Navigating the frontier of AI-assisted student assignments: challenges, skills, and solutions.

Background: The rise of artificial intelligence (AI) is transforming educational practices, particularly in assessment. While AI may support the students in idea generation and summarization of source materials, it also introduces challenges related to content validity, academic integrity, and the development of critical thinking skills. Educators need strategies to navigate these complexities and maintain rigorous, ethical assessments that promote higher-order cognitive skills. Aims: This manuscript provides practical guidance for educators on designing take-home assessments (e.g. research-based assignments) in the AI era. It explores how AI can be leveraged to enhance student learning during these assessment tasks and offers educators the ability to develop personalized feedback while maintaining academic integrity and avoiding potential risks to students' skill development. Description: Twelve tips are presented, organized into four key areas: (1) Co-developing AI literacy among students and educators, (2) designing assessments that prioritize process over output, (3) validating learning through AI-free assessments, and (4) preparing students for AI-enhanced workplaces by developing AI communication skills and promoting human-AI collaboration. These strategies emphasize ethical AI use, personalized feedback, and creativity. Methodology: This guidance was developed through a collaborative, consensus-driven process involving a consortium of three educators with diverse academic backgrounds, career stages, and perspectives on AI in education. Members, holding experience in higher education across the UK, USA, Australia, and MENA regions, brought varied insights into AI’s role in education. The team engaged in an iterative process of refining recommendations through fortnightly virtual meetings and offline discussions. Conclusion: By adopting these approaches, educators can balance the benefits and risks of AI in assessments, fostering authentic learning while preparing students for the challenges of an AI-driven world. This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

The impact of Artificial Intelligence (AI) continues to change the state of the world in its transformation of state capacity, geopolitical competition, as well as the practice of international law. Analyzing the ways in which the AI system impacts sovereignty (and state authority, territorial control, data governance, and strategic autonomy), international law (e.g. legal accountability, due diligence, human rights, and humanitarian law), and (v) global political stability (risk escalation, integrity, and arms-race) is a multifaceted venture. This study builds upon the following emerging governance frameworks: the EU AI Act (Regulation (EU) 2024/1689) and the United Nations General Assembly resolution on safe, secure, and trustworthy AI and the Council of Europe, Framework Convention on AI, and the (NIST) National Institute of Standards and Technology AI Risk Management Framework. This study also proposes to develop a quantitative model on AI risk, sovereignty erosion, and instability. The study proposes (within the EU for example) conflict to examine policy/legal experts’ perceptions of AI governance, digital sovereignty, adequacy, military-AI, and political stability. Illustrated statistical outputs detailing reliability control, interrelations, and regression pathways show how greater perceptions of military influenced AI both risk and lack of governance capacity predict higher perceived erosion of sovereignty and instability, more narrowly, are in line with current global calls for “risk-based” governance-with laws and liability. The results are especially pertinent for addressing the impacts of AI on sovereignty, which are significantly institutional. States with no regulatory capacity are more dependent on external governance structures, regulatory standards, and compute facilities. The Policy section suggests reframing military AI confidence-building and human rights treaty safeguard measures for regulatory predictability, risk control, cross-jurisdictional audit capabilities, and structured enforcement for greater harmonized data regulatory control. Overall, the paper contends and clearly demonstrates that the governance paradigm constraining control, accountability, and predictability is of paramount importance for the international order and stability in the era of AI.

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.

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d450368e107">The integration of generative artificial intelligence (AI) into higher education presents a critical juncture for assessment practices. Traditional methods, such as take-home essays and unsupervised coursework, are increasingly vulnerable to AI-generated submissions, challenging their validity and raising profound questions about academic integrity (Cotton et al., 2023). This paper argues that the prevailing response focusing on AI detection and restriction is unsustainable. Instead, we must seize this moment as a catalyst to fundamentally redesign assessments for authentic, humancentric learning. Specifically, this paper proposes an AI-resilient assessment design framework that aligns assessment tasks with cognitive, reflective and ethical capabilities that remain distinctly human. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d450368e109">Moving beyond a deficit model, this paper explores a framework for "AI-resilient" assessment that prioritises the cognitive processes education seeks to develop. Drawing on principles of authentic assessment (Boud &amp; Falchikov, 2007) and contemporary digital literacy frameworks (JISC, 2023), we will present and critique a spectrum of alternative approaches. These include scaffolded project work that evidence iterative development, reflective portfolios that articulate metacognitive journeys, and in-person vivas that probe critical reasoning. <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dir="auto" id="d450368e111">Critically, the paper contends that successful adaptation requires enhancing assessment literacy for both staff and students. This involves co-creating transparent guidelines on ethical AI use and fostering a shared understanding of how AI can function as a collaborative tool rather than a substitute for learning. Ultimately, the paper proposes that the AI era requires a necessary progression towards assessments that measure distinct human skills such as critical thinking, ethical reasoning, and creative collaboration within learning spaces that are essential for graduate success in a digitally transformed world (OECD, 2021).

The rise of Artificial Intelligence (AI) and Generative Artificial Intelligence (GenAI) in higher education necessitates assessment reform. This study addresses a critical gap by exploring student and academic staff experiences with AI and GenAI tools, focusing on their familiarity and comfort with current and potential future applications in learning and assessment. An online survey collected data from 35 academic staff and 282 students across two universities in Vietnam and one in Singapore, examining GenAI familiarity, perceptions of its use in assessment marking and feedback, knowledge checking and participation, and experiences of GenAI text detection. Descriptive statistics and reflexive thematic analysis revealed a generally low familiarity with GenAI among both groups. GenAI feedback was viewed negatively; however, it was viewed more positively when combined with instructor feedback. Academic staff were more accepting of GenAI text detection tools and grade adjustments based on detection results compared to students. Qualitative analysis identified three themes: unclear understanding of text detection tools, variability in experiences with GenAI detectors, and mixed feelings about GenAI’s future impact on educational assessment. These findings have major implications regarding the development of policies and practices for GenAI-enabled assessment and feedback in higher education.

ImportanceInterest in artificial intelligence (AI) has reached an all-time high, and health care leaders across the ecosystem are faced with questions about where, when, and how to deploy AI and how to understand its risks, problems, and possibilities.ObservationsWhile AI as a concept has existed since the 1950s, all AI is not the same. Capabilities and risks of various kinds of AI differ markedly, and on examination 3 epochs of AI emerge. AI 1.0 includes symbolic AI, which attempts to encode human knowledge into computational rules, as well as probabilistic models. The era of AI 2.0 began with deep learning, in which models learn from examples labeled with ground truth. This era brought about many advances both in people’s daily lives and in health care. Deep learning models are task-specific, meaning they do one thing at a time, and they primarily focus on classification and prediction. AI 3.0 is the era of foundation models and generative AI. Models in AI 3.0 have fundamentally new (and potentially transformative) capabilities, as well as new kinds of risks, such as hallucinations. These models can do many different kinds of tasks without being retrained on a new dataset. For example, a simple text instruction will change the model’s behavior. Prompts such as “Write this note for a specialist consultant” and “Write this note for the patient’s mother” will produce markedly different content.Conclusions and RelevanceFoundation models and generative AI represent a major revolution in AI’s capabilities, ffering tremendous potential to improve care. Health care leaders are making decisions about AI today. While any heuristic omits details and loses nuance, the framework of AI 1.0, 2.0, and 3.0 may be helpful to decision-makers because each epoch has fundamentally different capabilities and risks.

Purpose- In the contemporary business landscape, we are witnessing the rapid development of Artificial Intelligence (AI), which is fundamentally reshaping organizational practices. These developments mark what can be described as the "Era of AI", a significant milestone in technological history. While AI offers benefits, it also presents critical challenges, particularly concerning its adoption and the adaptation processes within organizations. Despite the swift evolution of AI technologies, research on their practical applications in organizational settings remains scarce and underdeveloped. This gap highlights a promising area for further exploration. In alignment with the literature, it can be argued that organizations with higher AI adoption rates tend to achieve better innovation outcomes, which suggests a need to revisit and potentially expand the Technology-Organization-Environment (TOE) paradigm. Originally developed to explain technological adoption/embracement, the TOE framework may not capture the complexities introduced by AI. This study aims to explore whether an expanded TOE paradigm is necessary to better address the contemporary dynamics of AI adoption. Methodology- This research investigates the historical development and consolidation of AI within organizations, using the TOE paradigm as a foundational theoretical look. The study examines whether the existing TOE model sufficiently explains AI adoption or whether it requires augmentation to remain relevant in the age of generative AI. Findings- Literature review findings indicate that the traditional TOE framework exhibits limitations when applied to AI adoption. To address these gaps, another study was found in the literature that proposes the inclusion of a human factor—transforming the TOE into a TOEH (Technology-Organization-Environment-Human) model. In our research we would like to integrate critical thinking (CT) skills under Human Factor, as organizations increasingly seek employees who can critically assess and effectively utilize outputs from generative AI (GenAI) tools. The ability to make intelligent and ethical decisions in the context of AI is now a vital competency. Conclusion- The proposed TOEH framework offers a more well-rounded approach to discovering AI adoption within organizations. By incorporating the human element, particularly critical thinking skills, organizations can better prepare to embrace AI in an ethical, effective, and innovative manner.

Purpose: The general objective of this study was to explore Intellectual Property Rights in the era of Artificial Intelligence. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to Intellectual Property Rights in the era of Artificial Intelligence. Preliminary empirical review revealed that the era of Artificial Intelligence (AI) has significantly transformed the landscape of Intellectual Property Rights (IPR), presenting both opportunities and challenges. It highlighted that traditional IP laws are increasingly inadequate to address the complexities introduced by AI-generated content, necessitating a rethinking of existing frameworks. The study emphasized the need for recognizing AI's role in the creation of new works and inventions and the importance of developing balanced approaches to protect both human and AI contributions. Ethical considerations, such as accountability, transparency, and fairness, were also deemed crucial in ensuring responsible AI use. Overall, the study called for a comprehensive and proactive approach to integrate AI into IPR, ensuring robust protections while fostering innovation. Unique Contribution to Theory, Practice and Policy: The Technological Determinism Theory, Innovation Diffusion Theory and Legal Realism Theory may be used to anchor future studies on Intellectual Property Rights in the era of Artificial Intelligence. The study recommended revising existing IP laws to explicitly include AI-generated content and inventions, clarifying criteria for authorship and inventorship. It suggested expanding theoretical frameworks to accommodate AI contributions, emphasizing the collaborative nature of human and AI creativity. Practical measures, such as enhanced cybersecurity and legal safeguards for AI-generated trade secrets, were advised. Policy-wise, the study advocated for international cooperation to harmonize IP laws concerning AI. Developing ethical guidelines for responsible AI use and implementing education programs to inform stakeholders about AI and IP implications were also recommended. These measures aimed to create a balanced IP framework supporting innovation while protecting the rights of all stakeholders.

본 연구는 인공지능 교육이 공교육에 도입되기 시작하는 초등학교 고학년을 대상으로 인공지능 핵심역량을 모델링함으로써 인공지능 시대의 인재상을 명확히 하고, 인공지능 교육의 방향성 수립에 기초를 제공하는 것을 목적으로 한다. 이를 위하여 전문가 일대일 면담, 포커스 그룹 인터뷰, 델파이 조사 등 의견수렴 및 타당화 과정을 반복적으로 실시하였고, 이러한 과정에서 초등 인공지능 교육의 핵심역량 구성요인을 도출하고 타당성을 검증하였다. 인공지능 교육 핵심역량은 ‘인공지능 이해’ 범주에 15개 역량, ‘인공지능 윤리’ 범주에 8개 역량, ‘인공지능 사회정서’ 범주에 8개 역량이 포함되어 총 3개 범주 31개 역량이 도출되었다. 최종안에 대한 델파이 조사결과, 전체 응답평균 3.66/4점, CVI 평균 0.930, CVR 평균 0.863으로 CVI 최솟값 0.70과, CVR 최솟값 0.455를 상회하는 등 높은 공감대를 획득하였다. 특히, ‘인공지능 이해’ 범주의 ‘AI 인지’와 ‘AI 활용’, ‘인공지능 윤리’ 범주의 ‘AI 윤리 문제인식’, ‘인공지능 사회정서’ 범주의 ‘인간-AI 협업’과 ‘능동적 자기관리’ 역량의 타당도와 중요도가 높게 나타났다. 본 연구는 초등 맥락의 인공지능 교육 핵심역량 모델링을 통해 인공지능 이해 교육을 넘어 인공지능 윤리와 사회정서 교육까지로 시야를 넓히는데 기여하고자 하였으며, 초중등 인공지능 교육의 체계를 마련하는데 기초를 제시하였다. 또한 인공지능 교육에 역량중심 융합교육을 도입할 수 있도록 토대를 마련한 것에 의의를 가진다.The purpose of this study is to clarify the concept of talent for the artificial intelligence era and to provide the foundation for designing the directivity of artificial intelligence education by modeling core competencies for upper grades of elementary school students where artificial intelligence education is initiated in public education. To this end, experts one-to-one interviews, focus group interviews, and Delphi surveys were repeatedly conducted, in which process, core competency components of elementary artificial intelligence education were derived and validated. The core competencies of artificial intelligence education included 15 competencies in the ‘artificial intelligence understanding’ category, 8 competencies in the ‘artificial intelligence ethics’ category, and 8 competencies in the ‘artificial intelligence social emotions’ category, resulting in a total of 31 competencies in 3 categories. As a result of the Delphi survey on the final draft, the overall response average was 3.66/4 points, the CVI average was 0.930, and the CVR average was 0.863, which exceeded the CVI minimum value of 0.70 and the CVR minimum value of 0.455. In particular, the validity and importance of the competencies of 'AI awareness', 'AI utilization' in the 'artificial intelligence understanding' category, ‘AI ethics problem recognition’ in the 'artificial intelligence ethics’ category, and 'human-AI collaboration', 'active self-management' in the 'artificial intelligence social emotions' category were found to be high. This study attempted to contribute to expanding the perspective beyond artificial intelligence principle education to artificial intelligence ethics and social-emotional education through modeling of core competencies for elementary schoolers and presented the basis for the systematic perspective of artificial intelligence education. It is also meaningful to lay the foundation for introducing competency-based convergence education into artificial intelligence education.

This qualitative study explores lecturers’ perspectives on the role of artificial intelligence (AI) in personalised learning within higher education. The rapid proliferation of AI has introduced numerous ethical challenges, including the potential for academic dishonesty and misuse. One concern highlighted in this study is maintaining academic integrity while fostering responsible and ethical AI use in educational contexts. Grounded in the Technology Acceptance Model, the research examines how lecturers navigate these complexities through innovative teaching and assessment strategies. Data were collected from 16 lecturers via open-ended questionnaires, and thematic analysis, guided by Braun and Clarke’s six-step framework, was employed to identify recurring themes. The study is limited in its reliance on self-reported data, which may not fully capture the nuances of lecturers’ experiences or practices. Additionally, the study is context-specific, focusing on a limited sample size, which may restrict the generalizability of the findings to broader contexts. Findings reveal that lecturers employ strategies such as using AI-detection tools like Turnitin to uphold academic integrity, redesigning assessments to include in-class components, and encouraging transparency by having students declare AI use. Another significant finding is the emphasis on fostering critical thinking skills to enable students to engage ethically with AI tools. The study recommends that higher education institutions train lecturers and students on ethical AI use, promote transparency in AI-assisted academic work, and invest in technologies that support these objectives. Revising assessment strategies to incorporate innovative, controlled formats is also suggested to mitigate misuse, ensuring the responsible integration of AI into educational practices.

In the dynamic landscape of digital media arts education, particularly under the pervasive influence of Artificial Intelligence (AI) Era, the maintenance of academic integrity emerges as a critical concern. This research delves into the nuanced definition, diverse manifestations, and illustrative case studies elucidating academic integrity within the domain of digital media arts. Additionally, it scrutinizes the potential repercussions of AI technology on the intricate fabric of academic integrity within this field. Employing rigorous survey methodologies and insightful case studies, the research unveils a discernible uptick in instances of academic non-integrity in the dynamic realm of digital media arts, necessitating prompt and strategic responses. Crucially, the study emphasizes the foundational role of academic integrity in shaping students into proficient practitioners, characterized by an unwavering commitment to ethical standards. By cultivating a dedication to academic ethics, the research advocates for the resilient growth of the digital media arts field amid ever-evolving technological paradigms, ensuring an unwavering commitment to integrity in academia.

The rise of Artificial Intelligence (AI) has permanently changed life as we know it. And education has been no exception. Understanding the issues and benefits that may come with the implementation of AI into higher education, in particular, this study examines the impact of advanced AI models, like ChatGPT, on academic integrity. By employing a mixed-methods approach, the study gathers insights from undergraduate students at the University "St. Kliment Ohridski" in Bitola, North Macedonia, exploring their views on AI's role in academic practices. By identifying the key points through a brief literature review, this study finds that the concerns about the use of AI in education are indeed founded – concerns including potential for cheating and the ethical dilemmas posed by such technologies. Some of these concerns were also confirmed by the data obtained through our survey. A sample of 114 undergraduate students kindly provided their responses for this study, helping further our insights with their perspective. The findings from the survey revealed that students are moderately comfortable with using AI for academic purposes, with a notable portion of them admitting to using ChatGPT without disclosure to professors. The reasons behind the undisclosed use of AI, according to the data collected, include pressures for high grades, time constraints and the accepted belief that cheating is “what everyone is doing”. Despite its limitations, such as reliance on self-reported data and its focus on a specific geographic and academic context, we believe that the study still manages to make a small, yet significant contribution to the ethical challenges posed by AI in education.

This study explores the sentiments and discussion topics of X/Twitter users regarding academic integrity in the era of artificial intelligence (AI). The approach incorporates sentiment analysis and topic modelling to reveal the public perspective on academic integrity issues, including plagiarism, online exams, and AI usage. Our study aims to provide a framework for exploring topics and findings related to the trend of academic integrity in the era of AI. In sentiment classification, Naive Bayes, support vector machine (SVM), and Random Forest algorithms are combined with vectorization techniques such as Count Vectorizer, Word Level TF-IDF, N-Gram TF-IDF, and Character Level TF-IDF. The results show that Naive Bayes with Count Vectorizer provides the best performance on imbalanced data. For the topic modelling, NMF proved to be the most effective in generating specific topics, such as plagiarism and AI detection, with the highest coherence scores. This study also examines the crucial role of each preprocessing step in enhancing data quality, which significantly impacts classification and topic modelling performance. The findings are expected to provide new insights into sentiment analysis and a deeper understanding of academic integrity issues in the era of artificial intelligence.

In the digital age, the intersection of artificial intelligence (AI) and higher education (HE) poses novel ethical considerations, necessitating a comprehensive exploration of this multifaceted relationship. This study aims to quantify and characterize the current research trends and critically assess the discourse on ethical AI applications within HE. Employing a mixed-methods design, we integrated quantitative data from the Web of Science, Scopus, and the Lens databases with qualitative insights from selected studies to perform scientometric and content analyses, yielding a nuanced landscape of AI utilization in HE. Our results identified vital research areas through citation bursts, keyword co-occurrence, and thematic clusters. We provided a conceptual model for ethical AI integration in HE, encapsulating dichotomous perspectives on AI’s role in education. Three thematic clusters were identified: ethical frameworks and policy development, academic integrity and content creation, and student interaction with AI. The study concludes that, while AI offers substantial benefits for educational advancement, it also brings challenges that necessitate vigilant governance to uphold academic integrity and ethical standards. The implications extend to policymakers, educators, and AI developers, highlighting the need for ethical guidelines, AI literacy, and human-centered AI tools.

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.