З досвіду використання штучного інтелекту істориком філософії: галюцинації та булшит, креативність та адаптивність

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

Response to M. Trengove & coll regarding "Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine".

Introduction Author Arthur C. Clarke famously argued that in science fiction literature “any sufficiently advanced technology is indistinguishable from magic” (Clarke). On 30 November 2022, technology company OpenAI publicly released their Large Language Model (LLM)-based chatbot ChatGPT (Chat Generative Pre-Trained Transformer), and instantly it was hailed as world-changing. Initial media stories about ChatGPT highlighted the speed with which it generated new material as evidence that this tool might be both genuinely creative and actually intelligent, in both exciting and disturbing ways. Indeed, ChatGPT is part of a larger pool of Generative Artificial Intelligence (AI) tools that can very quickly generate seemingly novel outputs in a variety of media formats based on text prompts written by users. Yet, claims that AI has become sentient, or has even reached a recognisable level of general intelligence, remain in the realm of science fiction, for now at least (Leaver). That has not stopped technology companies, scientists, and others from suggesting that super-smart AI is just around the corner. Exemplifying this, the same people creating generative AI are also vocal signatories of public letters that ostensibly call for a temporary halt in AI development, but these letters are simultaneously feeding the myth that these tools are so powerful that they are the early form of imminent super-intelligent machines. For many people, the combination of AI technologies and media hype means generative AIs are basically magical insomuch as their workings seem impenetrable, and their existence could ostensibly change the world. This article explores how the hype around ChatGPT and generative AI was deployed across the first six months of 2023, and how these technologies were positioned as either utopian or dystopian, always seemingly magical, but never banal. We look at some initial responses to generative AI, ranging from schools in Australia to picket lines in Hollywood. We offer a critique of the utopian/dystopian binary positioning of generative AI, aligning with critics who rightly argue that focussing on these extremes displaces the more grounded and immediate challenges generative AI bring that need urgent answers. Finally, we loop back to the role of schools and educators in repositioning generative AI as something to be tested, examined, scrutinised, and played with both to ground understandings of generative AI, while also preparing today’s students for a future where these tools will be part of their work and cultural landscapes. Hype, Schools, and Hollywood In December 2022, one month after OpenAI launched ChatGPT, Elon Musk tweeted: “ChatGPT is scary good. We are not far from dangerously strong AI”. Musk’s post was retweeted 9400 times, liked 73 thousand times, and presumably seen by most of his 150 million Twitter followers. This type of engagement typified the early hype and language that surrounded the launch of ChatGPT, with reports that “crypto” had been replaced by generative AI as the “hot tech topic” and hopes that it would be “‘transformative’ for business” (Browne). By March 2023, global economic analysts at Goldman Sachs had released a report on the potentially transformative effects of generative AI, saying that it marked the “brink of a rapid acceleration in task automation that will drive labor cost savings and raise productivity” (Hatzius et al.). Further, they concluded that “its ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines reflects a major advancement with potentially large macroeconomic effects” (Hatzius et al.). Speculation about the potentially transformative power and reach of generative AI technology was reinforced by warnings that it could also lead to “significant disruption” of the labour market, and the potential automation of up to 300 million jobs, with associated job losses for humans (Hatzius et al.). In addition, there was widespread buzz that ChatGPT’s “rationalization process may evidence human-like cognition” (Browne), claims that were supported by the emergent language of ChatGPT. The technology was explained as being “trained” on a “corpus” of datasets, using a “neural network” capable of producing “natural language“” (Dsouza), positioning the technology as human-like, and more than ‘artificial’ intelligence. Incorrect responses or errors produced by the tech were termed “hallucinations”, akin to magical thinking, which OpenAI founder Sam Altman insisted wasn’t a word that he associated with sentience (Intelligencer staff). Indeed, Altman asserts that he rejects moves to “anthropomorphize” (Intelligencer staff) the technology; however, arguably the language, hype, and Altman’s well-publicised misgivings about ChatGPT have had the combined effect of shaping our understanding of this generative AI as alive, vast, fast-moving, and potentially lethal to humanity. Unsurprisingly, the hype around the transformative effects of ChatGPT and its ability to generate ‘human-like’ answers and sophisticated essay-style responses was matched by a concomitant panic throughout educational institutions. The beginning of the 2023 Australian school year was marked by schools and state education ministers meeting to discuss the emerging problem of ChatGPT in the education system (Hiatt). Every state in Australia, bar South Australia, banned the use of the technology in public schools, with a “national expert task force” formed to “guide” schools on how to navigate ChatGPT in the classroom (Hiatt). Globally, schools banned the technology amid fears that students could use it to generate convincing essay responses whose plagiarism would be undetectable with current software (Clarence-Smith). Some schools banned the technology citing concerns that it would have a “negative impact on student learning”, while others cited its “lack of reliable safeguards preventing these tools exposing students to potentially explicit and harmful content” (Cassidy). ChatGPT investor Musk famously tweeted, “It’s a new world. Goodbye homework!”, further fuelling the growing alarm about the freely available technology that could “churn out convincing essays which can't be detected by their existing anti-plagiarism software” (Clarence-Smith). Universities were reported to be moving towards more “in-person supervision and increased paper assessments” (SBS), rather than essay-style assessments, in a bid to out-manoeuvre ChatGPT’s plagiarism potential. Seven months on, concerns about the technology seem to have been dialled back, with educators more curious about the ways the technology can be integrated into the classroom to good effect (Liu et al.); however, the full implications and impacts of the generative AI are still emerging. In May 2023, the Writer’s Guild of America (WGA), the union representing screenwriters across the US creative industries, went on strike, and one of their core issues were “regulations on the use of artificial intelligence in writing” (Porter). Early in the negotiations, Chris Keyser, co-chair of the WGA’s negotiating committee, lamented that “no one knows exactly what AI’s going to be, but the fact that the companies won’t talk about it is the best indication we’ve had that we have a reason to fear it” (Grobar). At the same time, the Screen Actors’ Guild (SAG) warned that members were being asked to agree to contracts that stipulated that an actor’s voice could be re-used in future scenarios without that actor’s additional consent, potentially reducing actors to a dataset to be animated by generative AI technologies (Scheiber and Koblin). In a statement issued by SAG, they made their position clear that the creation or (re)animation of any digital likeness of any part of an actor must be recognised as labour and properly paid, also warning that any attempt to legislate around these rights should be strongly resisted (Screen Actors Guild). Unlike the more sensationalised hype, the WGA and SAG responses to generative AI are grounded in labour relations. These unions quite rightly fear the immediate future where human labour could be augmented, reclassified, and exploited by, and in the name of, algorithmic systems. Screenwriters, for example, might be hired at much lower pay rates to edit scripts first generated by ChatGPT, even if those editors would really be doing most of the creative work to turn something clichéd and predictable into something more appealing. Rather than a dystopian world where machines do all the work, the WGA and SAG protests railed against a world where workers would be paid less because executives could pretend generative AI was doing most of the work (Bender). The Open Letter and Promotion of AI Panic In an open letter that received enormous press and media uptake, many of the leading figures in AI called for a pause in AI development since “advanced AI could represent a profound change in the history of life on Earth”; they warned early 2023 had already seen “an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control” (Future of Life Institute). Further, the open letter signatories called on “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4”, arguing that “labs and independent experts should use this pause to jointly develop and implement a set of shared safety protocols for advanced AI design and development that are rigorously audited and overseen by independent outside experts” (Future of Life Institute). Notably, many of the signatories work for the very companies involved in the “out-of-control race”. Indeed, while this letter could be read as a moment of ethical clarity for the AI industry, a more cynical reading might just be that in warning that their AIs could effectively destroy the w

Purpose. This article aims to analyze the role of generative artificial intelligence (GenAI), specifically ChatGPT, in educational activities while addressing concerns regarding academic integrity. The study explores the ambiguous boundaries of GenAI’s involvement in coursework, its potential ethical and technological challenges, and the need for clear policies regulating its use in education. Methodology. This study employs a mixed-methods approach, combining bibliometric analysis, direct interaction with ChatGPT, and a survey of Ukrainian students. Findings. The findings of this study reveal several key insights into the use of GenAI, specifically ChatGPT, in educational settings and its impact on academic integrity. The findings underscore the need for educational institutions to develop and implement policies that regulate GenAI’s role in academic activities. While GenAI offers significant potential as a technological assistant, there are risks associated with its misuse, particularly concerning academic dishonesty and the erosion of academic standards. Originality. The study’s originality lies in the comprehensive analysis of the problem of integrating GenAI, in particular ChatGPT, into the educational process from the point of view of academic integrity. For the first time, a systematic view of the stages of user interaction with GenAI has been proposed, potential points of violation of academic integrity at each of these stages are identified, and a “white box” concept has been developed to describe the use of GenAI, which allows controlling input and output parameters, minimizing risks. In addition, the study contains empirical data obtained as a result of a large-scale survey of Ukrainian students on their attitude to the use of GenAI in education, the level of awareness of university policies regarding GenAI, and support for the use of GenAI provided that academic integrity is observed. This outcome allows identifying the gap between existing practices and the need to develop effective strategies for integrating GenAI into the educational process. Practical value. The practical value of the work lies in the fact that the study’s results can serve as the basis for the development of clear recommendations and policies on using GenAI in higher education institutions. The proposed “white box” model can be applied to create practical tools that will help students and teachers understand the potential risks and consequences of using GenAI and develop skills for responsible use of these technologies. The student survey results can be used to inform and ensure dialogue between stakeholders on the optimal ways of integrating GenAI into the educational space, taking into account ethical aspects and the need to maintain academic integrity.

Academic integrity is the most important requirement for scientific research. However, the legal regulation of relations ensuring the academic integrity in scientific and educational activities is fragmented and does not contain effective mechanisms for influencing the violator of academic integrity. This necessitates a doctrinal study of the category “academic plagiarism” and the development of areas for reforming the current legislation in this field. Therefore, the purpose of this study is to analyse the statutory regulation of academic integrity as a phenomenon, the concept of academic plagiarism, its differences from plagiarism in the context of copyright compliance, to identify the scope of subjects responsible for establishing the facts of violations of academic integrity and their powers in the field of responding to corresponding violations, procedures for bringing to justice in case of violation of academic integrity. The present study, based both on general (historical, comparative, logical, and system) and special (structural-functional, formal legal, sociological, statistical, etc.) methods analyses the prospects of statutory regulation of the relatively new concept in Ukraine, which is academic plagiarism, including the legislative norms concerning the establishment of the concept of academic integrity, types of violations of academic integrity, procedures for considering issues of possible violations of academic integrity, types of responsibility for violations of academic integrity and bodies that have the right to apply them, verifies their compliance with international standards. The paper analyses the practice of the National Agency for Quality Assurance of Higher Education of Ukraine both on the consideration of complaints about violations of academic integrity, and within the framework of accreditation of educational programmes. Attention is drawn to the contradictions of current legal provisions in the legislation of Ukraine in the field of academic integrity. Proposals to the current legislation are formulated to optimise the legal regulation of the issue of compliance with academic integrity. The authors express their opinion on the necessity of accumulating legal regulation of academic integrity within the framework of a single law “On Academic Integrity” to define higher education institutions and scientific institutions as the main subject of ensuring compliance with the principles of academic integrity, and the National Agency for Quality Assurance of Higher Education – mainly by the appellate instance regarding decisions of higher education institutions on violations of academic integrity; adjusting the list of violations of academic integrity and specifying the procedure for their establishment and stimulating higher education institutions to real and not formal compliance with the principles of academic integrity

This work-in-progress research paper discusses issues of academic integrity which have long been a concern of education researchers and academic institutions within all fields of study. Academic integrity (AI) violations can consist of a broad range of student behaviors that are considered dishonest, including but not limited to plagiarism, copying others’ assignments, and paying for others to complete their work. A plethora of researchers have attempted to identify what underlying factors lead students to commit AI violations, and have identified several potential factors, including a lack of self-control, students’ ethical views of AI, perceived opportunities to commit AI violations, involvement in extracurricular activities, and students’ social groups.Another topic that has in recent times become a focal point of education research is students’ sense of belonging within their field of study. Researchers have identified several factors that contribute to students feeling less welcome within higher education, particularly within Engineering and Computer Science. Students who feel a lower sense of belonging have been identified as being at higher risk of performing poorly with their studies and retention rates for these students are historically lower. Despite this, little research has been conducted to examine where issues with students’ sense of belonging and their incidences of AI violations overlap. In this study, we attempt to try and better understand this relationship between students’ sense of belonging and AI violations by attempting to answer the following question: Can students’ sense of belonging within their discipline influence their propensity to violate academic integrity? We take up a student centered, restorative position, and choose to understand the cognitive underpinnings behind students’ choices to violate AI, with the goal of identifying if student outreach and more inclusive practices within Engineering and Computer Science can be utilized to prevent instances of AI violations. To accomplish this, we are employing a qualitative interview-based study of first-year students studying Computer Science at a large public university in the northeastern United States. We plan to analyze transcript data collected during interviews using Grounded Theory and Narrative Analysis methodologies. Our goal with this study is to draw awareness to additional underlying causes behind students deciding to violate AI, with the hope that this research will encourage academic institutions to employ a more preventative approach to handling AI issues by ensuring all students feel welcome and included within their chosen field of study, thereby helping prevent AI violations before they happen.

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. 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This paper examines the impact of artificial intelligence (AI) agents on society, focusing on the creation of synthetic content and artificial realities. As AI agents become more advanced, the content they produce may be increasingly difficult to distinguish from human-creation, raising important ethical concerns. The study highlights how generative AI can shape perceptions and blur the boundaries between traditional, authentic content and realistic, synthetic content. It explores advancements in natural language processing and image recognition, which allow for the creation of highly convincing fake content. The paper also looks at the role of AI agents in communication and the consequent social implications. Among those implications, the paper highlights the importance of augmenting creativity rather than radically replacing it with AI agents as a tool to augment human creativity rather than replacing it. Finally, it discusses the concept of superintelligence and its potential to transform society toward post-authenticity considering balanced regulation and ethical considerations.

In the modern academic environment, innovations in education play a crucial role in transforming the educational process, which poses complex tasks for the scientific community to preserve academic integrity. Integrating digital technologies, such as distance learning platforms, artificial intelligence, and automated assessment and analysis systems, is fundamentally changing traditional approaches to learning and teaching. However, despite the advantages of digitization of the educational process, which contribute to increasing the accessibility of education and its individualization, there are threats to compliance with the rules and principles of academic integrity. Access to an unlimited number of resources, simplification of the process of searching and processing information, as well as the possibility of using artificial intelligence algorithms can lead to an increase in cases of academic fraud and plagiarism and a decrease in the quality of knowledge obtained by students in the process of obtaining higher education. In this context, there is a need for an in-depth scientific analysis of the impact of innovative technologies on academic integrity, the development of effective methods for preventing violations of ethical norms, and the implementation of complex approaches to ensuring high standards of education. While writing the scientific article, the meaning of academic integrity was determined as a critical element of the modern higher education system, which includes ethical norms and standards of behavior of participants in the educational process. Various aspects of academic integrity are analyzed, particularly the principles and values of academic integrity of students of higher education institutions. It has been studied that one of the critical components of the fight against violations is the implementation of strict sanctions against violators of academic integrity, and it is found that these measures contribute to the maintenance of high standards of scientific research and education, preventing the discrediting of the academic community. The positive possibilities of applying innovative technologies in the educational process and new forms of violations of academic integrity using artificial intelligence and machine learning technologies are analyzed. It was determined that to effectively combat violations of academic integrity, it is necessary to develop clear rules and policies regarding using innovative technologies in the educational process. Keywords: academic integrity, innovative technologies, institutions of higher education, quality of education, ethical norms, artificial intelligence, machine learning.

Purpose Customer loyalty in the hospitality sector represents a critical determinant of a business’s success and competitive advantage. This paper aims to review the conceptual foundations of customer loyalty, its significance and the strategic mechanisms through which it can be cultivated and measured. Specifically, going beyond traditional strategies, this paper attempts to explain the concept of customer loyalty in the era of new technologies, especially AI agents, underscore its criticality and outline effective strategies for its enhancement and retention. Design/methodology/approach This paper synthesizes existing customer loyalty literature and proposes a framework based on insights from business, psychology and computer science to help companies and policymakers anticipate the impact of artificial intelligence (AI) agents on customer loyalty and guide future research directions in this emerging domain. Findings Building on prior literature and key developments in the last decade, this paper advocates for embedding retention-centric loyalty strategies while incorporating the newest technology. A proposed framework highlights the strategic alignment of AI capabilities, specifically AI agents, with loyalty objectives, emphasizing the critical role of data-driven personalization in sustaining competitive advantage and deepening customer relationships. Research limitations/implications The findings are primarily derived from secondary data sources and theoretical models, suggesting a need for empirical testing in diverse hospitality settings. Future research could explore the impact of AI and AI agents on loyalty across different cultures and market segments. Practical implications Hospitality firms may need to adapt loyalty strategies to account for AI-mediated decision-making. This includes enhancing algorithmic visibility, reconfiguring loyalty programs to engage both customers and their digital agents and understanding how AI shapes perceptions of value, convenience and brand preference. Firms must consider whether loyalty is being directed toward the brand, the agent or the ecosystem in which both operate. Social implications The integration of AI agents into loyalty ecosystems may have broader social consequences, including the erosion of consumer autonomy, increased algorithmic bias and new forms of digital exclusion. These transformations raise questions about the ethics of automated loyalty systems, the transparency of decision delegation and the future role of human connection in service interactions. Originality/value This paper fills a gap in existing research by examining the integration of AI with customer loyalty strategies within the hospitality industry. It offers a new perspective on how AI and AI agents can be aligned with traditional loyalty frameworks to enhance customer engagement and relationship management. The insights presented contribute to a deeper understanding of the practical implications of AI in shaping future loyalty programs and provide a foundation for further academic exploration and practical application in the field.

Editorial

BackgroundThe prevalence of chronic gastritis is high, and if not intervened in a timely manner, it may eventually lead to gastric cancer. Managing chronic gastritis essentially requires comprehensive lifestyle changes. However, the current health care environment does not support continuous follow-up by professional health care providers, making self-management a key component of postdiagnosis care. Increasingly, researchers are exploring the use of large language models (LLMs) for patient management. However, LLMs have limitations, including hallucinations, limited knowledge scope, and lack of timeliness. Artificial intelligence (AI) agents may provide a more effective solution. Nevertheless, it remains uncertain whether AI agents can effectively support postdiagnosis self-management for patients with chronic gastritis.ObjectiveThe purpose of this study was to explore the effectiveness of AI agents in the postdiagnosis management of patients with chronic gastritis from different perspectives.MethodsIn this study, we developed an agent framework for the health management of patients with chronic gastritis based on LLMs in conjunction with retrieval-augmented generation and a search engine tool. We collected real questions from patients with chronic gastritis in clinical settings and tested the framework’s performance across different difficulty levels and scenarios. We analyzed its safety and robustness and compared it with state-of-the-art models to comprehensively evaluate its effectiveness.ResultsUsing a dual-evaluation framework comprising automated metrics and expert manual assessments, our results demonstrated that AI agents substantially outperformed LLMs in addressing high-complexity questions (embedding average score: 82.849 for AI agents vs 77.825 for LLMs) and were particularly effective in clinical consultation tasks. Clinical evaluation of safety based on a 5-point Likert scale by physicians indicated that the safety of the agents was 4.98 (SD 0.15; 95% CI 4.96-4.99). After 30 repeated experiments, the mean absolute deviation of the AI agents in the embedding average score and BERTScore metrics were 0.0167 and 0.0387, respectively. Therefore, the safety and robustness analysis confirmed that the AI agents can produce safe, stable, and minimally variable responses. In addition, comparative results with those of advanced medical-domain LLMs (Baichuan-14B-M1 and MedGemma-27B) and general-domain LLMs (Qwen3-32B) also demonstrated that the AI agents in this study performed outstandingly in the field of chronic gastritis. Our findings underscore the superior reliability, interpretability, and practical applicability of AI agents over conventional LLMs in chronic gastritis management, offering a robust foundation for their broader adoption in health care settings.ConclusionsAI agents based on LLMs have high application value in the management of chronic gastritis. They can effectively guide patients with chronic diseases in addressing common issues, which may potentially reduce the workload of physicians and improve the quality of patient home care.

Purpose This study aims to critically examine the socio-technical, economic and governance challenges emerging at the intersection of Generative artificial intelligence (AI) and Urban AI. By foregrounding the metaphor of “the moon and the ghetto” (Nelson, 1977, 2011), the issue invites contributions that interrogate the gap between technological capability and institutional justice. The purpose is to foster a multidisciplinary dialogue–spanning applied economics, public policy, AI ethics and urban governance – that can inform trustworthy, inclusive and democratically grounded AI practices. Contributors are encouraged to explore not just what GenAI can do, but for whom, how and with what consequences. Design/methodology/approach This study draws upon interdisciplinary literature from public policy, innovation studies, digital governance and urban sociology to frame the emerging governance challenges of Generative AI and Urban AI. It builds a conceptual foundation by synthesizing insights from comparative city case studies, innovation systems theory and normative policy frameworks. The approach is interpretive and exploratory, aiming to situate AI technologies within broader institutional, geopolitical and socio-economic contexts. The study invites contributions that adopt empirical, theoretical or practice-based methodologies addressing the governance of GenAI in cities and regions. Findings This study identifies a critical gap between the rapid technological advancements in Generative AI and the institutional readiness of public governance systems – particularly in urban contexts. It finds that current policy frameworks often prioritize efficiency and innovationism over democratic legitimacy, civic trust and inclusive design. Drawing on comparative global city experiences, it highlights the risk of reinforcing power asymmetries without robust accountability mechanisms. The analysis suggests that trustworthy AI is not a purely technical attribute but a political and institutional achievement, requiring participatory governance architectures and innovation systems grounded in public value and civic engagement. Research limitations/implications As an editorial introduction, this study does not present original empirical data but synthesizes key theoretical frameworks, case studies and policy debates to guide future research. Its analytical scope is conceptual and comparative, offering a foundation for submissions that further investigate Generative and Urban AI through empirical, normative and practice-based lenses. The limitations lie in its broad coverage and reliance on secondary sources. Nonetheless, it provides an agenda-setting contribution by highlighting the urgent need for interdisciplinary research into how AI reshapes public governance, institutional legitimacy and urban democratic futures. Practical implications This editorial offers a structured framework for policymakers, urban planners, technologists and public administrators to critically assess the governance of Generative and Urban AI systems. By highlighting international case studies and conceptual tools – such as public algorithmic infrastructures, civic trust frameworks and anticipatory governance – the article underscores the importance of institutional design, regulatory foresight and civic engagement. It invites practitioners to shift from techno-solutionist approaches toward inclusive, democratic and place-based AI governance. The reflections aim to support the development of trustworthy AI policies that are grounded in legitimacy, accountability and societal needs, particularly in urban and regional contexts. Social implications The editorial underscores that Generative and Urban AI systems are not socially neutral but carry significant implications for equity, representation and democratic legitimacy. These technologies risk reinforcing existing social hierarchies and systemic biases if not governed inclusively. This study calls for reimagining trust not as a technical feature but as a relational, contested dynamic between institutions and citizens. It encourages submissions that examine how AI reshapes the urban social contract, affects marginalized communities and challenges existing civic infrastructures. The goal is to promote AI governance frameworks that are pluralistic, just and reflective of diverse societal values and lived experiences. Originality/value This editorial offers a timely and conceptually grounded intervention into the emerging field of Urban AI and Generative AI governance. By framing the challenges through Richard R. Nelson’s metaphor of The Moon and the Ghetto, this study foregrounds the gap between technical capabilities and enduring societal injustices. The contribution lies in its interdisciplinary synthesis – bridging innovation systems, AI ethics, public policy and urban governance. It introduces a critical framework for assessing “trustworthy AI” not as a technical goal but as a democratic achievement and encourages research that is policy-relevant, equity-oriented and attuned to the institutional realities of AI in cities.

Human-AI collaboration: Unraveling the effects of user proficiency and AI agent capability in intelligent decision support systems

PurposeThis paper identifies the effects of different dimensions of the cuteness (i.e. baby schema cuteness and whimsical cuteness) of artificial intelligence (AI) agents on attachment to them. In addition, the current paper examines the consequences of the attachment to AI agents.Design/methodology/approachA pretest to validate the measurement scale for the attachment to AI agents and a survey study were conducted with AI agent users. The authors used structural equation modeling to analyze the data for hypothesis testing.FindingsThe baby schema and whimsical cuteness of AI agents drive consumers to develop stronger attachments to their AI agents. This is because consumers perceive cute AI agents as being more trustworthy. As a result, consumers who feel attached to their AI agents are more inclined to report higher satisfaction and commitment levels. They are also more likely to purchase products or services recommended by their AI agents and use them more frequently.Originality/valueDespite the growing popularity of AI agents, there is a lack of understanding regarding which characteristics of AI agents affect consumer behavior. Therefore, this research examines how the attribute of cuteness influences consumers' attachment to AI agents and subsequently affects their satisfaction and purchase intention toward products recommended by AI agents. Our study demonstrates that the element of cuteness in AI agents plays a crucial role in shaping perceptions of benevolence trustworthiness, as well as fostering users' attachment to AI agents. Furthermore, we observe positive consumer behaviors as a result of their attachment to AI agents. The findings from this study provide valuable insights for practitioners on how to effectively utilize cuteness in AI agents.