This study explored the effects of interacting with ChatGPT 4.0 on L2 learners’ motivation to write English argumentative essays. Conducted at a public university in a non-English-speaking country, the study had an experimental and mixed-methods design. It utilized both quantitative and qualitative data analyses to inform the development of effective AI-enhanced tailored interventions for teaching L2 essay writing. Overall, the results revealed that interacting with ChatGPT 4.0 had a positive lasting effect on learners’ motivation to write argumentative essays in English. However, a decline in their motivation at the delayed post-intervention stage suggested the need to maintain a balance between utilizing ChatGPT as a writing support tool and enhancing their independent writing capabilities. Learners attributed the increase in their motivation to several factors, including their perceived improvement in essay writing skills, the supportive learning environment created by ChatGPT as a tutor, positive interactions with it, and the development of meta-cognitive awareness by addressing their specific writing issues. The study highlights the potential of AI-based tools in enhancing L2 learners’ motivation in English classrooms.

The place Generative AI (Gen AI) has within education and schooling has been subject to much scrutiny. Its ever-evolving and growing nature has left many educators and other stakeholders scrambling with questions about how to adapt its approach, methodology and place within the classroom. Gen AI has also been shown to have particularly efficacy in the area of Classical languages teaching. It also has challenges (Ross, 2023). The following paper explores a proactive approach to utilising Gen AI technology and programs within a Latin classroom NESA Stage 4-5/ MYP Years 1-3 in Australia (ages 11-16) (NESA: New South Wales Education Standards Authority. MYP: Middle Years Programme). It also develops some approaches to facilitate students’ reflection so as to improve their understanding of the uses and abuses of Gen AI platforms in their own learning.

This article discusses European copyright law as applied to the development and training of generative AI and natural language processing in public interest research institutions and libraries. The article focuses on the scope of the new exceptions from copyright law for text and data mining (TDM) for research purposes and discusses them from the perspective of research ethics and principles of open science in publicly financed research. The public interest mission of research institutions and libraries includes the open dissemination of research results but the exceptions from copyright are focused only on the training phase in AI development. Regulation on data transparency is fragmented. The article finds that while new exceptions open for developing language models under research institutions and libraries’ public interest mission to preserve national languages, the regulation is not adapted to principles of research ethics and open science, and legal uncertainty remains.

Standards complement regulation as frameworks for Artificial Intelligence governance. Within the European Union, this complementarity is laid down as the New Legislative Framework. Standards can be harmonised to provide a presumption of conformity with regulation. They draw legitimacy from the inclusion of all relevant stakeholders as well as the consensus principle although there are limitations in practice. At both European and international levels, standardisation for generative AI is still in its infancy due to standardisation following relying on a level of technical maturity. Therefore, most activity is currently seen in the policy domain. Potential directions for future generative AI standards are suggested. Generative AI drives the need for non-AI standards, too, especially in areas of digital trust and digital identity.

This article addresses a critical gap in international research concerning digital literacies and empowerment among adults who are English as an additional language (EAL) learners. In the Australian context, where digital communication and services are embedded in all aspects of life and work, proficiency in digital literacies, including advanced technologies like generative artificial intelligence (AI), is vital for working and living in Australia. Despite the increasing prevalence and significance of generative AI platforms such as ChatGPT, there is a notable absence of dedicated programs to assist EAL learners in understanding and utilising generative AI, potentially impacting their employability and everyday life. This article presents findings from a larger study conducted within training providers, spanning adult educational institutions nationwide. Through analysis of data gathered from surveys and focus groups, the article investigates the knowledge and attitudes of students, educators, and leaders regarding integrating generative AI into the learning program for adult EAL learners. The results reveal a hesitance among educators, particularly concerning beginning language learners, in incorporating generative AI into educational programs. Conversely, many adult learners demonstrate enthusiasm for learning about its potential benefits despite having limited understanding. These disparities underscore the pressing need for comprehensive professional development for educators and program leaders. The findings also highlight the need to develop the AI literacy of learners to foster their understanding and digital empowerment. The article concludes by advocating for a systemic approach to include generative AI as an important part of learning programs with students often from adult migrant and refugee backgrounds.

Several criminal offenses can originate from or culminate with the creation of content. Sexual abuse can be committed by producing intimate materials without the subject’s consent, while incitement to violence or self-harm can begin with a conversation. When the task of generating content is entrusted to artificial intelligence (AI), it becomes necessary to explore the risks of this technology. AI changes criminal affordances because it creates new kinds of harmful content, it amplifies the range of recipients, and it can exploit cognitive vulnerabilities to manipulate user behavior. Given this evolving landscape, the question is whether policies aimed at fighting Generative AI-related harms should include criminal law. The bulk of criminal law scholarship to date would not criminalize AI harms on the theory that AI lacks moral agency. Even so, the field of AI might need criminal law, precisely because it entails a moral responsibility. When a serious harm occurs, responsibility needs to be distributed considering the guilt of the agents involved, and, if it is lacking, it needs to fall back because of their innocence. Thus, legal systems need to start exploring whether and how guilt can be preserved when the actus reus is completely or partially delegated to Generative AI.

The Paper aims to explore whether the current ecosystem of existing and still-to-be adopted rules on artificial intelligence (AI) systems in the European Union does fully and adequately address the liability for damages caused by Generative AI system. It maps first and primarily the distinctive features and functional characteristics of Generative AI likely to impact on regulatory and legal considerations and, in particular, on determining the specific regulatory regime governing Generative AI and on tracing and allocating liability along the value chain pursuant to the AI Act. On the basis of this mapping exercise, the Paper focuses on testing the liability rules as provided for the draft Artificial Intelligence Liability Directive and the Revised Product Liability Directive and assessing their sufficiency and effectiveness in the face of Generative AI. Beyond the assessment of the rules laid down in the above-referred texts, the Paper briefly describes other liability scenarios to be explored in future works.

The rapid development of generative artificial intelligence (AI) systems, particularly those fuelled by increasingly advanced large language models, has raised concerns of their potential risks among policymakers globally. In July 2023, Chinese regulators enacted the Interim Measures for the Management of Generative AI Services (“the Measures”). The Measures aim to mitigate various risks associated with public-facing generative AI services, particularly those concerning information content safety and security. China’s approach to regulating AI to date has sought to address the risks associated with rapidly advancing AI technologies while fostering innovation and development. Tensions between these policy objectives are reflected in the provisions of the Measures. As Beijing moves towards establishing a comprehensive legal framework for AI governance, there will be growing interest in how China’s approach may influence AI governance and regulation at a global level.

Large or “foundation” models are now being widely used to generate not just text and images but also video, music and code from prompts. Although this “generative AI” revolution is clearly driving new opportunities for innovation and creativity, it is also enabling easy and rapid dissemination of harmful speech and potentially infringing existing laws. Much attention has been paid recently to how we can draft bespoke legislation to control these risks and harms; however, private ordering by generative AI providers, via user contracts, licenses and privacy policies, has so far attracted less attention. Drawing on the extensive history of study of the terms and conditions (T&C) and privacy policies of social media companies, this paper reports the results of pilot empirical work conducted in January–March 2023, in which T&C were mapped across a representative sample of generative AI. With the focus on copyright and data protection, our early findings indicate the emergence of a “platformisation paradigm,” in which providers of generative AI attempt to position themselves as neutral intermediaries. This study concludes that new laws targeting “big tech” must be carefully reconsidered to avoid repeating past power imbalances between users and platforms.

This paper explores the regulatory awakening regarding generative AI (GenAI) in the United States and European Union (EU) institutions with the release of ChatGPT. Based on a thematic analysis of regulatory documents, it investigates how governments have approached the deployment and use of this emerging technology within their classic government activities. The analysis shows several layers of regulatory approaches, ranging from command-and-control to an experimental approach, combined with risk- and management-based approaches. It also reveals different perspectives. The EU institutions have notably adopted more restrictive guidelines on the use of publicly available Large Language Models (LLMs) - a type o GenAI that is trained on vast amounts of text data to understand, generate, and respond in human-like language. This approach reflects greater caution about data security and confidentiality and the risks of foreign interference. However, the American and EU documents share a common concern about the risk of reinforcing discrimination and the protection of human rights. Interestingly, considering the administrative environment, neither the administrative activities in which GenAI may be used nor the key legal principles embedded by the rule of law are explicitly used for guiding administration in their development and use of GenAI. In this context, the paper calls for future research that could help contribute to the renewal of administrative law theory in the context of the digital transformation of public administration.

Generative artificial intelligence (AI) has catapulted into the legal debate through the popular applications ChatGPT, Bard, Dall-E and others. While the predominant focus has hitherto centred on issues of copyright infringement and regulatory strategies, particularly in the context of the AI Act, a critical but often overlooked issue lies in the friction between generative AI and data protection laws. The rise of these technologies highlights unresolved tension between safeguarding fundamental protection rights and and the vast, almost universal, of scale of data processing required for machine learning. Large language models, which scrape nearly the whole Internet rely on and may even generate personal data falling under the GDPR. This tension manifests across multiple dimensions, encompassing data subjects’ rights, the foundational principles of data protection and the fundamental categories of data protection. Drawing on ongoing investigations by data protection authorities in Europe, this paper undertakes a comprehensive analysis of the intricate interplay between generative AI and data protection within the European legal framework.

With recent leaps in large language model technology, conversational AI offer increasingly sophisticated interactions. But is it fair to say that they can offer authentic relationships, perhaps even assuage the loneliness epidemic? In answering this question, this essay traces the history of AI authenticity, historically shaped by cultural imaginations of intelligent machines and human communication. The illusion of human-like interaction with AI has existed since at least the 1960s, when the term “Eliza effect’ was named after the first chatbot Eliza. Termed a “crisis of authenticity” by sociologist Sherry Turkle, the Eliza effect has stood for fears that AI interactions can undermine real human connections and leave users vulnerable to manipulation. More recently, however, researchers have begun investigating less anthropomorphic definitions of authenticity. The expectation - and perhaps fantasy - of authenticity stems, in turn, from a much longer history of technologically mediated communications, dating back to the invention of the telegraph in the nineteenth century. Read through this history, the essay concludes that AI relationships might not mimic human interactions but must instead acknowledge the artifice of AI, offering a new form of companionship in our mediated, often lonely, times.

Can trust norms within the African moral system support data gathering for Generative AI (GenAI) development in African society? Recent developments in the field of large language models, such as GenAI, including models like ChatGPT and Midjourney, have identified a common issue with these GenAI models known as “AI hallucination,” which involves the presentation of misinformation as facts along with its potential downside of facilitating public distrust in AI performance. In the African context, this paper frames unsupportive data-gathering norms as a contributory factor to issues such as AI hallucination and investigates the following claims. First, this paper explores the claim that knowledge in the African context exists in both esoteric and exoteric forms, incorporating such diverse knowledge as data could imply that a GenAI tailored for Africa may have unlimited accessibility across all contexts. Second, this paper acknowledges the formidable challenge of amassing a substantial volume of data, which encompasses esoteric information, requisite for the development of a GenAI model, positing that the establishment of a foundational framework for data collection, rooted in trust norms that is culturally resonant, has the potential to engender trust dynamics between data providers and collectors. Lastly, this paper recommends that trust norms in the African context require recalibration to align with contemporary social progress, while preserving their core values, to accommodate innovative data-gathering methodologies for a GenAI tailored to the African setting. This paper contributes to how trust culture within the African context, particularly in the domain of GenAI for African society, propels the development of Afro-AI technologies.

Generative artificial intelligence (GenAI) has gained significant popularity in recent years. It is being integrated into a variety of sectors for its abilities in content creation, design, research, and many other functionalities. The capacity of GenAI to create new content—ranging from realistic images and videos to text and even computer code—has caught the attention of both the industry and the general public. The rise of publicly available platforms that offer these services has also made GenAI systems widely accessible, contributing to their mainstream appeal and dissemination. This article delves into the transformative potential and inherent challenges of incorporating GenAI into the domain of judicial decision-making. The article provides a critical examination of the legal and ethical implications that arise when GenAI is used in judicial rulings and their underlying rationale. While the adoption of this technology holds the promise of increased efficiency in the courtroom and expanded access to justice, it also introduces concerns regarding bias, interpretability, and accountability, thereby potentially undermining judicial discretion, the rule of law, and the safeguarding of rights. Around the world, judiciaries in different jurisdictions are taking different approaches to the use of GenAI in the courtroom. Through case studies of GenAI use by judges in jurisdictions including Colombia, Mexico, Peru, and India, this article maps out the challenges presented by integrating the technology in judicial determinations, and the risks of embracing it without proper guidelines for mitigating potential harms. Finally, this article develops a framework that promotes a more responsible and equitable use of GenAI in the judiciary, ensuring that the technology serves as a tool to protect rights, reduce risks, and ultimately, augment judicial reasoning and access to justice.

This study explores the integration of generative artificial intelligence (GenAI) in informal digital learning of English (IDLE) practices, focusing on its potential to enhance language learning outcomes and addressing the technological challenges language teachers face in utilising AI-based tools to facilitate second language acquisition. Based on the research context of IDLE and holistic learning ecology and drawing on the theoretical frameworks of technological pedagogical and content knowledge and social cognitive theory, we performed a mixed-methods investigation with an empirical experiment to assess the effectiveness of GenAI followed by semi-structured interviews. The results suggest that the GenAI-mediated IDLE practices effectively improve college students’ oral proficiency in English from both technological and humanistic perspectives. However, results also indicate that the GenAI conversational partner alone is not adequate to provoke continuous extramural GenAI-mediated IDLE practices. We discuss the theoretical and pragmatic significance of GenAI-mediated IDLE in educational equity and reformation.

This paper is written at a tipping point in the development of generative AI and related technologies and services, which heralds a new battleground between humans and computers in the shaping of reality. Large language models (LLMs) scrape vast amounts of data from the so called ‘publicly available' internet, enabling new ways for the past to be represented and reimagined at scale, for individuals and societies. Moreover, generative AI changes what memory is and what memory does, pushing it beyond the realm of individual, human influence, and control, yet at the same time offering new modes of expression, conversation, creativity, and ways of overcoming forgetting. I argue here for a ‘third way of memory’, to recognise how the entanglements between humans and machines both enable and endanger human agency in the making and the remixing of individual and collective memory. This includes the growth of AI agents, with increasing autonomy and infinite potential to make, remake, and repurpose individual and collective pasts, beyond human consent and control. This paper outlines two key developments of generative AI-driven services: firstly, they untether the human past from the present, producing a past that was never actually remembered in the first place, and, secondly, they usher in a new ‘conversational’ past through the dialogical construction of memory in the present. Ultimately, developments in generative AI are making it more difficult for us to recognise the human influence on, and pathways from, the past, and that human agency over remembering and forgetting is increasingly challenged.

Despite the rapid advancement of generative Large Language Models (LLMs), there is still limited understanding of their potential impacts on engineering design (ED). This study fills this gap by collecting the tasks LLMs can perform within ED, using a Natural Language Processing analysis of 15,355 ED research papers. The results lead to a framework of LLM tasks in design, classifying them for different functions of LLMs and ED phases. Our findings illuminate the opportunities and risks of using LLMs for design, offering a foundation for future research and application in this domain.

Generative design is now a core capability of today's CAD systems enabling broader exploration of the design space. This paper presents a desktop review of 15 tools with the aim of characterising where the tools are used in the design process and to what extent they can be considered creative.The results showed that, despite their increasing capabilities, GD tools are unable to exhibit all phases of creativity and cannot be applied in all stages of the design process. This highlights the criticality of the human in design providing opportunities for GD to augment designer creativity.