Determinants of Chinese Pre-service Teachers’ Acceptance towards Generative Artificial Intelligence: A Multi-Group SEM-ANN Analysis

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

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

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

We may have to soon decide what kind of Artificial General Intelligence (AGI) computers we will build and how they will coexist with humans. Many predictions estimate that artificial intelligence will surpass human intelligence during this century. This poses a risk to humans: computers may cause harm to humans either intentionally or unintentionally. Here we outline a possible democratic society structure that will allow both humans and artificial general intelligence computers to participate peacefully in a common society.There is a potential for conflict between humans and AGIs. AGIs set their own goals which may or may not be compatible with the human society. In human societies conflicts can be avoided through negotiations: all humans have the about the same world view and there is an accepted set of human rights and a framework of international and national legislation. In the worst case, AGIs harm humans either intentionally or unintentionally, or they can deplete the human society of resources.So far, the discussion has been dominated by the view that the AGIs should contain fail-safe mechanisms which prevent conflicts with humans. However, even though this is a logical way of controlling AGIs we feel that the risks can also be handled by using the existing democratic structures in a way that will make it less appealing to AGIs (and humans) to create conflicts.The view of AGIs that we use in this article follows Kantian autonomy where a device sets goals for itself and has urges or drives like humans. These goals may conflict with other actors’ goals which leads to a competition for resources. The way of acting and reacting to other entities creates a personality which can differ from AGI to AGI. The personality may not be like a human personality but nevertheless, it is an individual way of behaviour.The Kantian view of autonomy can be criticized because it neglects the social aspect. The AGIs’ individual level of autonomy determines how strong is their society and how strongly integrated they would be with the human society. The critic of their Kantian autonomy is valid, and it is here that we wish to intervene.In Kantian tradition, conscious humans have free will which makes them morally responsible. Traditionally we think that computers, like animals lack free will or, perhaps, deep feelings. They do not share human values. They cannot express their internal world like humans. This affects the way that AGIs can be seen as moral actors. Often the problem of constraining AGIs has used a technical approach, placing different checks and designs that will reduce the likelihood of adverse behaviour towards humans. In this article we take another point of view. We will look at the way humans behave towards each other and try to find a way of using the same approaches with AGIs.

To emphasize the journal’s concern with AGI safety, we inaugurate Artificial General Intelligence (AGI) by focusing the first issue on Risks, Governance, and Safety &amp; Alignment Methods. Risks The AI Risk Repository: A Comprehensive Meta-Review, Database, and Taxonomy of Risks from Artificial Intelligence The most comprehensive AI risk taxonomy — 777 specific risks classified into 43 categories — to date has been created by workers collaborating from a half-dozen institutions. We except 11 key pages from the original 79-page report. Their ‘living’ Repository is online and free to download and share. The authors’ intention is to provide a common frame of reference for AI risks. Slattery et al.’s set of ~100 references is excellent and thorough. Thus, pouring through this study for your own specific interest is an efficient way to get on top of the entire current AI risk literature. The highest of their three taxonomy levels, the Causal Taxonomy, is categorized according to the cause of the risk, Human or AI the intention , Intentional action or Unintentional, and timing — Pre-deployment of the AI system or Post-deployment. The Causal Taxonomy can be used “for understanding how, when, or why risks from AI may emerge.” They also call readers’ attention to the AI Incident Database.[1] The Incident Database publishes a monthly roundup here. AI Risk Categorization Decoded (AIR 2024) By examining 8 government and 16 corporate AI risk policies, Zeng et al. seek to provide an AI risk taxonomy unified across public and private sector methodologies. They present 314 risk categories organized into a 4-level hierarchy. The highest level is composed of System &amp; Operational Risks, Content Safety Risks, Societal Risks, and Legal &amp; Rights Risks. Their first takeaway from their analysis is more categories is advantageous, allowing finer granularity in identifying risks and unifying risk categories across methodologies. Thus, indirectly they argue for the Slattery et al. taxonomy with double the categories. This emphasis on fine granularity parallels a comment made to me by Lance Fortnow, Dean of Illinois Institute of Technology College of Computing, on the diversity and specificity of human laws indicating a similar diversity may be necessary to assure AGI safety, and that recent governance proposals may be simplistic. Indeed, Zeng et al.’s second takeaway is that government AI regulation may need significant expansion. Few regulations address foundation models, for instance. And their third takeaway is that comparing AI risk policies from diverse sources is extremely helpful to develop an overall grasp of the issues – how different organizations conceptualize risk, for instance – and how to move toward international cooperation to manage AI risk. AIR-Bench 2024: A Safety Benchmark Based on Risk Categories from Regulations and Policies Applying the work just described above, Zeng et al. constructed an AI safety benchmark aligned with their unified view of private and public sector AI risk policy and specifically targeting the gap in regulation of foundation models they uncovered. They develop and test nearly 6000 risky prompts and find inconsistent responses across foundation models. Zeng et al. give examples of foundation model safety failures in response to various prompts. This work seems a significant advance toward an AGI safety certification conducted by an AI industry consortium or an insurance company consortium along the lines of, e.g., UL Solutions (previously Underwriters’ Laboratory). A Comprehensive Survey of Advanced Persistent Threat Attribution We wanted to publish this important article had to pull it due to a license conflict – please see their arXiv preprint. APT [Advanced Persistent Threat] attacks are attack campaigns orchestrated by highly organized and often state-sponsored threat groups that operate covertly and methodically over prolonged periods. APTs set themselves apart from conventional cyber-attacks by their stealthiness, persistence, and precision in targeting. This systematic review by Rani et al. of 137 papers focuses on the increasing development of automated means to detect AI and ML APTs early and identify the malevolent actors involved. They present the Automated Attribution Framework, which consists of 1) collecting the training data of past attacks, 2) preprocessing and enrichment of the training data, 3) the actual training and pattern recognition on the data, and 4) attribution — applying the trained models to identify the malevolent perpetrating actors. The open research questions summarized by Rani et al. lead toward AI taking an increasing role in APT attribution. Governance Excerpts from Aschenbrenner, Situational Awareness I was pointed to Leopold Aschenbrenner’s 165-page missive by Scott Aaronson’s blog, which said he knew Leopold during his sabbatical at OpenAI and recommended people give it a read and take it seriously. The essence of it is that if we extrapolate from recent AI progress, we will have AGI by 2030, and therefore, for national security, a Manhattan Project-style national AI effort, including nationalizing leading private AGI labs, should be mounted. Here we reprint his Part IV, “The Project,” advocating this controversial effort and describing his vision of how it will occur. I recommend anyone concerned about the dangers of AGI, and especially those working toward AGI, read Aschenbrenner’s entire book. Take a look at the Table of Contents preceding our reprint of “The Project.” And we reprint his Ch. V, “Parting Thoughts,” in our Commentary section. Soft Nationalization: How the US Government Will Control AI Labs Aschenbrenner advocates nationalizing leading AI labs into a high-security, top-secret, US federal government project. OK, how, exactly? A perfect complement to Aschenbrenner’s thoughts is given by Deric Cheng and Corin Katzke of Convergence Analysis. They examine how AGI R&amp;D nationalization could happen realistically, effectively, and efficiently. Their report outlines key issues and initial thoughts as a prelude to their own and others’ detailed proposals to come. It is a beautiful piece of work, IMHO. It is not impossible for private companies to develop AGI responsibly and securely, but the main goal of this journal is to make AGI safety the central debate in the AGI community, and the nationalized, Manhattan-style project point of view must be presented. Further, I find Aschenbrenner’s arguments to be persuasive and Cheng and Katzke’s thoughtful outline of how nationalization could actually occur to be convincing, e.g. (pg. 8): The US may be able to achieve its national security goals with substantially less overhead than total nationalization via effective policy levers and regulation… We argue that various combinations of the policy levers listed below will likely be sufficient to meet US national security concerns, while allowing for more minimal governmental intrusion into private frontier AI development. Acceptable Use Policies for Foundation Models Acceptable use policies are legally binding policies that prohibit specific uses of foundation models. Klyman surveys acceptable use policies from 30 developers encompassing 127 specific use restrictions cited in 184 articles. Like Zeng et al. in “AI Risk Categorization Decoded (AIR 2024),” Klyman highlights the inconsistent number and type of restrictions across developers and lack of transparency behind their motivation and enforcement, indicating the need to for developers to create a unified consensus acceptable use policy. The general motivations are to reduce legal and reputational risk. However, standing in the way of developers working to create a unified policy set is the motivation to use restrictions to hinder competition from using proprietary models. Enforcement can also hinder effective use of a foundation model. Acceptable use policies can be categorized into content restrictions (e.g. the top 4: misinformation, harassment, privacy, discrimination) and end use restrictions, e.g. Anthropic’s restriction on “model scraping,” which is someone training their own AI model on prompts and outputs from Anthropic’s model. Another use restriction is scaling up AI-created content distribution such as automated online posting. As with the Zeng et al. articles, Klyman’s article points the way to create a homogeneous acceptable use policy across a diverse AI ecosystem. Steve Omohundro comments: “…the AI labs’ ‘alignment work’ … is all about the AIs rather than their impact on the world. For goodness sake, the Chinese People's Liberation Army has already fine-tuned Meta's Llama 3.1 to promote Chinese military goals! And Meta's response was ‘that's contrary to our acceptable use policy!’" From the article: Without information about how acceptable use policies are enforced, it is not obvious that they are actually being implemented or effective in limiting dangerous uses. Companies are moving quickly to deploy their models and may in practice invest little in establishing and maintaining the trust and safety teams required to enforce their policies to limit risky uses. Safety Methods Benchmark Early and Red Team Often (Executive Summary excerpt) Two leading methods for uncovering AI safety breaches are 1) inexpensive benchmarking against a standardized test suite, such as prompts for large language models, and 2) longer, higher-cost but more informative intensive, interactive testing by human domain experts (“red-teaming”). Barrett et al., from the UC Berkeley Center for Long-Term Cybersecurity, advocate for this two-pronged approach indicated by the article title. They analyze the methods’ potential for eliminating LLM “dual” use, i.e. corrupting LLMs into creating chemical, biological, radiological, nuclear (CBRN) or cyber or other weaponry or attacks, but the methods apply to less dangerous risk testing as well. Essentially Barrett et al. advocate frequent use of benchmarks until a model attains a high safety score, followed by intensive red-teaming to test the model in more depth and yield more accuracy. Their paraphrase of the article title is: Benchmark Early and Often, and Red-Team Often Enough. Against Purposeful Artificial Intelligence Failures A paper that had to be written, and not surprisingly was, by Yampolskiy, who has sought to cover every aspect of AGI risks, is one arguing that intentionally triggering an AI disaster should not be entertained as an option to alert humanity to the danger of AGI. Models That Prove Their Own Correctness Especially in light of Dalrymple et al.’s governance proposal, Toward Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems, ‘models that prove their own correctness’ seems especially desirable, if not essential. Dalrymple et al. call for 1) a world model, 2) a safety specification, and 3), a means to verify the safety specification, a highly intriguing proposal, but which falls short of providing an example of such a model or means of verification (we hear that Dalrymple is working on an example). Paradise et al. describe two uses of interactive proof systems (IPS) combined with ML to allow a model to prove its own ‘correctness,’ as specified by the user of the model. The first method requires access to a training set of IPS transcripts (the sequence of interactions between the Verifier and Prover) in which the Verifier accepted the Prover’s probabilistic proof. The second method, Reinforcement Learning from Verifier Feedback (RLVF; note their intentional similarity to Reinforcement Learning from Human Feedback, RLHF) avoids the need for the accepted transcripts (which are in essence an external truth oracle) but only after training on such a verified transcript (its ‘base model’) using transcript learning. From then on it can generate its own emulated verified transcripts. The paper opens the door to other innovative applications of ML to IPS. This is a rather deep paper that requires further analysis to judge the realization of its promise. We look forward to a revised version after its peer review at an unspecified journal. We thank Syed Rafi for the pointer to the paper and Quinn Dougherty for inviting Orr Paradise to his safe AGI reading group. Language-Guided World Models: A Model-Based Approach to AI Control Model-based agents are artificial agents equipped with probabilistic “world models” that are capable of foreseeing the future state of an environment (Deisenroth and Rasmussen, 2011; Schmidhuber, 2015). World models endow these agents with the ability to plan and learn in imagination (i.e., internal simulation)…. Citing Dalrymple et al., Zhang et al. likewise extend the capabilities of world models to increase human control over AI. By adjusting the world model, humans can affect many context-sensitive policies simultaneously. However, for the human-AI interaction to be efficient, the world model must process natural language (NLP); hence, language-guided world models (LWMs). NLP also increases the efficiency of model learning by permitting them to read text. World models increase AI transparency, which NL interaction furthers by allowing humans to query models verbally. As an example, in Sec. 5.3, “Application: Agents that discuss plans with humans,” Zhang et al. describe an agent that uses its LWM to plan a task and then ask a human to review it for safety. Commentary Steve Omohundro, “Progress in Superhuman Theorem Proving?” Our co-founding editor Steve Omohundro is a strong proponent of Provably Safe AI, in which automated theorem-proving will play a major role.[2] Here Steve discusses current developments in using proof to lessen LLM hallucinations, the implications of superhuman theorem-proving for safe AGI and resources for interested readers. On Yampolskiy, “Against Purposeful Artificial Intelligence Failures” Topic Editor Jim Miller, Professor of Economics, Game Theory, and Sociology at Smith College, critiques Roman Yampolskiy’s argument against triggering a deliberate AI failure to wake the world up to AI dangers. Leopold Aschenbrenner, Situational Awareness, “Parting Thoughts” Aschenbrenner dismisses his critics as unrealistic and outlines the core tenets of “AI Realism.” Rowan McGovern, “Unhobbling Is All You Need?” Commentary on Aschenbrenner’s Situational Awareness McGovern questions Aschenbrenner’s fundamental assumption that “unhobbling” alone — “fixing obvious ways in which models are hobbled by default, unlocking latent capabilities and giving them tools, leading to step-changes in usefulness” — will result in his extrapolation of recent AI progress to predict the advent of AGI by 2030. McGovern: “Unhobbling conflates computing power with intelligence.” [1] https://incidentdatabase.ai/. “Like similar databases in aviation and computer security, the AI Incident Database aims to learn from experience so we can prevent or mitigate bad outcomes.” [2] Tegmark, M., &amp; Omohundro, S. (2023). Provably safe systems: the only path to controllable AGI. arXiv. Retrieved from https://arxiv.org/abs/2309.01933.

Purpose This study examines the integration of generative and predictive artificial intelligence (AI) models within smart cities, focusing on how user readiness and technology adoption influence their contribution to sustainable urban development and governance. Design/methodology/approach The study applies a systematic literature review following PRISMA guidelines and synthesizes evidence from 50 peer-reviewed studies (2018–2025) indexed in Scopus and Web of Science. It combines bibliometric mapping using VOSviewer with thematic analysis to examine the drivers, barriers and governance mechanisms shaping the adoption of generative, predictive and hybrid applications in urban contexts. Findings Generative AI fosters participatory engagement, citizen co-design and interactive simulations, advancing SDG 11 (Sustainable Cities and Communities) and SDG 4 (Quality Education) through enhanced digital literacy and inclusive planning. Predictive AI improves operational efficiency, forecasting accuracy and data-driven policymaking, supporting SDG 9 (Industry, Innovation and Infrastructure) and SDG 13 (Climate Action) by promoting sustainable resource use and climate-resilient management. Hybrid AI integrates these strengths, addressing both social and operational aspects of smart city development and aligning with SDG 17 (Partnerships for the Goals) through cross-sector collaboration and shared governance. Collectively, these models contribute to broader sustainability goals, including SDGs 3, 7 and 12. Research limitations/implications This review acknowledges several key limitations. Reliance on Scopus and Web of Science may exclude regionally significant or domain-specific studies not indexed in these databases. The focus on English-language publications introduces potential language bias, possibly overlooking relevant research from non-English-speaking regions. Restricting the timeframe to 2018–2025 captures recent developments but may omit earlier foundational work or the most recent studies not yet indexed. Differences in research design, policy contexts and sample characteristics also affect comparability and limit generalizability. Future research should broaden data sources, include multilingual literature and adopt mixed-methods and longitudinal approaches to enhance contextual diversity and empirical robustness. Practical implications The findings provide practical guidance for policymakers, urban planners and technology developers to design AI governance systems that are transparent, accountable and aligned with the SDGs. Integrating generative and predictive AI can enhance operational efficiency, support participatory planning and promote responsible decision-making. The findings inform the development of adaptive policy frameworks that advance SDG 9 (Industry, Innovation and Infrastructure), SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action) through digital literacy initiatives, cross-sector collaboration and data-informed management. Strengthening these practices enables cities to translate AI’s potential into tangible contributions to inclusive and sustainable urban transformation. Social implications Integrating user readiness and digital literacy into AI adoption is essential for building inclusive and trustworthy smart cities. These efforts support SDG 4 (Quality Education), SDG 10 (Reduced Inequalities) and SDG 16 (Peace, Justice and Strong Institutions). Generative AI encourages citizen participation and collaborative planning, while predictive AI improves service accessibility and data-informed governance. Promoting ethical awareness and community engagement helps narrow digital divides and address bias. Collectively, these elements advance SDG 11 (Sustainable Cities and Communities) and SDG 17 (Partnerships for the Goals) by fostering socially responsive and transparent AI-driven urban development. Originality/value This review is among the first to integrate perspectives on user readiness and technology adoption with comparative insights into generative and predictive AI in smart cities. It advances understanding of how AI-driven urban innovation supports inclusivity, efficiency and sustainability, while outlining policy directions and a future research agenda for equitable and transparent AI governance.

Adult learners are a neglected species in the generative artificial intelligence (GenAI) era. The sweeping changes brought by GenAI in the educational arena have implications for adult learning. GenAI in education will usher in a world of adult learning that will be radically different from its predecessor. However, how adult learners will apply GenAI technologies to achieve their educational and professional goals remains blurred. To address this gap, it is crucial to examine essential principles for integrating GenAI into adult learning. For effective digital transformation of education, GenAI should optimize adult learning and ensure the safety of adult learners. This study proposes a “GenAI adult learning ecology” framework (GenAI-ALE) for higher education institutions in this digital era permeated by GenAI. The GenAI-ALE considers eight (8) essential principles categorized into two main themes; institutional factors (GenAI curriculum design, GenAI divide, GenAI policy, GenAI ethics) and interpersonal factors (GenAI human-centered andragogy, GenAI literacy, GenAI interest, and GenAI virtual learning). Malcolm Knowles’ andragogical model is used to provide a context for integrating GenAI into adult learning. Applying the framework in a real-world context follows four iterative systematic steps; pre-perception and perception, GenAI readiness, assessment, and outcome. Reimagining new forms of adult learning in the GenAI revolution calls for higher education institutions to develop education systems where there is a synergy between humans (adult learners) and GenAI.

Purpose This conceptual paper examines what underlies decision-making in generative change processes to explore how generative artificial intelligence (GAI) might shape the future of organisational change. Design/methodology/approach This investigation draws on Snowden and Stanbridge's (2004) social complexity concept, Archer's (1995) morphogenetic/morphostatic explanatory methodology and Mackenzie and Bititci's (2023) social systems-based model for organisational change to explain how structure, culture and agency influence generative change processes in complex adaptive social systems. Findings Both human-based decision-making and machine-based decision-making have roles to play in generative change. This paper proposes that human reflexivity mediates ideas, whereas the material aspects of artificial intelligence (AI) and GAI mediate tasks. The former shapes the change interventions that take place and the latter contributes to their more effective execution. Practical implications In generative change processes, AI and GAI technologies should be focused on tasks that support human-based decision-making. Originality/value This paper explores decision-making in organisational change from a social complexity perspective and identifies the complementary roles of human reflexivity and AI and GAI materiality in delivering emergent outcomes.

Presently, generative AI has taken center stage in the news media, educational institutions, and the world at large. Machine learning has been a decades-old phenomenon, with little exposure to the average person until very recently. In the natural world, the oldest and best example of a “generative” model is the human being - one can close one’s eyes and imagine several plausible different endings to one’s favorite TV show. This paper focuses on the impact of generative and machine learning AI on the financial industry. Although generative AI is an amazing tool for a discriminant user, it also challenges us to think critically about the ethical implications and societal impact of these powerful technologies on the financial industry. It requires ethical considerations to guide decision-making, mitigate risks, and ensure that generative AI is developed and used to align with ethical principles, social values, and in the best interests of communities.

Rapid urbanization, alongside escalating resource depletion and ecological degradation, underscores the critical need for innovative urban development solutions. In response, sustainable smart cities are increasingly turning to cutting-edge technologies-such as Generative Artificial Intelligence (GenAI), Foundation Models (FMs), and Urban Digital Twin (UDT) frameworks-to transform urban planning and design practices. These transformative tools provide advanced capabilities to analyze complex urban systems, optimize resource management, and enable evidence-based decision-making. Despite recent progress, research on integrating GenAI and FMs into UDT frameworks remains scant, leaving gaps in our ability to capture complex urban flows and multimodal dynamics essential to achieving environmental sustainability goals. Moreover, the lack of a robust theoretical foundation and real-world operationalization of these tools hampers comprehensive modeling and practical adoption. This study introduces a pioneering Large Flow Model (LFM), grounded in a robust foundational framework and designed with GenAI capabilities. It is specifically tailored for integration into UDT systems to enhance predictive analytics, adaptive learning, and complex data management functionalities. To validate its applicability and relevance, the Blue City Project in Lausanne City is examined as a case study, showcasing the ability of the LFM to effectively model and analyze urban flows-namely mobility, goods, energy, waste, materials, and biodiversity-critical to advancing environmental sustainability. This study highlights how the LFM addresses the spatial challenges inherent in current UDT frameworks. The LFM demonstrates its novelty in comprehensive urban modeling and analysis by completing impartial city data, estimating flow data in new locations, predicting the evolution of flow data, and offering a holistic understanding of urban dynamics and their interconnections. The model enhances decision-making processes, supports evidence-based planning and design, fosters integrated development strategies, and enables the development of more efficient, resilient, and sustainable urban environments. This research advances both the theoretical and practical dimensions of AI-driven, environmentally sustainable urban development by operationalizing GenAI and FMs within UDT frameworks. It provides sophisticated tools and valuable insights for urban planners, designers, policymakers, and researchers to address the complexities of modern cities and accelerate the transition towards sustainable urban futures.

Technologically advanced artificial intelligence (AI) is making a significant contribution to strengthening national security. AI algorithms facilitate the processing of vast amounts of information, increasing the speed and accuracy of decision-making. Artificial intelligence and machine learning (AI/ML) are crucial for state and integrated hybrid attacks and protecting new threats in cyberspace. Existing AI capabilities have significant potential to impact national security by leveraging existing machine learning technology for automation in labor-intensive activities such as satellite imagery analysis and defense against cyber attacks. This article examines selected aspects of the impact of artificial intelligence on enhancing a state’s ability to protect its interests and its citizens., artificial intelligence through the use of neutron networks, predictive analytics and machine learning algorithms enables security agencies to analyse vast amounts of data and identify patterns indicative of potential threats. Integrating artificial intelligence into surveillance, border control and threat assessment systems enhances the ability to respond preemptively to security challenges. In addition, artificial intelligence algorithms facilitate the processing of vast amounts of information, increasing the speed and accuracy of decision-making by police authorities. The rapid development of AI raises a number of questions for its use in securing not only national security but protecting all citizens. In particular, it is worth answering the question How does artificial intelligence affect national security and clarifying the issue of how law enforcement agencies can use artificial intelligence to maximise the benefits of the new technology in terms of security and protecting communities from rising crime. The analysis is based on a descriptive method in describing the phenomenon; by explaining the concepts and applications of artificial intelligence to determine its role in the national security sphere. An analysis of the usefulness of artificial intelligence in particular in police operations is undertaken, with the aim of defending the thesis that, despite some threats to the protection of human rights from AI, it is becoming the best tool in the fight against all types of crime in the country. Technological advances in AI can also have many positive effects for law enforcement, and useful for law enforcement agencies, for example in facilitating the identification of persons or vehicles, predicting trends in criminal activities, tracking illegal criminal activities or illegal money flows, flagging and responding to fake news. Artificial intelligence (AI) has emerged as one of the biggest threats to information security, but efforts are being made to mitigate this new threat, but also to find solutions on how AI can become an ally in the fight against cyber-security, crime and terrorist threats. Artificial intelligence algorithms search huge datasets of communication traffic, satellite images and social media posts to identify potential cyber security threats, terrorist activities and organized crime. It is advisable, when analyzing the opportunities and threats that AI poses to national and public security, to gain a strategic advantage in the context of rapid technological change and also to manage the many risks associated with AI. The conclusion highlights the impact of AI on national security, creating a range of new opportunities coupled with challenges that government agencies should be prepared for in addressing ethical and security dilemmas. Furthermore, AI improves predictive analytics, thereby enabling security agencies to more accurately anticipate potential threats and enhance their preparedness by identifying vulnerabilities in the national security infrastructure

The rapid integration of generative artificial intelligence (GenAI) into higher education has sparked debates about the future role of teachers (Chan &amp; Tsi, 2024), including in providing feedback information to students. While GenAI offers unprecedented accessibility and immediacy, this presentation argues that teachers' expertise remains irreplaceable in productive feedback – i.e., processes in which students make sense of information about their performance and use it to improve the quality of their work or learning strategies (Henderson et al., 2019, p. 1402). Drawing on a large-scale, cross-institutional survey involving 6,960 Australian university students (Henderson et al., 2025), this Pecha Kucha highlights students' perceptions of GenAI versus teacher feedback. The quantitative analysis revealed that nearly half of them (49.7%) reported using GenAI for feedback. However, they rated teacher feedback as more helpful and significantly more trustworthy. While 83.9% found GenAI feedback helpful, only 60.1% considered it trustworthy, compared to 90.5% who trusted teacher feedback. This trust gap may reflect the inconsistent quality identified in GenAI's feedback comments (Venter et al., 2024). The thematic analysis of 5,736 open-ended responses from students who used GenAI for feedback yielded 8,498 coded instances, revealing four interrelated characteristics in which teacher feedback was perceived as outperforming GenAI. Contextualisation and Relevance: Teacher feedback was perceived as more sensitive to specific assignment contexts (95.2% of 669 instances rated GenAI as less contextualised than teacher feedback) and more relevant to learning objectives (84.6% of 123 instances rated GenAI as less relevant). This contextual awareness enables teachers to identify what matters within disciplinary and course-specific frameworks. Reliability and Accuracy: Students perceived teacher feedback as significantly more reliable and trustworthy (95.4% of 1143 instances), reflecting teachers' ability to provide more trustworthy and accurate guidance without the hallucinations and factual inaccuracies that can appear on GenAI outputs. Relational Significance: Teachers offered more personal, connected feedback experiences (93.8% of 471 instances), providing the interpersonal recognition essential for productive learning relationships. This relational dimension cannot be replicated by GenAI’s algorithmic responses. Expertise: Students recognised teachers as more authoritative sources (88.2% of 119 instances), valuing their disciplinary knowledge and pedagogical understanding of student development trajectories. Students' evaluation of feedback is fundamentally shaped by perceptions of source credibility (Bearman et al., 2024), which may explain why students perceive teacher feedback as more trustworthy than GenAI's. Research demonstrates this selective engagement: uptake of content-focused GenAI feedback was considerably lower than form-focused feedback(Ziqi et al., 2024), suggesting students recognise GenAI's limitations for substantive guidance requiring disciplinary expertise. This translates into learning outcomes, with students not only perceiving instructor feedback as more useful but also demonstrating significantly greater lab score improvements than those receiving GenAI feedback (Er et al., 2025). GenAI may create opportunities for educators to focus on what they do best: providing expert, contextualised, and relationally-grounded feedback within authentic learning relationships. This potentially positions teacher expertise as increasingly valuable, with educators prioritising higher-level pedagogical responsibilities, such as developmental guidance, facilitating critical thinking, and disciplinary enculturation, while GenAI supports lower-level feedback processes, like grammar correction and initial draft review. Students appear to already recognise this distinction, trusting teachers for more substantive, transformative feedback while appreciating GenAI's supplementary role for immediate, accessible guidance.

While generative artificial intelligence (GenAI) tools demonstrate potential for enhancing students' learning outcomes, little is known about how students use GenAI at the micro‐level, particularly regarding when and how they seek assistance during self‐regulated learning (SRL). This study examined how learners' achievement goal orientations influence GenAI use patterns during problem‐solving within an AI‐powered learning environment. A total of 114 university students completed a nutrition recommendation task on the Healthy Choice platform. Prior to the task, students' goal orientations were measured using a self‐report scale. During the task, system log files captured learners' SRL activities and GenAI interactions. Hierarchical clustering analysis identified three distinct learner profiles: mastery‐oriented, performance‐oriented and low‐goal learners. Mastery‐oriented and performance‐oriented learners outperformed low‐goal learners. While ANOVA results revealed no significant differences in GenAI usage frequency across clusters, epistemic network analysis demonstrated significant differences in how GenAI was integrated into SRL processes. Mastery‐oriented learners exhibited stronger connections between GenAI use and cognitive activities (execution and evaluation), leveraging AI to deepen conceptual understanding. Performance‐oriented learners primarily used GenAI to support initial decision‐making. In contrast, low‐goal learners showed stronger temporal associations between GenAI use and metacognitive tasks like monitoring, reflection and final decision‐making. These findings inform differentiated scaffolding approaches based on student motivation profiles and the design of adaptive learning technologies that support personalized, effective engagement with GenAI tools. Practitioner notes What is already known about this topic Generative artificial intelligence (GenAI) tools can generate adaptive educational content and feedback. Previous studies have explored the positive impacts of GenAI integration on student learning experiences and outcomes. Learners' achievement goal orientation (AGO) has the potential to influence their GenAI use patterns. What this paper adds This study revealed how students with varying goal orientation profiles differed in the temporal patterns of GenAI use during their self‐regulated learning (SRL) processes. Mastery‐oriented learners tended to request assistance from GenAI tools as they conducted SRL activities of execution and evaluation, while performance‐oriented learners utilized GenAI tools for initial decision‐making. Low‐motivated learners relied on GenAI tools for metacognitive activities of monitoring and reflection, potentially leading to cognitive dependency. Implications for practice Frequency of GenAI use matters less than when and how students use these tools. Different scaffolding approaches are needed based on student motivation profiles. Understanding GenAI use patterns helps design AI‐supported learning environments that match student needs and promote meaningful learning rather than cognitive offloading.

Artificial General intelligence (AGI) offers enormous benefits for humanity, yet it also poses great risk. The aim of this systematic review was to summarise the peer reviewed literature on the risks associated with AGI. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Sixteen articles were deemed eligible for inclusion. Article types included in the review were classified as philosophical discussions, applications of modelling techniques, and assessment of current frameworks and processes in relation to AGI. The review identified a range of risks associated with AGI, including AGI removing itself from the control of human owners/managers, being given or developing unsafe goals, development of unsafe AGI, AGIs with poor ethics, morals and values; inadequate management of AGI, and existential risks. Several limitations of the AGI literature base were also identified, including a limited number of peer reviewed articles and modelling techniques focused on AGI risk, a lack of specific risk research in which domains that AGI may be implemented, a lack of specific definitions of the AGI functionality, and a lack of standardised AGI terminology. Recommendations to address the identified issues with AGI risk research are required to guide AGI design, implementation, and management.

Generative artificial intelligence in dentistry: A narrative review of current approaches and future challenges