Topical review: Incorporating generative artificial intelligence into neuropsychology training: best practices, pitfalls, and recommendations for effective implementation.

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

Marketing scientists as well as practitioners believe that artificial intelligence (AI) holds the promise of productivity gains for organizations. However, there has been little scientific research into these theories. This study investigates the role of AI in enhancing marketing productivity, deriving insights from a case study conducted with the marketing team of an industrial software start-up. Drawing upon Case Study Analysis by Yin (2018) and Participatory Action Research by Kemmis and McTaggart (2007), the study employs a combination of survey interviews, AI tool research and AI tool testings. Key findings indicate that productivity gains are more likely than productivity impairments with the use of marketing AI tools. This effect is even stronger when knowledge workers possess high levels of AI skills and utilize AI tools with suitable capabilities. Having closely analyzed six marketing disciplines, particularly SEO / content and design demonstrated significant productivity gains including generative AI (GAI) tools the team already subscribed to like ChatGPT 4 and Canva, but also new AI solutions. While an AI tool’s level of integration only showed a weak positive productivity impact, future studies are suggested to further investigate this variable by comparing the effects of less advanced but more accessible tools like generative AI versus highly advanced, but less accessible business AI. Having navigated the vast and dynamic landscape of AI tools, insights further emphasize the importance of AI experience sharing and informed decision-making, implying knowledge of own user rights and always staying updated on AI advancements. Zooming out from process level, the work's literature review further highlights the role of environmental and organizational AI enablers, like budget allocation, fostering AI trust and mindset, but also implementing AI routines and responsibilities. Overall, this research underscores the imperative for companies, especially startups and SMEs, to explore AI technology as a means to enhance productivity and gain a competitive edge.

The article analyzes international and national experiences in the use of artificial intelligence (AI) tools in education and outlines the legal and regulatory framework for their implementation. The need for developing clear rules and recommendations on the ethical applying of generative AI in academia is emphasized. Current approaches to integrating AI technologies into the educational process of design specialties are examined. The research explores contemporary trends in the application of artificial intelligence technologies in education, particularly within the Design field. The purpose of the study is to analyze the potential of artificial intelligence (AI), especially generative models, in developing a product design concept using the Method of Focal Objects (MFO). Methodology. The study employs a comprehensive approach that combines theoretical analysis of recent scientific publications, a comparative method, and experimental modeling of the ideation process using generative AI tools. The Method of Focal Objects is considered as an algorithmic basis for creating new visual and functional solutions, while AI functions as an analytical assistant in selecting, combining, and visualizing creative ideas. Results. The study revealed that the rapid development and dissemination of AI technologies, particularly generative models, significantly affect the structure, content, and methodology of the educational process. The key stages of integrating AI into the process of developing a design concept are identified: defining characteristics/ properties of auxiliary objects, selecting associative series, generating creative combinations, analyzing results, and constructing a design model. It is demonstrated that the use of AI reduces the time required for idea generation, expands the designer’s associative field, and enables the emergence of unexpected yet potentially functional solutions. A practical scheme of interaction between MFO and AI is proposed. It is concluded that the higher education system have to adapt its approaches to learning, teaching, and assessment, taking into account technological innovations and the principles of academic integrity. Scientific novelty. For the first time, the methodological interaction between the Method of Focal Objects and generative AI in product design has been substantiated. A model of the creative process is proposed, in which AI algorithms act as a catalyst for creative thinking rather than a substitute for the designer. An analogy between the cognitive principles of the Method of Focal Objects and the architecture of generative models has been identified. Practical relevance. The findings can be applied in the professional training of designers to develop creative thinking skills and effective collaboration with AI tools. The proposed approach contributes to optimizing the conceptual design process, fostering an individual creative style, and enhancing the competitiveness of design solutions in the digital era. The results can also be used to develop methodological recommendations for integrating AI tools into higher education curricula. The conclusions of the study support the improvement of teacher training for working with generative technologies and the development of educational programs aimed at fostering ethical, digital, and civic competencies among students.

Cultural competence is vital for nursing students providing patient-centered care. Generative artificial intelligence (AI) tools can enhance education in this area by promoting reflective learning and bias recognition. This discussion paper reviews literature on AI applications in nursing education aligned with cultural competence frameworks such as Leininger's Sunrise Model and Jeffreys' Cultural Competence and Confidence Model. Examples include AI-generated case studies, bias evaluation, medical language translation, and simulated patient communication. AI tools can supplement human instruction and clinical experiences in hybrid learning models. They offer interactive, personalized learning to improve communication and promote ethical care. Challenges include ensuring AI accuracy, mitigating bias, and avoiding over-reliance that could diminish critical thinking. Thoughtful AI integration can strengthen cultural humility and critical thinking. To maximize benefits, faculty development, ethical safeguards, and interdisciplinary collaboration are necessary, ensuring that empathy and human judgment remain central to nursing education.

With the increasing use of Generative Artificial Intelligence (AI) tools like ChatGPT and Bard, universities face challenges in maintaining academic integrity. This research investigates the impact of these tools on learning outcomes (factual knowledge, comprehension, critical thinking) in selected universities of Ghana's Upper East Region during the 2023-2024 academic year. The study specifically analyzes changes in student comprehension and academic integrity concerns when using Generative AI for content generation, research assistance, and summarizing complex topics. A mixed-methods approach was employed, combining qualitative data from interviews and open-ended questions with quantitative analysis of survey data and academic records. The research focuses on three institutions: C. K. Tedam University of Technology and Applied Sciences, Bolgatanga Technical University, and Regentropfen University College. A purposive sampling technique recruited 150 participants (50 from each university) who had used Generative AI tools. Key findings show that 72% of students reported improved understanding of course material through Generative AI use, yet 75% cited academic integrity as a primary concern. Quantitative analysis revealed a weak to moderate positive correlation (r = 0.45) between AI tool usage and improved grades, with variations depending on the specific AI tasks performed. Qualitative data highlighted concerns about overreliance on AI and its impact on critical thinking skills. This research contributes to the ongoing debate on AI's role in education by providing valuable insights for educators and policymakers worldwide. The findings suggest that while AI tools can enhance comprehension, ethical considerations and potential drawbacks related to critical thinking require careful attention. The study concludes with recommendations for integrating AI literacy programs, developing ethical guidelines, and implementing advanced plagiarism detection systems to harness the benefits of Generative AI while mitigating risks to academic integrity. Although specific to the Upper East Region of Ghana, these insights may be applicable to other educational systems with similar characteristics.

Artificial Intelligence (AI) is increasingly adopted by educational institutions, particularly as a generative AI (GenAI) tool for e‐learning. This study explores the effectiveness of using GenAI with engineering students at a leading university in Saudi Arabia and the Middle East. It aims to assess GenAI's impact in the College of Engineering and examine gender‐based differences in how students utilize AI as a learning tool. The study also investigates how students from different engineering majors utilize AI in their learning. To achieve this objective, an online survey with 15 questions was distributed to 403 engineering students to analyze their perceptions of AI adoption in education. The study employs two non‐parametric rank‐based statistical tests: the Mann–Whitney test to analyze gender differences, and the Kruskal–Wallis test to examine how various engineering disciplines such as industrial, electrical, mechanical, civil, chemical, nuclear, and mining engineering influence GenAI adoption. The findings reveal significant differences between male and female students in their experiences with GenAI, particularly regarding inaccurate or misleading responses, accurate and reliable responses, and their opinions regarding the users from applied academic field toward GenAI adoption. The results also indicate notable differences among engineering majors in their proficiency with GenAI features, their experiences with hallucinated responses, their views on using GenAI in theoretical disciplines, and their trust in the accuracy of information provided by ChatGPT. These findings support educational decision‐makers in integrating AI as a learning technology for engineering students and in understanding student engagement with AI tools in education.

Current challenges have accelerated the implementation of modern business concepts. Among the many practices of continuous business processes improvement is digitalization. Attention is focused on the benefits of digitalization in companies, which is to improve the processes quality, reduce their passage time, quickly fulfil orders, and hence increase customer loyalty. The concept of artificial intelligence is analysed, its three main types are identified: artificial narrow intelligence, general artificial intelligence, artificial superintelligence. Artificial narrow intelligence is focused on solving a narrowly defined, structured task; general artificial intelligence which is aimed at solving any problem, can respond to different environments and situations. Artificial superintelligence will be able to surpass people in absolutely everything, such as coping with creative tasks, decision-making and maintaining emotional relationships. The advantages of using artificial intelligence (accuracy in data processing, the ability to quickly analyse a large amount of information that will facilitate timely decision-making) are revealed. The main threats to the use of artificial intelligence (jobs disappearance, mass unemployment, loss of control over artificial intelligence – robots’ uncontrollability by humans) are also indicated. The most common technologies of artificial intelligence in enterprises (data science, machine learning, robotization) are considered. The business entities experience in the implementation of various artificial intelligence tools in operational activities, in the medical, legal, space, banking, educational spheres of activity, is presented. It was emphasized in the educational field that the annual increase in artificial intelligence is expected to reach 45% by 2030. It is also highlighted that artificial intelligence contributes to business development and global economic activity. The world's key players in the artificial intelligence market are considered, the top 10 world IT corporations are presented, the growth of their key performance indicators after the introduction of artificial intelligence technologies in goods and services is investigated.

Purpose: This analysis aims to propose guidelines for artificial intelligence (AI) research ethics in scientific publications, intending to inform publishers and academic institutional policies in order to guide them toward a coherent and consistent approach to AI research ethics.Methods: A literature-based thematic analysis was conducted. The study reviewed the publication policies of the top 10 journal publishers addressing the use of AI in scholarly publications as of October 2024. Thematic analysis using Atlas.ti identified themes and subthemes across the documents, which were consolidated into proposed research ethics guidelines for using generative AI and AI-assisted tools in scholarly publications.Results: The analysis revealed inconsistencies among publishers’ policies on AI use in research and publications. AI-assisted tools for grammar and formatting are generally accepted, but positions vary regarding generative AI tools used in pre-writing and research methods. Key themes identified include author accountability, human oversight, recognized and unrecognized uses of AI tools, and the necessity for transparency in disclosing AI usage. All publishers agree that AI tools cannot be listed as authors. Concerns involve biases, quality and reliability issues, compliance with intellectual property rights, and limitations of AI detection tools.Conclusion: The article highlights the significant knowledge gap and inconsistencies in guidelines for AI use in scientific research. There is an urgent need for unified ethical standards, and guidelines are proposed for distinguishing between the accepted use of AI-assisted tools and the cautious use of generative AI tools.

Human beings are endowed with a natural curiosity and creativity, which motivate them to learn new things from their interactions with the world. Human learning has involved exploration and experimentation, which have allowed humans to discover new facts and principles, and to invent new artifacts and systems. Human learning has also affected human evolution, both genetically and culturally, as humans have adjusted to different situations and demands in their environments. However, in the current world, human learning is largely facilitated by artificial intelligence (AI) tools, which are programs that can perform tasks that usually require human intelligence, such as comprehension, reasoning, problem-solving, and communication. AI tools can support humans in their learning endeavors, by giving them access to enormous amounts of information, and by delivering them customized and interactive assistance and feedback. AI tools can also amplify human creativity and innovation, by generating novel and diverse content, such as code, poems, essays, songs, and more. But what are the effects of this dependence on AI tools for human learning and evolution? Does it boost or diminish human curiosity and creativity? Does it enable or limit human autonomy and agency? Does it foster or hamper human diversity and collaboration? These are some of the questions that this topic will explore, by evaluating the pros and cons of using AI tools for human learning, and the ethical and social issues that arise from this phenomenon. [28] Today when we look around us we observe the advancement in technology has brought a lot of comfort to our lives in terms of traveling, education, or enjoying content virtually. [29] Talking about our basic requirements, technology has become so friendly that we can learn everything through E-Learning. Everyone only wondered about having an AI which will help in making our lives easy. The latest concept in terms of AI which is widely received and accepted by the people everywhere around the Globe is the Open AI that is Chat Gpt, Gemini, Copilot. All of these AI helps us in decision making or cutting our chase short for finding solutions for either lengthy solutions like writing a summary related to something or Questions which are easy to solve but difficult to look for solutions. About a quarter (27%) of Americans say they interact with artificial intelligence almost constantly or several times a day. Artificial intelligence (AI) is used in a variety of ways, including online product recommendations, facial recognition software and chatbots. One in six (17%) adults reported that they can often or always recognise when they are using AI, one in two (50%) adults reported that they can some of the time or occasionally recognise when they are using AI, one in three (33%) adults reported that they can hardly ever or never recognise when they are using AI. [26] In this project we are testing the dependence upon the recently emerged Open AI tools such as ChatGPT, Google Bard, Bing. Our motive is to find out whether people are using these powerful tools to help in their academics or other tasks only or do they take advice from these tools in their financial planning as well.

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

In the dynamic field of programming education, integrating artificial intelligence (AI) tools has started to play a significant role in enhancing learning experiences. This paper presents a case study conducted during a foundational programming course for first-year students in higher education, where students were encouraged to utilize generative artificial intelligence programming copilot extensions in their programming IDE and browser-based generative AI tools as supportive AI tools. The primary objective was to observe the impact of AI on the learning curve and the overall educational experience.Key findings suggest that the introduction of AI tools significantly altered the learning experience for students. Many who initially struggled with grasping elementary programming concepts found that AI support made understanding basic programming concepts much easier, enhancing their confidence and skills. This was particularly evident in the reduced levels of anxiety typically associated with early programming learning, as the AI copilot provided a non-judgmental, always-available source for clarifying doubts, including queries that students might hesitate to ask in a traditional classroom setting.Notably, some students leveraged the AI to generate similar exercise problems, reinforcing their understanding and skills. The AI's capability to address basic queries also freed up the instructor's time, allowing for more personalized student guidance in more advanced problems. This shift in the instructional dynamic further contributed to a learning environment where students felt more comfortable engaging with complex topics, thereby reducing the psychological barriers often linked with early-stage programming education.The course's structure, enriched by AI, enabled students to delve into more complex programming constructs earlier than traditional curricula would allow. For instance, students were tasked with simulating basic e-commerce operations, such as user registration, product browsing, and cart functionalities. These practical challenges naturally introduced advanced concepts like external data storage, unit testing, and user interface design, which are typically reserved for more advanced courses. With the help of generative AI programming copilot tools, students at any programming skill level were able to develop nearly functional complex structures. Interestingly, even when their projects were not fully functional, students remained motivated. Instead of feeling discouraged by these imperfect outcomes, they showed resilience and a keen interest in understanding and improving their code. This reaction is a significant shift from traditional learning settings, where unfinished or flawed projects often lead to increased anxiety or a drop in motivation.Furthermore, the AI's proactive suggestions inspired students to explore beyond the curriculum. Advanced learners delved into databases, cryptography libraries in Python, and even more advanced user interface design, ensuring that they remained engaged and challenged. This elementary course, enhanced by generative AI tools, also inspired students to learn other programming languages since they now learned that individual learning is more available with the aid of generative AI.In conclusion, the integration of AI in programming education offers a promising avenue for enhancing both the learning experience and outcomes. This case study underscores the potential of AI to revolutionize traditional teaching methodologies, fostering a more dynamic, responsive, and inclusive learning environment.This paper handles the results, possibilities and challenges of AI empowered education in programming. It also gives practical examples as well as future research perspectives.

Purpose: This article examines the impact of artificial intelligence (AI) on the evolution of business models in the video game industry. The main objective is to fill the research gap concerning how AI integration not only generates new business models but also modifies existing ones, influencing companies’ strategies and players’ experiences. The study addresses the tension between innovation and risk, showing how AI adoption reshapes value creation, delivery, and capture within this creative sector. Design/methodology/approach: The research combines a systematic literature review (SLR) of 56 Scopus-indexed publications with qualitative empirical analysis. A comparative case study approach was adopted, focusing on Ubisoft, which actively experiments with generative AI, and CD Projekt, which deliberately limits AI use. The criteria of analysis included strategic goals, the degree of creative control retained by developers, and the legal and ethical challenges of AI-driven innovation. Findings: The results reveal that AI enables cost optimization, higher player retention through personalization, and novel monetization models such as NFTs and dynamic pricing. Ubisoft uses AI tools like Ghostwriter and NEO NPC to automate NPC dialogues and create dynamic interactions, enhancing immersion while maintaining authorial oversight. In contrast, CD Projekt refrains from integrating generative AI into flagship projects like The Witcher 4, citing copyright and quality concerns, and applies AI only in supportive functions such as testing or voice reconstruction. These contrasting strategies show that AI’s impact on business models depends on technological choices and risk tolerance. Research limitations/implications: The study is limited to global trends and two cases; future research should include regional and ethical perspectives. Practical implications: Game developers can leverage AI to optimize processes and retention, but must apply legal audits and ethical safeguards. Social implications: AI expands gaming as a social and cultural space but raises concerns about manipulation, copyright, and creativity. Originality/value: The study integrates theory and practice, showing how contrasting AI strategies transform business models and offering a framework for future industry adoption.

The use of Generative Artificial Intelligence (AI) tools in international commercial arbitration reveals a complex intersection with the potential risk of confidential data breaches. Adopting a doctrinal research approach, this research article analyses the legal and regulatory framework applicable to ensure responsible and ethical uses of AI so as to protect confidentiality in international arbitration. This article argues that the use of AI in international arbitration has brought in a new age of efficiency and accuracy in international arbitration, but it also raises concerns on the protection of confidentiality as third-party owned AI tools and systems are prone to a potential risk of confidential data breaches and confidentiality violations on volumes of data stored together in AI tools. The application of the guidelines and principles on the use of AI in international arbitration as well as emerging regulations and laws on AI have varied approaches that are either discretionary or only play a guiding role on the protection of confidential information in international arbitration. Ultimately, this article recommends that it is imperative for the upcoming versions of institutional arbitration rules to enhance the confidentiality obligations in arbitration proceedings with a focus on the integration of AI tools. Alternatively, with the use of confidentiality orders, arbitration participants must ensure that appropriate safeguards are in place to ensure that confidentiality is a core consideration from the initial stages of deploying AI tools. Confidentiality by design could also be applied in Generative AIs used by law firms, arbitral tribunals or institutions.

Performance of an artificial intelligence tool with real-time clinical workflow integration - Detection of intracranial hemorrhage and pulmonary embolism.

The rapid advancements in artificial intelligence (AI), particularly generative AI tools like ChatGPT, have transformed language education, fostering opportunities and challenges in English as a Second Language (ESL) learning. The growth of artificial intelligence (AI) and incredibly generative AI tools like ChatGPT in language education have widely affected the landscape of ESL students’ teaching and learning experiences. Despite the advancement of AI technology, limited research has been conducted on the impact of AI on a more profound cognitive impact, particularly on students’ ability to think critically and independently. This scoping review examines how AI influences ESL learners’ critical thinking and writing skills. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this study synthesizes findings from 15 peer-reviewed articles published between 2023 and 2025 to answer the research questions: How does AI affect critical thinking development? Moreover, 2) What are the ethical concerns resulting from AI-assisted learning? This study conducted a systematic literature review (SLR) to discover in-depth insights on the over-reliance on generative AI techniques and the development of critical thinking skills in the context of ESL writing to contribute to the existing literature. Results indicate that while AI significantly enhances writing quality and critical thinking skills, excessive reliance on AI tools may hinder independent learning. The study calls for AI literacy programs to ensure AI’s effective and ethical integration into ESL education.