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Background and Aims: ChatGPT represents the most popular and widely used generative artificial intelligence (AI) model that received significant attention in healthcare research. The aim of the current study was to assess the future trajectory of the needed research in this domain based on the recommendations of the top influential published records. Materials and Methods: A systematic search was conducted on Scopus, Web of Science, and Google Scholar (27–30 November 2023) to identify the top ten ChatGPT-related published records in healthcare across the three databases. Classification of the records as “top” denoting high influence in the field was based on citation counts. Results: A total of 22 unique records from 17 different journals representing 14 different publishers were identified as the top ChatGPT-related publications in healthcare subject. Based on the identified records’ recommendations, the following themes appeared as important areas to consider in future ChatGPT research in healthcare: improving healthcare education, improved efficiency of clinical processes (e.g., documentation), addressing ethical concerns (e.g., patient privacy and consent), supporting research tasks (e.g., data analysis, manuscript preparation), mitigating ChatGPT output biases, improving patient education and engagement, and developing standardized assessment protocols for ChatGPT utility in healthcare. Conclusions: The current review highlighted key areas to be prioritized in assessment of ChatGPT utility in healthcare. Interdisciplinary collaborations and standardizing methodologies are needed to synthesize robust evidence in these studies. Based on these recommendations and the promising potential of ChatGPT on healthcare, JMJ launched a call for papers for a special issue entitled “Evaluating Generative AI-Based Models in Healthcare”. [ABSTRACT FROM AUTHOR]

The rise of generative Artificial Intelligence (AI) has raised concerns about its potential impact on academic integrity, especially concerning research papers and academic writing. Some institutions have decided to ban the use of AI, while others are looking for ways to integrate it into their curriculum responsibly. Pennsylvania State University offers a course called "Emerging Technologies in Popular Culture," which explores the ethical implications of AI-generated art. This approach emphasizes critical evaluation and problematization of AI-generated outputs and serves as a model for embracing AI while maintaining academic rigor. As AI reshapes education, educators must prepare students with the necessary skills to navigate this evolving landscape. [ABSTRACT FROM AUTHOR]

The irruption of artificial intelligence (AI) in all areas of our lives is a reality to which the university, as an institution of higher education, must respond prudently, but also with no hesitation. This paper discusses the potential that resources based on AI presents as potential reviewers of scientific articles in a hypothetical peer review of already published articles. Using different models (GPT-3.5 and GPT-4) and platforms (ChatPDF and Bing), we obtained three full reviews, both qualitative and quantitative, for each of the five articles examined, thus being able to delineate and contrast the results of all of them in terms of the human reviews that these same articles received at the time. The evidence found highlights the extent to which we can and should rely on generative language models to support our decisions as qualified experts in our field. Furthermore, the results also corroborate the hallucinations inherent in these models while pointing out one of their current major shortcomings: the context window limit. On the other hand, the study also points out the inherent benefits of a model that is in a clear expansion phase, providing a detailed view of the potential and limitations that these models offer as possible assistants to the review of scientific articles, a key process in the communication and dissemination of academic research. [ABSTRACT FROM AUTHOR]

Generative artificial intelligence (GAI) applications, such as ChatGPT (Chat Generative Pretrained Transformer) and Midjourney, have recently attracted much attention from researchers and school teachers. While many people are eager to learn more about GAI applications, some scholars are concerned about the potential misuse of them. It is predicted that the use of GAI applications will increase rapidly in the coming years. Therefore, it is important to consider the challenges and research issues through some concrete application examples of using GAI for education. In this position paper, the authors aim to address these issues from the perspectives of academic research and educational objectives. Along with defining GAI, several illustrative examples of using GAI applications in educational settings are provided. Moreover, potential research issues of GAI-based learning, including research design, relevant learning strategies, research focus, and measuring tools, are discussed. ET&S journal is especially welcoming research on unlocking the potential of GAI for education to realize the two notions of "Knowing [why] is the essential element for learners to have in-depth understanding" and "It is all about prompts: Get rid of the 'search' mindset and use 'programming prompt' instead." [ABSTRACT FROM AUTHOR]

This document is an editorial introduction from the Journal of Management Information Systems. It emphasizes the importance of incorporating artificial intelligence (AI) into the field of Information Systems (IS) research. The editorial discusses the potential benefits and risks of AI, including its impact on societal well-being, economic benefits, and long-term threats. It also highlights several research papers published in the journal that explore topics such as trust in AI, AI investments, cybersecurity, value creation through AI systems, and the effects of customization in e-commerce. The editorial encourages the IS community to contribute to the understanding and deployment of AI in order to enhance organizational development and human capabilities. [Extracted from the article]

Artificial intelligence (AI) has the potential to positively impact library and archives collections and services--enhancing reference, instruction, metadata creation, recommendations, and more. However, AI also has ethical implications. This paper presents an extensive literature and review analysis that examines AI projects implemented in library and archives settings, asking the following research questions: RQ1: How is artificial intelligence being used in libraries and archives practice? RQ2: What ethical concerns are being identified and addressed during AI implementation in libraries and archives? The results of this literature review show that AI implementation is growing in libraries and archives and that practitioners are using AI for increasingly varied purposes. We found that AI implementation was most common in large, academic libraries. Materials used in AI projects usually involved digitized and born digital text and images, though materials also ranged to include web archives, electronic theses and dissertations (ETDs), and maps. AI was most often used for metadata extraction and reference and research services. Just over half of the papers included in the literature review mentioned ethics or values related issues in their discussions of AI implementation in libraries and archives, and only one-third of all resources discussed ethical issues beyond technical issues of accuracy and human-in-the-loop. Case studies relating to AI in libraries and archives are on the rise, and we expect subsequent discussions of relevant ethics and values to follow suit, particularly growing in the areas of cost considerations, transparency, reliability, policy and guidelines, bias, social justice, user communities, privacy, consent, accessibility, and access. As AI comes into more common usage, it will benefit the library and archives professions to not only consider ethics when implementing local projects, but to publicly discuss these ethical considerations in shared documentation and publications. [ABSTRACT FROM AUTHOR]

Due to the fast-changing environments caused by artificial intelligence development, the socio-technical challenge in contemporary educational systems focuses on the need for more regulative measures guiding system stakeholders' behavior. In fulfilling the present legal gap, enacted soft law regulation has been laid out, and a detailed systematic literature review was conducted in the paper presented. The specific methodological approach was selected to deal with two crucial research tasks: to reveal and recommend fundamental governing mechanisms regarding the use and application of generative artificial intelligence; more precisely, large language models in educational systems. Three systematically guided layers of quantitative and qualitative content analysis of central policy, legislation, and regulatory mechanisms in governing AI in education were extracted from the 142 Scopus Database and Web of Science research papers analyzed and presented. These research findings benefit policymakers, regulatory and legislative bodies, and agencies in constructing governing frames for using and applying generative artificial intelligence in education. [ABSTRACT FROM AUTHOR]

The article highlights a Chinese team's discovery of a bacterium within mosquitoes' guts that inhibits dengue and Zika viruses, potentially aiding disease control efforts. Topics include the bacterium's efficacy in disrupting viral transmission, the significance amidst rising mosquito resistance to insecticides, and ongoing research to assess its real-world impact alongside existing control measures.

The emergence of generative artificial intelligence such as ChatGPT has left people feeling ambivalent and disagreement among scholars, academicians, educators and the community at large prevails. While the artificial intelligence could potentially revolutionize how research is conducted and how research papers are written, a number of ethical concerns arise. In particular, the world of academia has reservations pertaining to whether this language model will actually do more good than harm, especially as far as academic writing is concerned. This paper argues that the cutting-edge technology is here to stay and the question is not whether to accept it, but rather, how to best utilize it judiciously, cautiously and responsibly to improve research performance by strictly adhering to academic integrity and transparency. Potential benefits and drawbacks of ChatGPT will be critically examined in light of current literature and, when relevant, potential solutions to the drawbacks will also be provided or commented on. Needless to say, the use of artificial intelligence in academic writing is still in its infancy and more discussion and debates pertaining to its use and merit are highly urged. This paper contributes to these on-going debates. [ABSTRACT FROM AUTHOR]

The use of generative artificial intelligence (GenAI) in academia is a subjective and hotly debated topic. Currently, there are no agreed guidelines towards the usage of GenAI systems in higher education (HE) and, thus, it is still unclear how to make effective use of the technology for teaching and learning practice. This paper provides an overview of the current state of research on GenAI for teaching and learning in HE. To this end, this study conducted a systematic review of relevant studies indexed by Scopus, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The search criteria revealed a total of 625 research papers, of which 355 met the final inclusion criteria. The findings from the review showed the current state and the future trends in documents, citations, document sources/authors, keywords, and co-authorship. The research gaps identified suggest that while some authors have looked at understanding the detection of AI-generated text, it may be beneficial to understand how GenAI can be incorporated into supporting the educational curriculum for assessments, teaching, and learning delivery. Furthermore, there is a need for additional interdisciplinary, multidimensional studies in HE through collaboration. This will strengthen the awareness and understanding of students, tutors, and other stakeholders, which will be instrumental in formulating guidelines, frameworks, and policies for GenAI usage. [ABSTRACT FROM AUTHOR]

The Journal of Management Information Systems has published a special section on cognitive reapportionment in relation to artificial intelligence (AI) and advances in computing. The section explores the allocation of tasks between humans and machines as AI becomes more capable of cognitive tasks. The limitations of current AI systems are discussed, as well as the potential for collaboration between humans and AI. The journal also includes papers on topics such as knowledge-aware models, crowdsourcing, social media effects, and the impact of government contracting on high-tech firms. [Extracted from the article]

This paper investigates the complex interplay between generative artificial intelligence (AI) and human intellect in academic writing and publishing. It examines the 'organic versus synthetic' paradox, emphasizing the implications of using generative AI tools in educational and academic integrity contexts. The paper critiques the prevalent 'publish or perish' culture in academia, highlighting the need for systemic reevaluation due to generative AI's emerging role in academic writing and reporting. It delves into the legal and ethical challenges of authorship and ownership, especially in relation to copyright laws and AI-generated content. The paper discusses generative AI's diverse roles and advocates for transparent reporting to uphold academic integrity. Additionally, it calls for a broader examination of generative AI tools and stresses the need for new mechanisms to identify generative AI use and ensure adherence to academic integrity and ethics. The implications of generative AI are also explored, suggesting the need for innovative AI-inclusive strategies in academia. The paper concludes by emphasizing the significance of generative AI in various information-processing domains, highlighting the urgency to adapt and transform academic practices in an era of rapid generative AI-driven change. [ABSTRACT FROM AUTHOR]

This document is an editorial that explores the potential impact of artificial intelligence (AI) on education. It emphasizes the need for guidelines and frameworks to facilitate the adoption of AI in educational settings. The document also presents a collection of research papers that cover various topics related to AI in education, including AI chatbots, predictive learning analytics, virtual reality, and blended learning. These papers provide valuable insights and empirical evidence for educators, policymakers, and AI developers interested in the future of education. [Extracted from the article]

This paper explores the emerging field of prompt engineering within generative AI, emphasizing its role as a critical intersection between art and science. Prompt engineering is identified as the key to unlocking the full potential of generative AI technologies by optimizing human-AI communication. Through a comprehensive analysis of the related literature, this study illustrates how prompt engineering transcends mere technical manipulation, requiring a blend of creativity, strategic thinking, and a deep understanding of generative AI capabilities. This paper provides various strategies for crafting effective prompts, from simple to sophisticated techniques, highlighting the importance of ethical considerations and the potential risks associated with prompt manipulation. By establishing a set of principles and guidelines, this paper aims to advance prompt engineering as a discipline essential for enhancing AI's functionality and reliability and, with this justification, introduces the 'Prompt Engineering for Gen[i]erative AI Framework'. After all, this paper calls for a multidisciplinary approach to prompt engineering, advocating for its recognition and development as a pivotal component of AI literacy and application. Through this exploration, this paper intends to contribute to the evolving dialogue on the integration of human creativity with generative AI capabilities, offering insights into the future of effective and ethical AI interaction. [ABSTRACT FROM AUTHOR]

The paper seeks to investigate novel potentials for building design and structure generation that arise at the intersection of computational design and AI-generated 3D geometries. Although the use of AI technologies is exponentially increasing inside the architectural discipline, the design of spatial building configurations using AI-generated 3D geometries is still limited in its applications and represents an ongoing field of investigation in advanced architectural research. In this regard, several questions still need to be answered: how can we design new building typologies from AI-generated 3D geometries? And how can we use these typologies to shape both the real and the virtual world? The paper proposes a new approach to architectural design where artificial intelligence is used as the starting point for design exploration, while computational design procedures are employed to convert AI-generated 3D geometries into building elements – such as columns, beams, horizontal and vertical surfaces. The paper starts with a general overview of the current use of artificial intelligence inside the architectural discipline, and then it moves towards the explanation of specific AI generative models for 3D geometry reconstruction and representation. Subsequently, the proposed working pipeline is analysed in more detail – from the creation of 3D geometries using generative AI models to the conversion of such geometries into building elements that can be further designed and optimised using computational design tools and methods. The results shown in the paper are achieved using Shap-E as the main AI model, though the proposed pipeline can be implemented with multiple AI models. The paper ends by showing some of the generated results, finally adding some considerations to the relationship between human and artificial creativity inside the architectural discipline. The work presented in the paper suggests that the use of computational design tools and methods combined with the tectonics of the latent space opens new opportunities for topological and typological explorations. In a time where traditional architectural typologies are moving towards stagnation due to their inability to satisfy new human needs and ways of living, exploring AI-based working pipelines related to architectural design allows the definition of new design solutions for the generation of new architectural spaces. In doing so, the serendipitous aspect of AI biases is used as an auxiliary force to inform design decisions, promoting the discovery of a new inbuilt dynamism between human and artificial creativity. In a time where AI is everywhere, understanding the measure of such dynamism represents a key aspect for the future of the architectural discipline. [ABSTRACT FROM AUTHOR]

One of the most common questions that students ask statistics advisors is 'What test should I do?' This paper explores the use of generative AI chatbots, specifically ChatGPT, as a tool to assist students, in particular those with limited experience in statistics, in selecting appropriate statistical tests for their analyses. Traditional methods, such as flowcharts and online test selectors, require at least a basic understanding of measurement scales and research design, which can be an issue for many students who have limited exposure to statistics on their courses. This research focuses on developing and refining prompts to guide ChatGPT in providing accurate and relevant statistical test recommendations. A hypothetical scenario was used to test the effectiveness of various prompts, ranging from simple, naïve questions to more sophisticated ones utilising specific prompt patterns, such as the 'context manager' and 'flipped interaction.' These patterns were selected to enhance the chatbot's responses and ensure the relevance and accuracy of the test suggestions. The findings suggest that while AI chatbots like ChatGPT can be a valuable resource for students, their effectiveness is highly dependent on the quality of the prompts used. The paper concludes with a discussion on the potential of these AI tools in educational settings, acknowledging the limitations of current technology and suggesting directions for future research and development. [ABSTRACT FROM AUTHOR]

The advent of Generative Artificial Intelligence (GAI) is reshaping the landscape of project management and stakeholder dynamics. This paper explores the multifaceted impact of AI on traditional stakeholder relationships, examines how AI can enhance stakeholder management, and proposes strategies for effectively "managing" AI as a stakeholder itself, i.e. an AI-Stakeholder. Furthermore, it deepens into the ethical considerations surrounding AI's role in stakeholder interactions and concludes by summarizing the risks and opportunities presented by this technological and relational revolution. As AI becomes increasingly integrated into project management processes, understanding its influence on stakeholder perspectives is crucial for successful project outcomes and organizational adaptation in an AI-driven future. [ABSTRACT FROM AUTHOR]

The September issue of Pedagogy in Health Promotion focuses on the future of teaching and learning in an evolving digital world. The papers in this issue address the challenges posed by advances in artificial intelligence and online communication. One set of papers discusses the need for educators to equip students with skills to navigate the widespread misinformation and disinformation in public health. Another set of papers explores the use of artificial intelligence in teaching and learning, highlighting its benefits and ethical considerations. The issue also emphasizes the importance of providing faculty with training in online pedagogy. Overall, the journal aims to encourage further exploration of how pedagogy in health promotion is adapting to the digital world. [Extracted from the article]

The rapid advances in Generative AI tools have produced both excitement and worry about how AI will impact academic writing. However, little is known about what norms are emerging around AI use in manuscript preparation or how these norms might be enforced. We address both gaps in the literature by conducting a survey of 271 academics about whether it is necessary to report ChatGPT use in manuscript preparation and by running GPT-modified abstracts from 2,716 published papers through a leading AI detection software to see if these detectors can detect different AI uses in manuscript preparation. We find that most academics do not think that using ChatGPT to fix grammar needs to be reported, but detection software did not always draw this distinction, as abstracts for which GPT was used to fix grammar were often flagged as having a high chance of being written by AI. We also find disagreements among academics on whether more substantial use of ChatGPT to rewrite text needs to be reported, and these differences were related to perceptions of ethics, academic role, and English language background. Finally, we found little difference in their perceptions about reporting ChatGPT and research assistant help, but significant differences in reporting perceptions between these sources of assistance and paid proofreading and other AI assistant tools (Grammarly and Word). Our results suggest that there might be challenges in getting authors to report AI use in manuscript preparation because (i) there is not uniform agreement about what uses of AI should be reported and (ii) journals might have trouble enforcing nuanced reporting requirements using AI detection tools. [ABSTRACT FROM AUTHOR]

This paper explores the multifaceted realm of artificial intelligence (AI) implementation in financial services, providing insights into its potential, challenges and best practices. Highlighting the emergence of generative AI (GenAI) as a transformative tool, the paper underscores its significant impact on productivity and revenue generation within investment banks and capital markets. Addressing inherent risks of AI adoption, the paper stresses the importance of robust governance frameworks to mitigate operational, reputational and compliance risks. Specific attention is given to the phenomenon of GenAI hallucinations and the imperative for deterministic AI models to ensure data integrity and regulatory compliance. The paper outlines four key pillars of AI's applications in financial services: predictive AI, anomaly detection AI, classification AI and GenAI. Examples highlight AI's role in risk management, fraud prevention, customer experience enhancement and internal process optimisation, underscoring its transformative potential across the industry. The paper also covers the distinction between public and private AI models, emphasising the advantages of proprietary data-driven insights in ensuring competitive advantage and regulatory compliance. Concluding with actionable insights for AI implementation, the paper advocates for a strategic approach encompassing clear vision setting, risk oversight, data privacy management, centralised data architecture and comprehensive process automation. [ABSTRACT FROM AUTHOR]

This document is a summary of a journal article titled "Artificial Intelligence in Modeling and Simulation." The article discusses the integration of artificial intelligence (AI) into modeling and simulation (M&S) processes. It highlights the various applications of AI in fields such as engineering, physics, social sciences, and biology. The article also provides an overview of 11 selected papers from a special issue on AI and M&S, covering topics such as AI techniques for simulation and optimization, AI in agent-based modeling, AI for data processing and classification models, and artificial neural network (ANN) methods for improved M&S. The papers explore different methodologies and approaches to enhance the efficiency and validity of modeling and simulation using AI. The article concludes by emphasizing the progress and diverse uses of AI in M&S and expressing gratitude to the authors, reviewers, and editorial team involved in the special issue. [Extracted from the article]

Artificial intelligence (AI) is impacting and has the potential to upend entire business models and structures. The adoption of such new technologies to support newsgathering processes is established practice for newsrooms. For AI specifically, we are seeing a new era of AI‐assisted journalism emerge with trust in the AI‐driven analyses and accuracy of results as core tenets. In Part I of this position paper, we discuss the contributions of six recently published research papers co‐authored by Bloomberg's Artificial Intelligence Engineering team that show the intricacies of training AI models for reliable newsgathering processes. The papers investigate (a) the creation of models for updated headline generation, showing that headline generation models benefit from access to the past state of the article, (b) sequentially controlled text generation, which is a novel task and we show that in general, more structured awareness results in higher control accuracy and grammatical coherence, (c) chart summarization, which looks into identifying the key message and generating sentences that describe salient information in the multimodal documents, (d) a semistructured natural language inference task to develop a framework for data augmentation for tabular inference, (e) the introduction of a human‐annotated dataset (ENTSUM) for controllable summarization with a focus on named entities as the aspect to control, and (f) a novel defense mechanism against adversarial attacks (ATINTER). We also examine Bloomberg's research work, building its own internal, not‐for‐commercial‐use large language model, BloombergGPT, and training it with the goal of demonstrating support for a wide range of tasks within the financial industry. In Part II, we analyze the evolution of automation tasks in the Bloomberg newsroom that led to the creation of Bloomberg's News Innovation Lab. Technology‐assisted content creation has been a reality at Bloomberg News for nearly a decade and has evolved from rules‐based headline generation from structured files to the constant exploration of potential ways to assist story creation and storytelling in the financial domain. The Lab now oversees the operation of hundreds of software bots that create semi‐ and fully automated stories of financial relevance, providing journalists with depth in terms of data and analysis, speed in terms of reacting to breaking news, and transparency to corners of the financial world where data investigation is a gigantic undertaking. The Lab recently introduced new tools that provide journalists with the ability to explore automation on demand while it continues to experiment with ways to assist story production. In Part III, we conceptually discuss the transformative impact that generative AI can have in any newsroom, along with considerations about the technology's shortcomings in its current state of development. As with any revolutionary new technology, as well as with exciting research opportunities, part of the challenge is balancing any potential positive and negative impacts on society. We offer our principles and guidelines used to inform our approach to experimenting with the new generative AI technologies. Bloomberg News' style guide reminds us that our "journalism is aimed at possibly the most sophisticated audience in the world, for whom accuracy is essential." [ABSTRACT FROM AUTHOR]

The value of generative AI tools in higher education has received considerable attention. Although there are many proponents of its value as a learning tool, many are concerned with the issues regarding academic integrity and its use by students to compose written assessments. This study evaluates and compares the output of three commonly used generative AI tools, ChatGPT, Bing and Bard. Each AI tool was prompted with an essay question from undergraduate (UG) level 4 (year 1), level 5 (year 2), level 6 (year 3) and postgraduate (PG) level 7 biomedical sciences courses. Anonymised AI generated output was then evaluated by four independent markers, according to specified marking criteria and matched to the Frameworks for Higher Education Qualifications (FHEQ) of UK level descriptors. Percentage scores and ordinal grades were given for each marking criteria across AI generated papers, inter-rater reliability was calculated using Kendall's coefficient of concordance and generative AI performance ranked. Across all UG and PG levels, ChatGPT performed better than Bing or Bard in areas of scientific accuracy, scientific detail and context. All AI tools performed consistently well at PG level compared to UG level, although only ChatGPT consistently met levels of high attainment at all UG levels. ChatGPT and Bing did not provide adequate references, while Bing falsified references. In conclusion, generative AI tools are useful for providing scientific information consistent with the academic standards required of students in written assignments. These findings have broad implications for the design, implementation and grading of written assessments in higher education. [ABSTRACT FROM AUTHOR]

In this paper, we undertake a critical analysis of how chatbots built on generative artificial intelligence impact assumptions underlying definitions of trust. We engage a particular definition of trust and the object-oriented model of trust that was built upon it and identify how at least four implicit assumptions may no longer hold. Those assumptions include that people generally provide others with a default level of trust, the ability to identify whether the trusted agent is human or artificial, that risk and trust can be readily quantified or categorized, and that there is no expectation of gain by agents engaged in trust relationships. Based on that analysis, we suggest modifications to the definition and model to accommodate the features of generative AI chatbots. Our changes re-emphasize developers' responsibility for the impacts of their AI artifacts, no matter how sophisticated the artifact may be. The changes also reflect that trust relationships are more fraught when participants in such relationships are not confident in identifying the nature of a potential trust partner. [ABSTRACT FROM AUTHOR]

This paper conducts a systematic review and interdisciplinary analysis of the ethical challenges of generative AI technologies (N = 37), highlighting significant concerns such as privacy, data protection, copyright infringement, misinformation, biases, and societal inequalities. The ability of generative AI to produce convincing deepfakes and synthetic media, which threaten the foundations of truth, trust, and democratic values, exacerbates these problems. The paper combines perspectives from various disciplines, including education, media, and healthcare, underscoring the need for AI systems that promote equity and do not perpetuate social inequalities. It advocates for a proactive approach to the ethical development of AI, emphasizing the necessity of establishing policies, guidelines, and frameworks that prioritize human rights, fairness, and transparency. The paper calls for a multidisciplinary dialogue among policymakers, technologists, and researchers to ensure responsible AI development that conforms to societal values and ethical standards. It stresses the urgency of addressing these ethical concerns and advocates for the development of generative AI in a socially beneficial and ethically sound manner, contributing significantly to the discourse on managing AI's ethical implications in the modern digital era. The study highlights the theoretical and practical implications of these challenges and suggests a number of future research directions. [ABSTRACT FROM AUTHOR]

This paper delves into the transformative era of generative artificial intelligence (GenAI) in marketing, underscoring the vast potential of AI to revolutionise marketing strategies through enhanced personalisation and efficiency while highlighting the rapid adoption rate among companies and marketing professionals. Despite the enthusiasm, it navigates through the apprehensions surrounding job displacement, misinformation and brand safety, offering a comprehensive guide to the strategic adoption of artificial intelligence (AI) with a view to harnessing its benefits without compromising brand integrity. It identifies common pitfalls in AI adoption, such as lack of preparation, myopic focus on current use cases, and neglect of strategic planning, emphasising the importance of a thoughtful, multi-disciplinary approach to AI integration. This includes viewing AI as an assistive tool rather than an end goal, understanding its implications for media, fostering internal leadership, and establishing stringent brand protection guardrails. The paper concludes with a call to action for marketers to embrace AI with strategic foresight, leveraging its revolutionary potential to drive innovation while steadfastly upholding ethical standards and brand values. [ABSTRACT FROM AUTHOR]

Since the 1990s, Singapore has sought to project itself as innovative and technologically cutting-edge through narratives such as 'intelligent island', digital ecosystem', and more recently, 'smart nation'. The Smart Nation initiative is aimed at digitising and, 'datafying' as many public services as possible. To this end, the government launched a National Artificial Intelligence Strategy in 2019 that envisions Singapore as a global hub for developing, test-bedding, deploying, and scaling AI solutions. In late 2023, the same year Generative AI entered public consciousness thanks to ChatGPT, Version 2.0 of Singapore's AI strategy was released. Providing a commentary of Singapore's digital journey and its governing approaches, this paper contends that AI is more than just a new digital tool for Singaporeans; it is also a proxy for Singapore's digital and 'smart nation' global reputation. [ABSTRACT FROM AUTHOR]

The rapid development of Generative Artificial Intelligence (GAI) technology has shown tremendous potential in various fields, such as image generation, text generation, and video generation, and it has been widely applied in various industries. However, GAI also brings new risks and challenges to cybersecurity. This paper analyzes the application status of GAI technology in the field of cybersecurity and discusses the risks and challenges it brings, including data security risks, scientific and technological ethics and moral challenges, Artificial Intelligence (AI) fraud, and threats from cyberattacks. On this basis, this paper proposes some countermeasures to maintain cybersecurity and address the threats posed by GAI, including: establishing and improving standards and specifications for AI technology to ensure its security and reliability; developing AI-based cybersecurity defense technologies to enhance cybersecurity defense capabilities; improving the AI literacy of the whole society to help the public understand and use AI technology correctly. From the perspective of GAI technology background, this paper systematically analyzes its impact on cybersecurity and proposes some targeted countermeasures and suggestions, possessing certain theoretical and practical significance. [ABSTRACT FROM AUTHOR]

The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of artificial intelligence (AI), propelling vehicles into realms of unprecedented autonomy. Commencing with an overview of the current industry landscape with respect to Operational Design Domain (ODD), this paper delves into the fundamental role of AI in shaping the autonomous decision-making capabilities of vehicles. It elucidates the steps involved in the AI-powered development life cycle in vehicles, addressing various challenges such as safety, security, privacy, and ethical considerations in AI-driven software development for autonomous vehicles. The study presents statistical insights into the usage and types of AI algorithms over the years, showcasing the evolving research landscape within the automotive industry. Furthermore, the paper highlights the pivotal role of parameters in refining algorithms for both trucks and cars, facilitating vehicles to adapt, learn, and improve performance over time. It concludes by outlining different levels of autonomy, elucidating the nuanced usage of AI algorithms, and discussing the automation of key tasks and the software package size at each level. Overall, the paper provides a comprehensive analysis of the current industry landscape, focusing on several critical aspects. [ABSTRACT FROM AUTHOR]

This paper delves into the intricate relationship between Artificial Intelligence (AI) and the art ecosystem, emphasizing the need for a decolonizing approach in the face of AI's growing influence. It argues that the development of AI is not just a technological leap but also a significant cultural and societal moment, akin to the advent of moving images that Walter Benjamin famously analyzed. The paper examines how AI, particularly in its current oligarchical and corporate-driven form, perpetuates and magnifies the existing social inequalities, thereby necessitating a critical and radical rethinking of its role in society and the arts. At the heart of the discussion is the concept of AI as a broad term encompassing various forms of machine intelligence, from natural language processing to computer vision. The paper criticizes the dominant anthropocentric view of intelligence and creativity, proposing a more inclusive approach that considers the diverse forms of intelligence present in other species and potentially in AI itself. It underscores the role of AI in shaping the art ecosystem, not just in the creative process but also in gatekeeping and decision-making. The paper proposes a framework for decolonizing AI in the art ecosystem, focusing on four key tasks: recognizing access as a form of power, understanding and addressing biases inherent in AI, assessing the impact of AI on marginalized communities, and challenging dominant narratives and epistemologies to create space for alternative voices and perspectives. It emphasizes the need for artists and the art community to engage actively with AI, shaping its development towards more equitable and just outcomes. In conclusion, the paper calls for a radical reimagination of AI's role in society and the arts, advocating for a future where AI is not just about technological advancement but also about fostering a more inclusive, equitable, and creatively diverse world. It invites artists, thinkers, and innovators to join in this journey of reimagining and reshaping the future of AI and the art ecosystem. [ABSTRACT FROM AUTHOR]

The competition inside higher education institutions, namely universities, is tightening, putting emphasize on competitive intelligence (CI) function. At the same time, communication has shifted to digital channels, this trend was largely influenced by Corona virus pandemic. This presents a challenge for university reputation measurement and ranking, while the electronic word to mouth (E-wom) is more challenging to measure, control or influence than the issues measured in traditional university rankings. While traditional metrics are based on measuring academic reputation via surveys and gathering data from research organisations, this paper presents a way to include AI, namely chatGPT and big-data based media-analytics with social media sentiment to aid analysing the reputation of a University. Results based on Finnish universities indicate, that differences between media visibility and sentiment exist, and can be to some extent utilized in rating universities in local level and also generalize to global level, finally targeting to URS (University reputation score) -index. Due to complexity of measuring the reputation of the university strictly via AI and automated opinion mining, several limitations exist. The context of Finnish universities were chosen in order to limit the scope of the analysis. [ABSTRACT FROM AUTHOR]

This article traces the development of Creative Nursing from its origin in 1981 as a newsletter about Primary Nursing to its current position as a quarterly international, interdisciplinary, peer-reviewed, indexed, themed journal that continues to nurture novice authors, welcome international submissions, review articles that other journals won't consider, and address subjects that many journals avoid. Future directions include content in multiple languages, new author guidelines that invite submissions of research methods papers, moving beyond statistical significance based on p-value thresholds, asking authors to make explicit the implications for knowledge translation in their papers, and thinking creatively about how artificial intelligence can be leveraged for research, education, and practice. [ABSTRACT FROM AUTHOR]

Background and Purpose: With the rapid advancements in generative AI, understanding its ability to emulate human language conventions is crucial. This work aims to analyze the possibility of applying AI technology in language production by comparing the lexico-grammatical features of abstracts created with the help of ChatGPT and written by British and American researchers. Methodology: Twenty papers written by researchers affiliated with UK universities and twenty by researchers affiliated with American universities were selected from the journals listed under the first quartile of the Web of Science and Scopus. Using the titles that were from the selected works, 40 abstracts were generated from ChatGPT for comparison. Each article was introduced with its title, and ChatGPT was asked to create an abstract based on the title. Subsequently, the subjects were examined with the help of Biber’s (1991) multivariate model considering five dimensions, which include the Informational vs Involved discourse, the Narrative vs Non-narrative, the Explicit vs. Situation- dependent discourse, Overt expression of argumentation/persuasion and the Impersonal/Abstract style as opposed to the Non impersonal/Non-abstract style. Findings: The five factors analysed in the texts give evidence that ChatGPT generates more information centric, non-narrative, argumentative, and less abstractive discourse than human researchers. Contributions: The results of the study show the possibilities for the further development of AI that helps to create language closer to human language. [ABSTRACT FROM AUTHOR]

Generative AI is impacting many areas of society and the economy. In particular, in the fashion industry, it has brought innovation in fashion design by influencing fashion trend prediction and creativity, and it plays an important role in providing customized products to consumers and improving quality through the optimization of the manufacturing process. Therefore, this paper searches Google for 'generative AI' and 'digital fashion' to investigate domestic and international business and fashion site articles since the 2020s and analyzes AI use cases in the fashion industry to study the changes in the fashion industry and suggest possible developments. The results of this paper are as follows. First, the combination of generative AI and humans in design can lead to creative outcomes. Second, generative AI can be used in the production and distribution processes to maximize efficiency. Third, it can drive sustainable fashion by minimizing resource waste through demand forecasting and logistics management. [ABSTRACT FROM AUTHOR]

The first half of 2023 was dominated by a public discussion of the nature and implications of generative artificial intelligence (genAI) models that are poised to become the most significant cross-cultural global disruptor since the invention of the World-Wide Web. It can be predicted that genAI will affect how cultural heritage is being managed and practiced, primarily by providing analysis and decision-making tools, but also by genAI generated texts and images, in particular reconstructions of objects and sites. The more speculative interpretations of contexts and alternative interpretations generated by genAI models may constitute manifestations of cultural heritage in their own right. But do these constitute human cultural heritage, or are they AI cultural heritage? This paper is a deliberation of the realities and future(s) of cultural heritage in a genAI and post-genAI world. [ABSTRACT FROM AUTHOR]

This paper introduces the collection of the Journal on Machine Learning (ML) and the user. It provides a brief history of ML from the 1950's through to the current time, sketching the nature of the kinds of precursor AI techniques used in such things as expert systems right the way through to the emergence of ML and its tool sets, including deep learning. It concludes with the 'generative AI' used in such ML technologies as PaLM and GPT-3. The history highlights key changes and developments in ML, the especial importance and limitations of deep learning, and the changing attitudes and expectations of users in an environment when ML can and often is oversold. The paper then explores the ways CSCW research has addressed the social context of organisational systems and how the same can apply for ML tools and techniques. It urges research that focuses on the particular ways that ML comes to fit into 'real world' collaborative work sites and hence speaks to the CSCW cannon. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Technology & Chinese Language Teaching is the property of Middle Tennessee State University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Digital Education Review is the property of University of Barcelona, Virtual Teaching & Learning Research Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Artificial intelligence (AI) refers to computer systems that can perform tasks that typically require human intelligence. This is achieved by using algorithms and neural networks for machine learning (ML) and deep learning. ChatGPT is an AI-powered chatbot that can generate responses on any topic based on the user's input or queries. In this review, the focus is on the potential applications of generative AI and ChatGPT in the fields of medical and health professions education. It is important to educate both educators and students about the impact of using generative AI, such as ChatGPT, which is widely used through generative adversarial networks (GANs). In the field of healthcare, GANs can analyse vast datasets to assist in diagnosis, patient data management, and analysis. Students use ChatGPT to obtain factual answers, write papers and translate languages. It can help students with their assignments by summarising literature reviews and generating new ideas. In medical education, educators use ChatGPT to develop learning activities, assessments, and curricula, enhance student learning, and even generate research papers for publication. However, students need to use generative AI carefully so that it does not impede their ability to think critically or write effectively. Guidelines are being formulated in different institutions to regulate the use of this immensely powerful tool. This review could highlight the scope of incorporating ChatGPT into the field of medical education. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) have had a significant impact on several domains, including software engineering. However, a comprehensive understanding of LLMs' use, impact, and potential limitations in software engineering is still emerging and remains in its early stages. This paper analyzes the role of large language models (LLMs), such as ChatGPT-3.5, in software requirements engineering, a critical area in software engineering experiencing rapid advances due to artificial intelligence (AI). By analyzing several studies, we systematically evaluate the integration of ChatGPT into software requirements engineering, focusing on its benefits, challenges, and ethical considerations. This evaluation is based on a comparative analysis that highlights ChatGPT's efficiency in eliciting requirements, accuracy in capturing user needs, potential to improve communication among stakeholders, and impact on the responsibilities of requirements engineers. The selected studies were analyzed for their insights into the effectiveness of ChatGPT, the importance of human feedback, prompt engineering techniques, technological limitations, and future research directions in using LLMs in software requirements engineering. This comprehensive analysis aims to provide a differentiated perspective on how ChatGPT can reshape software requirements engineering practices and provides strategic recommendations for leveraging ChatGPT to effectively improve the software requirements engineering process. [ABSTRACT FROM AUTHOR]

Copyright of Inteligencia Artificial: Revista Iberoamericana de Inteligencia Artificial is the property of Sociedad Iberoamericana de Inteligencia Artificial (IBERAMIA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A drug molecule is a substance that changes an organism's mental or physical state. Every approved drug has an indication, which refers to the therapeutic use of that drug for treating a particular medical condition. While the Large Language Model (LLM), a generative Artificial Intelligence (AI) technique, has recently demonstrated effectiveness in translating between molecules and their textual descriptions, there remains a gap in research regarding their application in facilitating the translation between drug molecules and indications (which describes the disease, condition or symptoms for which the drug is used), or vice versa. Addressing this challenge could greatly benefit the drug discovery process. The capability of generating a drug from a given indication would allow for the discovery of drugs targeting specific diseases or targets and ultimately provide patients with better treatments. In this paper, we first propose a new task, the translation between drug molecules and corresponding indications, and then test existing LLMs on this new task. Specifically, we consider nine variations of the T5 LLM and evaluate them on two public datasets obtained from ChEMBL and DrugBank. Our experiments show the early results of using LLMs for this task and provide a perspective on the state-of-the-art. We also emphasize the current limitations and discuss future work that has the potential to improve the performance on this task. The creation of molecules from indications, or vice versa, will allow for more efficient targeting of diseases and significantly reduce the cost of drug discovery, with the potential to revolutionize the field of drug discovery in the era of generative AI. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to endorse and elaborate on the recommendations put forward by the Sharland Foundation Developmental Disabilities Applied Behavioural Research and Impact Network (SF-DDARIN), emphasising their significance in the field of developmental disabilities. Design/methodology/approach: This paper outlines a specific point of view. The first section focuses on integrating developmental theory and advanced technology in interventions for developmental disabilities. Subsequently, the commentary explores virtual reality (VR) and generative artificial intelligence (AI) for enhancing social skills and personalising support. Finally, the piece highlights innovations like SocialWise VR and Custom Generative Pre-Trained Transformers in aligning interventions with developmental stages. Findings: Technologies like VR and generative AI hold vast potential to revolutionise how clinicians provide timely and relevant knowledge to individuals with developmental disabilities. Research limitations/implications: This is strictly a commentary. Practical implications: Availability of technology. Social implications: Both VR and generative AI will impact service delivery in a meaningful way. Originality/value: The paper advocates for incorporating these technologies into SF-DDARIN's approach, emphasising their potential to revolutionise evidence-based interventions in developmental disabilities. [ABSTRACT FROM AUTHOR]