related to the development of specific speech skills or the formation of lexical, grammatical or phonetic speech skills of students. At the present stage, the language teaching potential of AI technologies allows to systematically consider the issue of in-tegrating AI into the process of language and methodological pre-service teachers’ training, which has not yet been the subject of a separate study. The aim of the paper is to develop a structural model for the integration of AI technologies in language and methodological pre-service teachers’ training. The model proposed by the authors for the integration of AI technologies in lan-guage and methodological pre-service teachers’ training consists of five blocks: lin-guistic disciplines, narrow professional disciplines, methodological disciplines, peda-gogical practice and research work. Within each block there are sub-blocks aimed at solving specific pedagogical/research tasks. The authors show which AI tools and tasks can be used in the context of teaching the disciplines from each block. The methodo-logical content of the blocks is based on the typology of feedback from generative AI as applied to teaching a foreign language, including educational-social, information-reference, methodological, analytical, evaluative and conditionally creative types. The methodological content of the blocks is based on the typology of feedback from generative AI in relation to foreign language teaching. The research is based on systematic and expert approaches using theoretical (comparative, component and complex analysis of AI tools, classification, synthesis, generalisation, modelling) and empirical (observa-tions and interviews with teachers and students) research methods. The research materials included scientific articles (Article and Review) published in academic journals indexed in the Scopus and Web of Science databases (Q1 and Q2), as well as Russian academic journals from the list of the Higher Attestation Commission of the Russian Federation on pedagogical sciences (С1 and С2). The subject of the research in the works used was the development of practical methods for teaching a foreign language (types of speech activity and aspects of language) using specific artificial intelligence tools.

The aim of the paper is to explore the relationship between skills shortages and the adoption of artificial intelligence (AI) in organizational contexts. While existing literature often considers AI as a substitute for labor, this study addresses a less examined perspective – AI adoption as a strategic response to persistent difficulties in recruiting highly educated and skilled workers. Drawing on theoretical frameworks from human capital theory and labor economics, and using empirical data from small and medium-sized enterprises (SMEs) across 34 developed countries, the analysis demonstrates that labor shortages – particularly at the bachelor’s and master’s education levels – significantly increase the probability of AI implementation. The paper shows that AI is not merely a labor-saving technology, but a means of maintaining competitiveness and scalability under conditions of human capital constraints. However, the adoption of AI is not uniform: it depends on internal readiness and sectoral dynamics, pointing to a paradox: while AI can mitigate talent shortages, it also introduces new demands for technical capabilities. These insights suggest that addressing the talent gap requires integrated strategies across organizational design, workforce development, and public policy.

This study investigates the practical applications of artificial intelligence (AI) in managerial work, with a focus on how tools such as large language models (LLMs) support the personal and professional development of managers. It explores how AI is used for decision-making, information analysis, and communication, and assesses its perceived impact on managerial knowledge, skills, and productivity. The research combines a comprehensive literature review with empirical data from a structured survey of 42 managers in Moravian companies, primarily in the Czech Republic. The study evaluates the frequency, purpose, and tools used in AI-assisted management practice. AI tools are frequently used by managers, especially for information summarization, meeting preparation, and text generation. ChatGPT (GPT-4) is the dominant tool, with most respondents reporting weekly or daily use. The majority perceive AI as beneficial, with an average Likert score of 7.86 regarding its positive impact on performance. Limitations include the localized sample, rapid research timeframe, and fast-paced AI evolution, which may affect long-term generalizability. The study offers practical insights into how AI enhances managerial routines and leadership development. This paper provides rare, localized empirical evidence on AI adoption in management, contributing to a fast-growing global research area.

Machine Learning is a modern and actively developing field of computer science, devoted to extracting and estimating dependencies from empirical data. It combines such fields as statistics, optimization theory and artificial intelligence. In practical tasks, the general aim of Machine Learning is to construct algorithms able to generalize and predict in previously unseen situations based on some set of examples. Given some finite information, Machine Learning provides ways to exract knowledge, describe, explain and predict from data. Kernel Methods are one of the most successful branches of Machine Learning. They allow applying linear algorithms with well-founded properties such as generalization ability, to non-linear real-life problems. Support Vector Machine is a well-known example of a kernel method, which has found a wide range of applications in data analysis nowadays. In many practical applications, some additional prior knowledge is often available. This can be the knowledge about the data domain, invariant transformations, inner geometrical structures in data, some properties of the underlying process, etc. If used smartly, this information can provide significant improvement to any data processing algorithm. Thus, it is important to develop methods for incorporating prior knowledge into data-dependent models. The main objective of this thesis is to investigate approaches towards learning with kernel methods using prior knowledge. Invariant learning with kernel methods is considered in more details. In the first part of the thesis, kernels are developed which incorporate prior knowledge on invariant transformations. They apply when the desired transformation produce an object around every example, assuming that all points in the given object share the same class. Different types of objects, including hard geometrical objects and distributions are considered. These kernels were then applied for images classification with Support Vector Machines. Next, algorithms which specifically include prior knowledge are considered. An algorithm which linearly classifies distributions by their domain was developed. It is constructed such that it allows to apply kernels to solve non-linear tasks. Thus, it combines the discriminative power of support vector machines and the well-developed framework of generative models. It can be applied to a number of real-life tasks which include data represented as distributions. In the last part of the thesis, the use of unlabelled data as a source of prior knowledge is considered. The technique of modelling the unlabelled data with a graph is taken as a baseline from semi-supervised manifold learning. For classification problems, we use this apporach for building graph models of invariant manifolds. For regression problems, we use unlabelled data to take into account the inner geometry of the input space. To conclude, in this thesis we developed a number of approaches for incorporating some prior knowledge into kernel methods. We proposed invariant kernels for existing algorithms, developed new algorithms and adapted a technique taken from semi-supervised learning for invariant learning. In all these cases, links with related state-of-the-art approaches were investigated. Several illustrative experiments were carried out on real data on optical character recognition, face image classification, brain-computer interfaces, and a number of benchmark and synthetic datasets.

This article explores how artificial intelligence (AI) is reshaping the transformation of scientific knowledge into school knowledge, a process traditionally conceptualized through the lens of didactic transposition theory. Drawing from curriculum theory, philosophy and sociology of education, and educational technology, the study examines two key dimensions: the use of AI in curriculum development and textbook authorship, and the role of algorithmic systems in selecting and structuring pedagogically relevant content. A conceptual model, “ReKnow-AI”, is proposed to illustrate how AI can be integrated into all stages of knowledge mediation, from scientific production to classroom practice. The study employs a theoretical and conceptual methodology, grounded in interdisciplinary content analysis. It proceeds through a systematic literature review and comparative synthesis of over 60 international sources published between 2020 and 2025, covering educational research, digital pedagogy, and AI-driven instructional design. No empirical data or statistical tools were employed; instead, conceptual modelling was used to identify epistemic shifts in curriculum-making under AI mediation. Findings highlight pedagogical opportunities such as content personalization, dynamic updating, and real-time instructional support. However, they also underline risks associated with algorithmic bias, lack of transparency, and diminished teaching agency. The proposed “ReKnow-AI” model identifies the critical junctures where AI systems influence educational content and advocates for ethical, human-centered oversight. This research contributes a novel perspective by framing AI not merely as a technological aid but as an epistemic actor in curriculum construction. Its practical significance lies in informing curriculum designers, policymakers, and teacher educators about the structural and normative implications of AI-based content mediation in contemporary education.

The tourism sector, which is an important contributor to global economic development, in Kolkata, a vibrant city full of cultural vibrancy, has undergone a deep transformation with the advent of artificial intelligence (AI). This paper discusses the impact of AI on the tourism industry in Kolkata using the strategic management perspective. The research, based on empirical data and a comprehensive literature review, explores how AI technologies are changing consumer experiences and operations in the tourism business, such as planning, bookings, and experiencing travel by tourists in Kolkata’s tourism industry. In this context, findings will be presented to show that AI improves operational efficiency, customer satisfaction, and safety in the post-pandemic era, making it an important tool for sustainable tourism development. However, the integration has its challenges, such as workforce displacement and technology that needs to be sustained. More importantly, AI has a double role in developing community-based tourism enterprises (CBTEs) and large-scale tourism projects in order to balance growth with social equity and to maximize resource use and operations costs. The study contributes to the ongoing debate on AI adoption in tourism and provides strategic insights for policymakers and industry stakeholders in Kolkata to leverage the potential of AI.

This article examines the role of the World Trade Organization’s (WTO) Agreement on Technical Barriers to Trade (TBT Agreement) in addressing the regulatory challenges arising from the proliferation of artificial intelligence (AI) regulations. The objective is to assess how the TBT Agreement can facilitate international cooperation, enhance transparency, and mitigate regulatory fragmentation in the context of a “multilayered AI governance”. The methodology consists of a doctrinal analysis of the TBT Agreement’s provisions – particularly those related to transparency, notification, and the use of international standards – supplemented by case studies of recent AI regulatory measures, such as the European Union (EU)’s AI Act, and their treatment within the WTO system. The article also draws on WTO jurisprudence and empirical data on notifications and Specific Trade Concerns (STCs) to evaluate the practical application of the TBT framework to AI-related measures. The analysis demonstrates that the TBT Agreement’s mechanisms – especially the notification process and the encouragement of the use of international standards – are increasingly relevant for AI regulation. The TBT Committee has emerged as a unique forum for the preemptive discussion and peer review of national AI regulations, enabling Members to identify and address potential trade concerns before they escalate. In conclusion, the article argues that the TBT Agreement provides valuable tools for improving AI governance and reducing regulatory fragmentation, but also underscores the need for further reforms to ensure its continued effectiveness in the context of emerging digital technologies.

BACKGROUND: Artificial intelligence (AI) is increasingly used in healthcare research for data analysis and prediction. However, its rapid adoption raises ethical concerns, particularly in nursing, related to patient-centered care, fairness, and trust. AIM: This evidence-based practice (EBP) project systematically reviewed ethical guidelines concerning AI use in nursing research and developed recommendations specifically tailored to the Saudi context. METHODOLOGY: The Johns Hopkins Nursing EBP model informed the formulation of a Population, Intervention, Comparison, and Outcome question and guided the literature search, conducted between January 2025 and April 2025. PubMed, CINAHL, and EBSCOhost databases were searched for English-language, peer-reviewed studies published between 2020 and 2025 that addressed the ethical considerations of AI in healthcare research. Twelve articles fulfilled the inclusion criteria, encompassing systematic and scoping reviews, scholarly analyses, a comparative study, and policy or guideline documents. FINDINGS: Twelve studies revealed five recurring ethical domains in AI-related nursing research: accountability, transparency, fairness, data privacy, and human oversight. While global frameworks emphasize these principles, most evidence is based on expert consensus rather than empirical data. In Saudi Arabia, despite strong national AI governance through Saudi data and Artificial Intelligence Authority (SDAIA), nursing-specific ethical guidance and implementation remain limited, highlighting the need for localized frameworks and nurse-led research. RECOMMENDATIONS: Nursing researchers should integrate robust data protection, transparency, and informed consent processes into all AI-enabled studies to ensure the integrity of research. Institutions should align nursing research governance with SDAIA and Vision 2030 ethical standards while developing nursing-specific frameworks and training on AI ethics. Ongoing capacity-building, mentorship, and nurse-led empirical research are essential to operationalize these principles and ensure the responsible and culturally relevant adoption of AI in Saudi nursing research. CONCLUSION: Current ethical guidance on AI in nursing research relies primarily on expert consensus and international frameworks, with minimal empirical validation. Locally relevant guidelines that incorporate SDAIA regulations into nursing protocols are needed to support the ethical adoption of AI. Empirical studies led by nurses are crucial for operationalizing these principles.

The fact that Artificial Intelligence (AI) has quickly been integrated into the information systems of enterprises has reinvented the decision-making process and allowed organizations to transition to predictive strategies rather than reactive ones. The current paper explores how AI-based predictive analytics can change the quality of decisions, speed, and performance of modern companies. Based on multi-industry data sets and recorded enterprise benchmarks, the study employs a quasi-experimental design based on difference-in-differences estimation and panel regression models to eliminate the causal influence of AI adoption. Quantitative data indicate that the organizations that use AI-enabled predictive systems experience, on average, 17-25% decrease in decision latency, 20% increase in the accuracy of forecasts, and 9-14% increase of the operational key performance indicators (KPIs) like cost efficiency and timeliness. Such effects are enhanced in companies where information-system maturity is high and strong data-governance systems are in place. The findings indicate that AIs value is not only achieved by implementing algorithmic sophistication, but also by integrating systems as part of enterprise architectures and decision processes. The article also develops a governance-risk framework of sustainable AI implementation with a focus on explainability, auditability, and human supervision. Altogether, this study adds to the intersection of AI and Information Systems as it provides empirical data on how predictive analytics facilitates the process of managerial decision-making and develops an operational model that businesses may adopt to achieve the best ROI on AI investments. The research ends with the strategic suggestions on merging AI maturity with organizational readiness to provide visibility, data-driven, and ethically controlled decision ecosystems.

A new era of enhanced user experience and operational efficiency across multiple domains is being ushered in by the widespread use of artificial intelligence (AI) personal assistants [1].Though the one-size-fits-all approach might work in some situations, industries with specific needs like healthcare, finance, and retail require customization [2].In order to increase the utility and efficacy of AI personal assistants in enterprise mobile applications, this study explores the idea of tailoring them to the intricate needs of various industries.In order to enable the assistants to understand and become acquainted with the terminologies and jargon particular to each sector, we investigate the underlying technology that permits the embedding of industryspecific knowledge bases and terminologies into the learning algorithms [3].We examine how personalized AI applications, by providing more precise, accurate, and contextual support, can dramatically improve operational efficiency, lower errors, and increase user satisfaction through a thorough and in-depth review [4], [5].Through case studies and empirical data from the healthcare, finance, and retail industries, the practical applicability of this customization is assessed, illuminating the transformative potential of industry-specific AI personal assistants in streamlining workflows, enhancing decision-making, and producing a more immersive and intuitive user experience [6,7,8].The study makes use of recent developments in knowledge representation and natural language processing [3],[4], and [5], as well as foundational works in the field of artificial intelligence personal assistants [1], [2].Additionally, it integrates best practices and empirical results from industry-specific deployments [6,7,8], offering a comprehensive view of the opportunities, difficulties, and real-world applications of modifying AI assistants for operational excellence.

In the conditions of digitalization, which has penetrated all spheres of human life and society, artificial intelligence (AI) and ultra-precise neural networks influence our thinking and form consumer demand and loyalty. Modern AI applications cover all the essential areas of activity: education, manufacturing, marketing, management, law, medicine, and e-commerce. The study aims to build a methodology for analyzing the influence of public opinion in social networks, particularly micro and nano influencers, through AI and machine learning on the effectiveness of companies' marketing activities. The authors studied the stages of consumer interaction with marketing profits and highlighted the role of AI technologies in each of them. They showed that public opinion was the main driving force for effective sales of products on the Internet. Based on empirical data, a strong direct relationship was found between the number of influencers and the dynamics of marketing profit: it was revealed that the correlation coefficients between the marketing profit index and the number of micro and nano influencers were 0.86–0.99 in all surveyed companies implementing AI technologies, regardless of the market niche. Based on the expert method, the company's marketing system is investigated. An integral indicator of its effectiveness is introduced, consisting of sub-indices of analysis, diffusion and marketing information management. The authors considered the factors of profit from marketing activities of five companies actively implementing AI in brand promotion, including those using the leadership opinions of influencers. The applied value of the developed methodology is the possibility of using it to construct Internet marketing strategies by companies. By permanently monitoring the relationship between the marketing profit index and the degree of brand popularization in social networks, companies can manage sales at all stages of consumer communications, optimizing the costs of influencers promoting products on different social platforms. The prospects for further research are to study the relationship between the degree of interactivity of the influencer with the audience, the content posted by it with the conversion rate and sales volumes; grouping and identification of influencers based on the clustering method.

In the last few years, business firms have substantially invested into the artificial intelligence (AI) technology. However, according to several studies, a significant percentage of AI projects fail or do not deliver business value. Due to the specific characteristics of AI projects, the existing body of knowledge about success and failure of information systems (IS) projects in general may not be transferrable to the context of AI. Therefore, the objective of our research has been to identify factors that can lead to AI project failure. Based on interviews with AI experts, this article identifies and discusses 12 factors that can lead to project failure. The factors can be further classified into five categories: unrealistic expectations, use case related issues, organizational constraints, lack of key resources, and technological issues. This research contributes to knowledge by providing new empirical data and synthesizing the results with related findings from prior studies. Our results have important managerial implications for firms that aim to adopt AI by helping the organizations to anticipate and actively manage risks in order to increase the chances of project success.

The data-driven insights, predictive analytics, and automation capabilities of Artificial intelligence (AI) have made it a transformative factor in corporate strategic decision-making. This research examines how AI affects strategic decision-making processes in companies, as AI-powered tools can cut costs and driving uncertainty, increasing efficiency and ensuring a competitive advantage. Through case study analysis and empirical data across multiple industries, this research delineates crucial factors that govern AI adoption, such as data accessibility, corporate culture, and legal compliance. The study also provides analysis of accompanying challenges, including those surrounding ethics, algorithmic biases, and the role of human-AI collaboration. The results imply that although artificial intelligence greatly improves both the speed of decision-making and the accuracy of decisions, the effectiveness of AI is required balance, expertise, and ethical governance. This research adds to the growing discussion around using AI to inform corporate strategy, which is crucial for practice to advance and helpful to both business leaders and policymakers.