Redesigning Multiliteracies: What Does It Mean to Design Social Futures in Today's Racialized, Transnational, and Digitized Lifeworlds?

In this supplement to the reprint of “A Pedagogy of Multiliteracies: Designing Social Futures,” Mary Kalantzis and Bill Cope revisit the foundational ideas of the New London Group thirty years after the article's publication. They explore how the multiliteracies framework has evolved over time in response to changes in technology, media, and education. From the emergence of social media to the many possibilities of artificial intelligence, they examine how these developments have deepened the relevance of multiliteracies in fostering educational justice. Central to their multiliteracies framework is the concept of design, which they reframe as an essential practice for navigating and reshaping an increasingly digital and interconnected world. Kalantzis and Cope also reflect on their ongoing work in AI-assisted pedagogy, highlighting its potential to expand and transform learning in the twenty-first century.

AI telemedicine screening in ophthalmology: health economic considerations

Technology, education and Arab youth in the 21st century: A study of the UAE

This research aims to identify the role of internal auditors, accountability, transparency, and how technology can be used to improve the effectiveness, benefits and risks of internal audits. The extent to which technology changes, artificial intelligence, and the tools offered by these technologies will impact the future of internal audit. This research method uses qualitative data collection methods, using literature observations carried out by researchers and practitioners. This research was carried out using the LR (Literature Review) method, which was carried out in several stages: First, the author researched journals using keywords such as 'Digital Technology', 'Artificial Intelligence', 'Internet of Things'. “Internal Control” and “Internal Audit” The author has conducted research using electronic databases such as Google Scholar, Semantic Scholar, Scopus, Publish Or Perish, Scispace and many other websites. This research is an important tool that provides information about technological changes and technical intelligence that are critical to the future of the audit profession and offers various suggestions for improving the efficiency and effectiveness of audit professionals' work. Therefore, this study suggests future research to investigate changes and increase knowledge.

The transformation of communication processes characterizes modern society due to the expansion of digital and internet technologies, especially in the form of Web 2.0, the development of the post-information society, and post-culture. The communication media space increasingly incorporates networks, digital technologies alter the public sphere as a shared communication platform, the presence of digital identities grows, filter bubbles and echo chambers emerge, and phenomena such as cancel culture, post-truth, and post-journalism rise. Communicative transformations are primarily linked to the impact of new digital media – online media and artificial intelligence, raising questions about how new convergent technologies change the public sphere and, consequently, communicative strategies. It is essential to address a series of questions, including how online communication influences offline communication and how the unconscious and symbolic actor – the digital algorithm AI – shapes specific features of social interactions. The negative impact of artificial intelligence, the spread of pseudoscientific knowledge, and the difficulty of their verification also remain significant factors. The article addresses the emergence of a new social actor – artificial intelligence – and the issue of digital identities that may not fully coincide with a real person or only partially coincide. The purpose of the article is to outline important communicative transformations caused by the development of modern digital media. The implementation of this purpose involves performing the following tasks: describing the main methodological approaches for analyzing the problem of communicative transformations, justifying their dependence on new digital media, and defining general communicative strategies through the emergence of new hybrid digital media. The article concludes that the development of digital technologies, especially AI, leads to communicative transformations in the public sphere, particularly through the formation of echo chambers, digital identities, virtualization, gamification, and blitz communication, shaping the processes of contemporary cultural changes.

The Institutional Logic of Digitalization

Artificial intelligence speakers, artificial intelligence secretaries, and artificial intelligence translations are all now naturally incorporated into many people's daily lives. Artificial intelligence, which is used in various fields from simply increasing the convenience of work daily to creating artwork using artificial intelligence technology, is being actively studied in relation to the human emotional field. This study will examine the process in which artificial intelligence newly stimulates human emotions by learning emotions. going beyond just recognizing human emotions. Also, the study will examine the definition, technology, and cases of artificial emotional intelligence that can be used for emotional control or decision-making based on emotional information recognized and learned by artificial intelligence. The study has meaning in suggesting what elements are needed for the industrialization of artificial emotional intelligence.

This dialogue is from an early scene in the 2014 film Ex Machina, in which Nathan has invited Caleb to determine whether Nathan has succeeded in creating artificial intelligence.1 The achievement of powerful artificial general intelligence has long held a grip on our imagination not only for its exciting as well as worrisome possibilities, but also for its suggestion of a new, uncharted era for humanity. In opening his 2021 BBC Reith Lectures, titled "Living with Artificial Intelligence," Stuart Russell states that "the eventual emergence of general-purpose artificial intelligence [will be] the biggest event in human history."2Over the last decade, a rapid succession of impressive results has brought wider public attention to the possibilities of powerful artificial intelligence. In machine vision, researchers demonstrated systems that could recognize objects as well as, if not better than, humans in some situations. Then came the games. Complex games of strategy have long been associated with superior intelligence, and so when AI systems beat the best human players at chess, Atari games, Go, shogi, StarCraft, and Dota, the world took notice. It was not just that Als beat humans (although that was astounding when it first happened), but the escalating progression of how they did it: initially by learning from expert human play, then from self-play, then by teaching themselves the principles of the games from the ground up, eventually yielding single systems that could learn, play, and win at several structurally different games, hinting at the possibility of generally intelligent systems.3Speech recognition and natural language processing have also seen rapid and headline-grabbing advances. Most impressive has been the emergence recently of large language models capable of generating human-like outputs. Progress in language is of particular significance given the role language has always played in human notions of intelligence, reasoning, and understanding. While the advances mentioned thus far may seem abstract, those in driverless cars and robots have been more tangible given their embodied and often biomorphic forms. Demonstrations of such embodied systems exhibiting increasingly complex and autonomous behaviors in our physical world have captured public attention.Also in the headlines have been results in various branches of science in which AI and its related techniques have been used as tools to advance research from materials and environmental sciences to high energy physics and astronomy.4 A few highlights, such as the spectacular results on the fifty-year-old protein-folding problem by AlphaFold, suggest the possibility that AI could soon help tackle science's hardest problems, such as in health and the life sciences.5While the headlines tend to feature results and demonstrations of a future to come, AI and its associated technologies are already here and pervade our daily lives more than many realize. Examples include recommendation systems, search, language translators - now covering more than one hundred languages - facial recognition, speech to text (and back), digital assistants, chatbots for customer service, fraud detection, decision support systems, energy management systems, and tools for scientific research, to name a few. In all these examples and others, AI-related techniques have become components of other software and hardware systems as methods for learning from and incorporating messy real-world inputs into inferences, predictions, and, in some cases, actions. As director of the Future of Humanity Institute at the University of Oxford, Nick Bostrom noted back in 2006, "A lot of cutting-edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore."6As the scope, use, and usefulness of these systems have grown for individual users, researchers in various fields, companies and other types of organizations, and governments, so too have concerns when the systems have not worked well (such as bias in facial recognition systems), or have been misused (as in deepfakes), or have resulted in harms to some (in predicting crime, for example), or have been associated with accidents (such as fatalities from self-driving cars).7Dædalus last devoted a volume to the topic of artificial intelligence in 1988, with contributions from several of the founders of the field, among others. Much of that issue was concerned with questions of whether research in AI was making progress, of whether AI was at a turning point, and of its foundations, mathematical, technical, and philosophical-with much disagreement. However, in that volume there was also a recognition, or perhaps a rediscovery, of an alternative path toward AI - the connectionist learning approach and the notion of neural nets-and a burgeoning optimism for this approach's potential. Since the 1960s, the learning approach had been relegated to the fringes in favor of the symbolic formalism for representing the world, our knowledge of it, and how machines can reason about it. Yet no essay captured some of the mood at the time better than Hilary Putnam's "Much Ado About Not Very Much." Putnam questioned the Dædalus issue itself: "Why a whole issue of Dædalus? Why don't we wait until AI achieves something and then have an issue?" He concluded:This volume of Dædalus is indeed the first since 1988 to be devoted to artificial intelligence. This volume does not rehash the same debates; much else has happened since, mostly as a result of the success of the machine learning approach that was being rediscovered and reimagined, as discussed in the 1988 volume. This issue aims to capture where we are in AI's development and how its growing uses impact society. The themes and concerns herein are colored by my own involvement with AI. Besides the television, films, and books that I grew up with, my interest in AI began in earnest in 1989 when, as an undergraduate at the University of Zimbabwe, I undertook a research project to model and train a neural network.9 I went on to do research on AI and robotics at Oxford. Over the years, I have been involved with researchers in academia and labs developing AI systems, studying AI's impact on the economy, tracking AI's progress, and working with others in business, policy, and labor grappling with its opportunities and challenges for society.10The authors of the twenty-five essays in this volume range from AI scientists and technologists at the frontier of many of AI's developments to social scientists at the forefront of analyzing AI's impacts on society. The volume is organized into ten sections. Half of the sections are focused on AI's development, the other half on its intersections with various aspects of society. In addition to the diversity in their topics, expertise, and vantage points, the authors bring a range of views on the possibilities, benefits, and concerns for society. I am grateful to the authors for accepting my invitation to write these essays.Before proceeding further, it may be useful to say what we mean by artificial intelligence. The headlines and increasing pervasiveness of AI and its associated technologies have led to some conflation and confusion about what exactly counts as AI. This has not been helped by the current trend-among researchers in science and the humanities, startups, established companies, and even governments-to associate anything involving not only machine learning, but data science, algorithms, robots, and automation of all sorts with AI. This could simply reflect the hype now associated with AI, but it could also be an acknowledgment of the success of the current wave of AI and its related techniques and their wide-ranging use and usefulness. I think both are true; but it has not always been like this. In the period now referred to as the AI winter, during which progress in AI did not live up to expectations, there was a reticence to associate most of what we now call AI with AI.Two types of definitions are typically given for AI. The first are those that suggest that it is the ability to artificially do what intelligent beings, usually human, can do. For example, artificial intelligence is:The human abilities invoked in such definitions include visual perception, speech recognition, the capacity to reason, solve problems, discover meaning, generalize, and learn from experience. Definitions of this type are considered by some to be limiting in their human-centricity as to what counts as intelligence and in the benchmarks for success they set for the development of AI (more on this later). The second type of definitions try to be free of human-centricity and define an intelligent agent or system, whatever its origin, makeup, or method, as:This type of definition also suggests the pursuit of goals, which could be given to the system, self-generated, or learned.13 That both types of definitions are employed throughout this volume yields insights of its own.These definitional distinctions notwithstanding, the term AI, much to the chagrin of some in the field, has come to be what cognitive and computer scientist Marvin Minsky called a "suitcase word."14 It is packed variously, depending on who you ask, with approaches for achieving intelligence, including those based on logic, probability, information and control theory, neural networks, and various other learning, inference, and planning methods, as well as their instantiations in software, hardware, and, in the case of embodied intelligence, systems that can perceive, move, and manipulate objects.Three questions cut through the discussions in this volume: 1) Where are we in AI's development? 2) What opportunities and challenges does AI pose for society? 3) How much about AI is really about us?Notions of intelligent machines date all the way back to antiquity.15 Philosophers, too, among them Hobbes, Leibnitz, and Descartes, have been dreaming about AI for a long time; Daniel Dennett suggests that Descartes may have even anticipated the Turing Test.16 The idea of computation-based machine intelligence traces to Alan Turing's invention of the universal Turing machine in the 1930s, and to the ideas of several of his contemporaries in the mid-twentieth century. But the birth of artificial intelligence as we know it and the use of the term is generally attributed to the now famed Dartmouth summer workshop of 1956. The workshop was the result of a proposal for a two-month summer project by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon whereby "An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves."17In their respective contributions to this volume, "From So Simple a Beginning: Species of Artificial Intelligence" and "If We Succeed," and in different but complementary ways, Nigel Shadbolt and Stuart Russell chart the key ideas and developments in AI, its periods of excitement as well as the aforementioned AI winters. The current AI spring has been underway since the 1990s, with headline-grabbing breakthroughs appearing in rapid succession over the last ten years or so: a period that Jeffrey Dean describes in the title of his essay as a "golden decade," not only for the pace of AI development but also its use in a wide range of sectors of society, as well as areas of scientific research.18 This period is best characterized by the approach to achieve artificial intelligence through learning from experience, and by the success of neural networks, deep learning, and reinforcement learning, together with methods from probability theory, as ways for machines to learn.19A brief history may be useful here: In the 1950s, there were two dominant visions of how to achieve machine intelligence. One vision was to use computers to create a logic and symbolic representation of the world and our knowledge of it and, from there, create systems that could reason about the world, thus exhibiting intelligence akin to the mind. This vision was most espoused by Allen Newell and Hebert Simon, along with Marvin Minsky and others. Closely associated with it was the "heuristic search" approach that supposed intelligence was essentially a problem of exploring a space of possibilities for answers. The second vision was inspired by the brain, rather than the mind, and sought to achieve intelligence by learning. In what became known as the connectionist approach, units called perceptrons were connected in ways inspired by the connection of neurons in the brain. At the time, this approach was most associated with Frank Rosenblatt. While there was initial excitement about both visions, the first came to dominate, and did so for decades, with some successes, including so-called expert systems.Not only did this approach benefit from championing by its advocates and plentiful funding, it came with the suggested weight of a long intellectual tradition-exemplified by Descartes, Boole, Frege, Russell, and Church, among others-that sought to manipulate symbols and to formalize and axiomatize knowledge and reasoning. It was only in the late 1980s that interest began to grow again in the second vision, largely through the work of David Rumelhart, Geoffrey Hinton, James McClelland, and others. The history of these two visions and the associated philosophical ideas are discussed in Hubert Dreyfus and Stuart Dreyfus's 1988 Dædalus essay "Making a Mind Versus Modeling the Brain: Artificial Intelligence Back at a Branchpoint."20 Since then, the approach to intelligence based on learning, the use of statistical methods, back-propagation, and training (supervised and unsupervised) has come to characterize the current dominant approach.Kevin Scott, in his essay "I Do Not Think It Means What You Think It Means: Artificial Intelligence, Cognitive Work & Scale," reminds us of the work of Ray Solomonoff and others linking information and probability theory with the idea of machines that can not only learn, but compress and potentially generalize what they learn, and the emerging realization of this in the systems now being built and those to come. The success of the machine learning approach has benefited from the boon in the availability of data to train the algorithms thanks to the growth in the use of the Internet and other applications and services. In research, the data explosion has been the result of new scientific instruments and observation platforms and data-generating breakthroughs, for example, in astronomy and in genomics. Equally important has been the co-evolution of the software and hardware used, especially chip architectures better suited to the parallel computations involved in data- and compute-intensive neural networks and other machine learning approaches, as Dean discusses.Several authors delve into progress in key subfields of AI.21 In their essay, "Searching for Computer Vision North Stars," Fei-Fei Li and Ranjay Krishna chart developments in machine vision and the creation of standard data sets such as ImageNet that could be used for benchmarking performance. In their respective essays "Human Language Understanding & Reasoning" and "The Curious Case of Commonsense Intelligence," Chris Manning and Yejin Choi discuss different eras and ideas in natural language processing, including the recent emergence of large language models comprising hundreds of billions of parameters and that use transformer architectures and self-supervised learning on vast amounts of data.22 The resulting pretrained models are impressive in their capacity to take natural language prompts for which they have not been trained specifically and generate human-like outputs, not only in natural language, but also images, software code, and more, as Mira Murati discusses and illustrates in "Language & Coding Creativity." Some have started to refer to these large language models as foundational models in that once they are trained, they are adaptable to a wide range of tasks and outputs.23 But despite their unexpected performance, these large language models are still early in their development and have many shortcomings and limitations that are highlighted in this volume and elsewhere, including by some of their developers.24In "The Machines from Our Future," Daniela Rus discusses the progress in robotic systems, including advances in the underlying technologies, as well as in their integrated design that enables them to operate in the physical world. She highlights the limitations in the "industrial" approaches used thus far and suggests new ways of conceptualizing robots that draw on insights from biological systems. In robotics, as in AI more generally, there has always been a tension as to whether to copy or simply draw inspiration from how humans and other biological organisms achieve intelligent behavior. Elsewhere, AI researcher Demis Hassabis and colleagues have explored how neuroscience and AI learn from and inspire each other, although so far more in one than the other, as and have the success of the current approaches to AI, there are still many shortcomings and as well as problems in It is useful to on one such as when AI does not as or or or that can to or when it on or information about the world, or when it has such as of all of which can to a of public shortcomings have captured the attention of the wider public and as well as among there is an on AI and In recent years, there has been a of to principles and approaches to AI, as well as involving and such as the on AI, that to best important has been the of with to and - in the and developing AI in both and as has been well in recent This is an important in its own but also with to the of the resulting AI and, in its intersections with more the other there are limitations and problems associated with the that AI is not capable of if could to more more or more general AI. In their Turing deep learning and Geoffrey took of where deep learning and highlighted its current such as the with In the case of natural language processing, Manning and Choi the challenges in and despite the of large language Elsewhere, and have the notion that large language models do anything learning, or In & of in a and discuss the problems in systems, the as how to reason about other their systems, and well as challenges in both and especially when the include both humans and Elsewhere, and others a useful of the problems in there is a growing among many that we do not have for the of AI systems, especially as they become more capable and the of use although AI and its related techniques are to be powerful tools for research in science, as examples in this volume and recent examples in which AI not only help results but also by design and become what some have AI to science and and to and challenges for the possibility that more powerful AI could to new in science, as well as progress in some of challenges and has long been a key for many at the frontier of AI research to more capable the of each of AI, the of more general problems that to the possibility of more capable AI learning, reasoning, of and and of these and other problems that could to more capable systems the of whether current characterized by deep learning, the of and and more foundational and and reinforcement or whether different approaches are in such as cognitive agent approaches or or based on logic and probability theory, to name a few. whether and what of approaches be the AI is but many the current along with of and learning architectures have to their about the of the current approaches is associated with the of whether artificial general intelligence can be and if how and Artificial general intelligence is in to what is called that AI and for tasks and goals, such as The development of on the other aims for more powerful AI - at as powerful as is generally to problem or and, in some the capacity to and improve as well as set and its own and the of and when will be is a for most that its achievement have and as is often in and such as A through and The to Ex and it is or there is growing among many at the frontier of AI research that we for the possibility of powerful with to and and with humans, its and use, and the possibility that of could and that we these into how we approach the development of of the research and development, and in AI is of the AI and in its what Nigel Shadbolt the of AI. This is given the for useful and applications and the for in sectors of the However, a few have made the development of their the most of these are and each of which has demonstrated results of increasing still a long way from the most discussed impact of AI and automation is on and the future of This is not In in the of the excitement about AI and and concerns about their impact on a on and the was that such technologies were important for growth and and "the that but not Most recent of this including those I have been involved have and that over time, more are than are that it is the and the and the of will the In their essay AI & and John discuss these for work and further, in & the of & to discuss the with to and and as well as the opportunities that are especially in developing In "The Turing The & of Artificial Intelligence," discusses how the use of human benchmarks in the development of AI the of AI that rather than human He that the AI's development will take in this and resulting for will on the for companies, and a that the that more will be than too much from of the and does not far enough into the future and at what AI will be capable The for AI could from of that in the is and labor and ability to are and and until automation has mostly physical and but that AI will be on more cognitive and tasks based on and, if early examples are even tasks are not of the In other are now in the world machines that that learn and that their ability to do these is to a range of problems they can will be with the range to which the human has been This was and Allen Newell in that this time could be different usually two that new labor will in which will by other humans for their own even when machines may be capable of these as well as or even better than The other is that AI will create so much and all without the for human and the of will be to for when that will the that once the first time since his creation will be with his his to use his from how to the which science and interest will have for to live and and However, most researchers that we are not to a future in which the of will and that until then, there are other and that be in the labor now and in the such as and other and how humans work increasingly capable that and John and discuss in this are not the only of the by AI. Russell a of the potentially from artificial general intelligence, once a of or ten But even we to general-purpose AI, the opportunities for companies and, for the and growth as well as from AI and its related technologies are more than to pursuit and by companies and in the development, and use of AI. At the many the is it is generally that is a in AI, as by its growth in AI research, and as highlighted in several will have for companies and given the of such technologies as discussed by and others the may in the way of approaches to AI and (such as whether they are companies or as and have have the to to in AI. The role of AI in intelligence, systems, autonomous even and other of increasingly In &

These days it is hard to identify occupations or daily activities that have not been shaped by the use or non-use of digital technology. Identified as part of the digital revolution or the Industrial Revolution 4.0, digital technology has moved from expensive and specialised, to ubiquitous and widely accepted. This has important implications for occupational therapists (Liu, 2018). Keeping up with the contributions of, and needs related to, digital technology has seen new roles and responsibilities for occupational therapists. An emerging role, for example, is in the design and evaluation of mainstream technologies (Proffitt et al., 2019). Occupational therapists and users with disabilities work in companies creating gaming, rehabilitation and health-related digital technologies (Proffitt et al., 2019). There are calls for engaging occupational therapists in the creation of accessible options to promote working remotely, inclusive health and social networks, transportation systems and communities (e.g. Anzai et al., 2023; Field et al., 2021; Slegers et al., 2020). The role for ensuring we support people in adjusting to requirements to use technologies and as technologies change has also been noted (Liddle, Worthy, Frost, Taylor, Taylor, et al., 2022). The lack of consideration of all potential users in the development of current mainstream technologies and in datasets that fuel automated systems and artificial intelligence has led to concerns about technology-related exclusion (Langston, 2020; Proffitt et al., 2019). The human rights implications of accessibility and potential exclusion in emerging technologies are of international concern (Australian Human Rights Commission, 2021). Vitally, occupational therapists need to be aware of and consider the digital divide in their practice. The digital divide, captured locally by the Australian Digital Inclusion Index (Thomas et al., 2021), describes how technology availability, accessibility, support, cost and confidence are leading to the increasing exclusion of some groups within Australia. Against a trend of increasing digital inclusion, a substantial number of Australians remain highly excluded, with over one in four Australians experiencing digital exclusion. Those at risk of digital exclusion include people with lower incomes, those living in regional and remote areas, those with lower levels of education, older people and those living with a disability. While the pandemic has shifted many activities into digital spaces, its impact on digital inclusion was not experienced consistently across the population. Those people not successfully engaging with technology were further excluded from participation. People working to reduce the digital divide identify the need to particularly address digital skill building, digital health literacy and technology during disasters and crises, and community led ways of building skill and capacity (Good Things Foundation., 2022). In moving towards an inclusive future, it is essential that occupational therapists support the creation, adaptation and use of meaningful and accessible technologies. One important avenue towards this is through the codesign of technologies and associated services and supports. Partnering with technology users (or potential users) to create more accessible and acceptable options, and recognising the expertise of assistive technology users as a vital resource will support more inclusion (Layton et al., 2021; Liddle, Worthy, Frost, Taylor, & Taylor, 2022; Ong et al., 2023). Occupational therapists can develop and support participatory and inclusive ways of working with people and technologies. It is important that occupational therapists directly address the potential for technology to impact on occupational justice and health (Larsson-Lund & Nyman, 2020). This needs to include advocating for more inclusive technologies, ways of working, infrastructure, technology funding and supports.

Strategic priorities of social production digitalization: world experience

Introduction. In higher education, the issue of using digital technologies that have already proved their value in other areas of social life (big data, digital libraries, the Internet of Things, etc.), and those that have recently become publicly available and still are rarely used in the education system (but are sure to be used in the future) is gaining relevance nowadays. The latter include the artificial intelligence, with ChatGPT 4.0 neural network being one of its types. In Russia, the need to implement the artificial intelligence (AI) is directly stated in the Executive Order of the President of the Russian Federation No. 490 of October 10, 2019, where the National AI Development Strategy for the period until 2030 has been approved. The Russian Federation Government Directive No. 3759-r “On Approval of the Strategic Direction in the Field of Science and Higher Education Digital Transformation” of December 21, 2021 emphasizes that the goal of digital transformation is to achieve a high level of “digital maturity” in higher education organizations, scientific organizations and the responsible sectoral body of federal executive power. The above regulatory requirements induce the research on the prospects of AI technology implementation in educational institutions of all types, including law faculties. The objective of the research is to analyse the organisational, legal and ethical aspects of using the artificial intelligence and justify (on an example of ChatGPT) the advantages and threats of integrating the neural networks in the study process at law faculties of Russian universities.Materials and Methods. In the research, the commonly accepted in Russia methods of scientific cognition were used: the dialectical method, analysis, synthesis, historical case-specific method, logical, comparative legal, systemic methods, and others.Results. In the frame of the research, the theory and practice of using the artificial intelligence in educational process at law faculties have been analysed on an example of ChatGPT neural network. The need of embedding into the curriculum (e.g., at Master's level) the special academic courses that present the features of using the artificial intelligence in juridical practices has been justified. The advantages and threats of using the artificial intelligence, possible regulations for its use in the study process have been identified. Ethical issues of using the artificial intelligence have been investigated, and the experience of various Russian and foreign universities that accept its usage has been summarised.Discussion and Conclusion. Humanity is on the threshold of a new industrial revolution, therefore it is impossible to hinder the development of artificial intelligence any longer. Under the influence of ChatGPT, not only the concept of higher education and a list of teacher and student competencies will change in the nearest future, but also the very understanding of ethics, acceptability and parameters of using the artificial intelligence and other digital technologies. The ideology and philosophy of higher education will change, which will entail legal, methodological, technical and other consequences resulting in adoption of the new digital-reality-related amendments to the Russian Law on Education. The countries where a conservative approach to the new digital educational technologies prevails, will be lagging behind the advanced countries of the world. Formation of a new digital model of graduate’s competencies will happen against the background of other social life changes, including the use of the Internet of Things, unmanned aerial vehicles, robots, artificial intelligence, big data, nanotechnology and many other things. Although the majority of these innovations are not directly related to law, their use will require a legal framework, and lawyers competent in resolving digital disputes will be needed. This is precisely why digital technologies should be integrated into education and taught to students of law faculties.

The aim of the article is to identify theoretical and practical aspects of bridging the gap between design concept and construction by integrating artificial intelligence systems. Research methods are based on a combination of several scientific approaches that allow for an effective assessment of the potential for integrating artificial intelligence systems into the design and construction process. The im- portance of forming an integrated approach to design is proven, which involves a preliminary analysis of the relationship between design, architectural, engineering, economic and environmental components. The role of artificial intelligence as an intelligent navigator capable of assessing constructive feasibility, predicting financial costs and ensuring compliance with regulatory requirements at all stages of the project is substantiated. It is established that the fragmentation of coordination between participants in the architectural-construction process and the inconsistency of actions cause significant delays, overspending of resources and a decrease in the quality of the final result. The principles of complex integration and dynamic planning are described, which become the basis for effective interaction between specialists in different areas. The feasibility of transparent budgeting aimed at optimal use of funds and avoiding duplication of work is proven. Special attention is paid to methods of risk analysis and modelling of alternative scenarios, which allows you to quickly respond to external challenges and ensure flexibility of the architectural-construction process. It is established that automated performance control increases the accuracy of meeting deadlines and quality indicators, minimizing the gap between the initial design concept and actual construction. It is substantiated that the use of artificial intelligence contributes not only to the operational coordination of teams, but also to better exchange of information with investors and regulatory authorities, which increases transparency and trust in the construction industry as a whole. The scientific novelty of the article lies in the comprehensive approach to bridging the gap between design concepts and construction through the integration of artificial intelligence systems, which allows optimizing the design and implementation processes of construction projects. For the first time, the paper proposes a model for the use of artificial intelligence to ensure integrated management, enabling the synchronization of conceptual, technical, and economic aspects at all stages of construction. Conclusions. Thus, the article proves that the implementation of innovative solutions based on artificial intelligence systems is an effective tool for improving management decisions and sustainable development of the architectural and construction sector. Using neural networks as a basis for effective integration of the design concept with the real construction process provides an opportunity to reduce risks, reduce costs and improve the quality of constructed objects.

The emotional system of artificial intelligence (AI) is being increasingly woven into the emotional tapestry of human society, prompting widespread discourse on the essence, technological frontiers, and ethical ramifications of emotion. In this study, the potential and constraints of AI replicating and augmenting human emotions is investigated. According to philosophical, psychological, and ethical viewpoints, the fundamental traits, moral dilemmas, and societal repercussions of AI emotional systems are discussed. Evidence suggests that the emotional manifestations of AI can provide emotional support to humans, especially in addressing loneliness and resolving emotional quandaries; however, these systems are essentially instrumental and devoid of authentic emotional depth and experience. As AI emotional systems evolve, ethical and social challenges arise. Humanity must define the role and boundaries of AI in the emotional sphere to ensure that its applications align with societal values and do not harm human emotional structures or social principles. We posit that a symbiotic relationship between AI and human emotions is possible, endorsing the measured use of AI emotional systems via technical and social strategies to complement, not replace, human emotional encounters. Furthermore, we underscore the need for interdisciplinary collaboration, the setting of ethical standards, and the exploration of AI emotional boundaries to achieve this goal. Through a comprehensive analysis of AI emotional systems, we aim to provide guidance for their future trajectory and application, ensuring that AI advancements in the emotional domain enhance the well-being of human society. We examine how AI, by trying to surpass human emotions, illustrates a general human habit of giving away or discharging one’s emotions when one faces internal emotional problems. According to this study, the main issue of having AI handle emotions lies in people’s overreliance on machines for emotional assistance and support. Supported by Kantian ethics, Buddhist metaphysics, and Nietzschean existentialism, we review and analyse the boundaries of emotional AI. Kant’s deontological imperative warns against using emotions as simple tools; Buddhist ideas about impermanence and attachment highlight the illusion that machines can be made human; and Nietzsche’s criticism of self-transcendence reminds us not to impose human values on artificial beings. Although emotional AI is technologically advanced, we believe that these systems have little or no independence and are not independently responsible. Their use worries many experts because of the emotional outsourcing and increasing separation in relationships related to this use. Ultimately, we suggest principles for safe interactions between humans and AI, emphasizing ethical design, cultural needs, and emotional education to reduce further problems when integrating these AI systems with real emotions.

AI Applications in Health Sector: Use of Artificial Intelligence in Covid-19 Crisis and Impacts of Medical Robots on Global Economy

Digital technologies are fundamentally transforming the way businesses operate. Digital insurance holds significant growth potential and promising prospects for development. The insurance industry is on the cusp of significant technological changes. Therefore, the objectives outlined in this article are to define the essence of digital insurance and its advantages, characterize trends and development prospects, and describe the challenges and strategies for optimizing digital progress in insurance. Digital insurance entails the digitization of all insurance operations and replacing physical presence in insurance with online interactions. Digital insurance, known as InsurTech, combines traditional understanding of insurance with 21st-century technologies. Artificial intelligence (AI) can be used to analyze large volumes of data for better risk assessment and setting insurance premiums. Blockchain technologies can be used to create secure and transparent insurance contracts. Internet of Things (IoT) devices, such as smartwatches or vehicles, can provide real-time data to aid in risk assessment and insurance claim monitoring. Mobile applications and web platforms can provide convenient access to insurance services and informa- tion for customers. The most significant advantages of insurance digitalization will manifest in the following ways: Increased efficiency and productivity; Improved customer service ;Access to new markets; Proactive risk management; Reduction of fraud. Current trends in digital insurance include the utilization of Big Data, Artificial Intelligence (AI), and the Internet of Things (IoT) technologies. Digital insurance has significant growth potential. The prospects for the development of digital insurance in Ukraine include: Development of digital infrastructure; Development of the fintech industry; Legislative initiatives; Connected insurance. However, for successful development of digital insurance in Ukraine, it is necessary to overcome certain challenges, including: Data privacy issues; Cybersecurity; Regulation; Integration challenges; Change of corporate culture and skills. Three key strategies have been identified to optimize digital progress in the insurance sector: The importance of digital transformation for business success, yet the fear of fully unleashing its potential. Successful investments in digital self-service platforms and robotic process automation. Understanding the necessity for insurers to adopt a new approach to workforce reorganization that promotes genuine digital thinking within the organization. The main obstacle for insurers in becoming digital organizations is change management. Digitalization of the insurance industry provides significant opportunities for the development of this type of entrepreneurial activity, offering advantages for both insurance companies and clients.