Pedagogical translanguaging with GenAI: From dilemmas to design

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.

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

<sec><title>Objective</title> As urban design faces increasing demands for contextual responsiveness, iterative optimization, and data-informed reasoning, integrating artificial intelligence into the design process has gained renewed relevance. Among emerging technologies, generative artificial intelligence (GAI) shows strong potential for automating content creation and simulating spatial configurations. This research provides a comprehensive review of recent developments in the application of GAI to urban design. The research identifies representative technical pathways, their respective intervention stages, and the functional mechanisms by which generative models are reshaping the design workflow. This research presents a structured, theory-informed synthesis of how different generative models contribute to tasks such as intention modeling, spatial reasoning, and performance-driven design. Building on design thinking and a descriptive lens informed by the technology acceptance model (TAM), the research examines how model type, data modality, and task characteristics affect GAI’s functional role, usability, and acceptance. Particular attention is given to mapping deployment forms, from isolated tools to coordinated multi-model workflows, and to characterizing cross-cutting challenges of controllability, transparency, and contextual adaptability in urban design settings. </sec><sec><title>Methods</title> Following the PRISMA protocol, the research conducts a multi-stage literature review combining automated search and expert screening. A total of 125 peer-reviewed articles and high-impact preprints are selected from Web of Science, CNKI, arXiv, and selected industry sources, covering the period from 2014 to July 2025. Search terms such as “generative AI”, “AIGC”, “GAN”, “diffusion model”, “variational autoencoder”, “autoregressive model”, “large language model”, and urban-related keywords are used in various combinations. Based on the collected literature, four types of generative models are summarized as image-driven, language-driven, structure-driven, and feedback-optimized models, according to their application characteristics in urban design tasks. These types are aligned with four stages of the design process: preliminary analysis, scheme generation, evaluation and decision-making, and outcome expression. On this basis, a two-dimensional framework to examine how different GAI pathways intervene across tasks is formed. To refine the mapping, each design stage is further broken down into three representative sub-tasks. Preliminary analysis includes public demand analysis, urban data enhancement, case/task framing, and spatial element recognition. Scheme generation covers design intention modeling, spatial layout generation, and 3D form construction. The evaluation and decision-making stage includes multi-objective optimization, scheme evaluation, and scenario prediction. The final expression stage involves textual documentation, 2D representation, and visual rendering. A quantitative analysis is also conducted to show the distribution of model types over design stages, identify common combinations, and trace the evolution of research focus over time. TAM informs a descriptive synthesis of perceived usefulness (PU) and perceived ease of use (PEU) across model types to illuminate adoption patterns. </sec><sec><title>Results</title> The findings reveal that GAI models are increasingly integrated into urban design workflows but exhibit uneven adoption across task types and modalities. Image-driven models dominate in both early-stage analysis and final visual representation due to their high interpretability, usability, and compatibility with existing design practices. Language-driven models are commonly used in public demand analysis, participatory planning, and scenario scripting, enabled by the rise of large language models (LLMs) such as ChatGPT and DeepSeek. Structure-driven models, though less prevalent, show promise in generating street networks, land-parcel layouts, and spatial typologies using graph-based logic. Feedback-optimized models, which rely on reinforcement learning, evolutionary algorithms, and performance simulation are the least adopted, but demonstrate strong potential in multi-objective optimization and iterative decision-making. Recent research indicates an increasing use of multi-model workflows, such as text-to-image pipelines integrated with urban simulation or feedback loops. While GAI applications increasingly support design iteration, their adoption is heavily influenced by the controllability, explainability, and contextual adaptability of models. PU and PEU vary significantly by model type, with image-driven models rated highest and structure-driven and feedback-optimized models facing usability challenges due to complexity and low transparency. </sec><sec><title>Conclusion</title> Although GAI has demonstrated broad applicability across the urban design process, current implementations are largely procedural and auxiliary in nature. Most models recombine existing inputs rather than construct original logic, and few possess autonomous reasoning or normative awareness. This limits their role to content augmentation rather than conceptual guidance in design development. Moreover, issues such as opaque decision logic, lack of domain-specific knowledge embedding, and poor adaptability to local planning norms hinder practical adoption. Addressing these challenges requires multi-level efforts: 1) Construct structured, regionally grounded urban design datasets; 2) improve model interpretability, controllability, and responsiveness to professional input; and 3) develop modular, multi-model systems that support seamless interaction across design stages. Human – AI collaboration mechanisms — especially those based on iterative prompts and semantic feedback-must be enhanced to enable AI not just as a tool, but as an active design partner. This review offers a comprehensive reference for scholars and practitioners seeking to understand how GAI is reshaping the logic, structure, and agency of urban design. </sec>

Generative artificial intelligence (AI) has taken the world by storm, with notable tension transpiring in the field of education. Given that Generative AI is rapidly emerging as a transformative innovation, this article endeavors to offer a seminal rejoinder that aims to (i) reconcile the great debate on Generative AI in order to (ii) lay the foundation for Generative AI to co-exist as a transformative resource in the future of education. Using critical analysis as a method and paradox theory as a theoretical lens (i.e., the “how”), this article (i) defines Generative AI and transformative education (i.e., the “ideas”), (ii) establishes the paradoxes of Generative AI (i.e., the “what”), and (iii) provides implications for the future of education from the perspective of management educators (i.e., the “so what”). Noteworthily, the paradoxes of Generative AI are four-fold: (Paradox #1) Generative AI is a ‘friend’ yet a ‘foe’, (Paradox #2) Generative AI is ‘capable’ yet ‘dependent’, (Paradox #3) Generative AI is ‘accessible’ yet ‘restrictive’, and (Paradox #4) Generative AI gets even ‘popular’ when ‘banned’ (i.e., the “what”). Through a position that seeks to embrace rather than reject Generative AI, the lessons and implications that emerge from the discussion herein represent a seminal contribution from management educators on this trending topic and should be useful for approaching Generative AI as a game-changer for education reformation in management and the field of education at large, and by extension, mitigating a situation where Generative AI develops into a Ragnarök that dooms the future of education of which management education is a part of (i.e., the “so what”).

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

Background: Generative Artificial Intelligence (AI), particularly large language models like ChatGPT, has rapidly entered medical education. In pharmacology instruction within MBBS programs, these AI tools offer novel educational benefitsfrom ondemand tutoring to personalized learningbut also pose risks due to "hallucinations, " i. e., AI -generated misinformation. Objective: This review explores how generative AI is being integrated into pharmacology education, the educational advantages it provides, the nature and causes of AI hallucinations, and the potential risks of relying on inaccurate AI -generated content in medical learning. Mitigation strategies, best practices, case examples, ethical considerations, and future directions are discussed to inform balanced and safe use. Methods: A literature review was conducted focusing on recent (2020-2025) peer -reviewed sources and authoritative reports on generative AI in medical and pharmacology education. Results: Generative AI can enhance pharmacology education through adaptive learning, rapid information access, and interactive simulations, which students have found useful for studying and clinical preparation. However, AI hallucinationsplausible -sounding yet incorrect answersare common, with studies reporting a substantial fraction of AI -generated medical responses containing errors. These inaccuracies can mislead learners and threaten patient safety if not recognized. Strategies such as careful validation of AI -provided information, AI literacy training, and technical approaches (e. g. retrieval -augmented generation) are identified to mitigate risks. Conclusions: Generative AI holds significant promise in medical pharmacology education by augmenting learning and engagement. Yet, educators and students must remain vigilant about AI hallucinations. A balanced approachleveraging AI's benefits while implementing safeguards and ethical guidelinesis essential to ensure that AI becomes a reliable adjunct in pharmacology education rather than a source of misinformation. Ongoing research, policy development, and training are needed to fully realize generative AI's potential in medical education while minimizing its risks.

Teacher education students play double role as present learners and future educators. Hence, they need targeted training to navigate the growing influence of Generative Artificial Intelligence (GenAI) on teaching, learning and professional identity. However, existing artificial intelligence (AI) literacy programmes predominantly emphasize technical AI knowledge and pre‐GenAI tools or are offered by GenAI platforms that focus on their own technologies' features, thereby lacking pedagogically structured frameworks that address non‐technical dimensions such as ethics, contextual understanding and the development of human‐centered, critical and lifelong learning mindsets to adapt to rapid technology changes. Furthermore, limitation of time in teacher education syllabus and faculty's lack of AI literacy require targeted intervention. This design‐based research (DBR) aims to fill this gap by developing and evaluating a set of design principles for GenAI literacy training in teacher education. Integrating contemporary AI competency frameworks for learners and teachers, the study implements these principles in a workshop that serves as the research prototype. The prototype was first piloted with 14 master's students and then evaluated with 29 teacher education (TE) students. Results show significant gains in participants' AI competence self‐efficacy, attitudes towards GenAI and commitment to critical, ethical and pedagogical engagement with GenAI tools. The findings highlight the need for teacher education programmes to integrate GenAI literacy that supports teachers' evolving roles as reflective practitioners, co‐creators and lifelong learners in an AI‐driven world. Practitioner notes What is already known about this topic Students' self‐efficacy and mindset influence their adoption of GenAI in learning and teaching. GenAI competency is essential for teacher education students, encompassing technological, ethical and sociocultural dimensions. Implementing AI literacy training faces challenges such as educators' limited AI literacy, the lack of pedagogical frameworks and the need to integrate technical knowledge with ethical reasoning, self‐regulated learning and metacognitive skills. Transformative learning is crucial in preparing teacher education students as co‐creators and facilitators in AI‐enhanced education. What this paper adds This study presents the design and evaluation of an AI competency training programme to enhance teacher education students' self‐efficacy, critical and human‐centered AI mindsets and human–AI interaction skills. Using design‐based research, it identifies key principles for AI literacy training, demonstrating how transformative, active and experiential learning approaches can prepare teacher education students for evolving roles in AI‐enhanced education. Implications for practice and/or policy Educational technologists can apply the proposed design principles to develop GenAI tools and AI systems that enhance AI competency and self‐regulated learning (SRL). Educators can integrate these AI training principles into their teaching strategies and enhance their task assignments and assessments to foster deep learning, self‐regulated learning and metacognitive skills in the age of GenAI. Policymakers should update teacher education curriculum to include AI competency, strengthen content and pedagogical knowledge, especially in social emotional learning, formative and authentic assessment methods and establish guidelines for selecting GenAI tools that support human intelligence and mitigate over‐reliance.

The current debate on artificial intelligence (AI) tends to associate AI imaginaries with the vision of a future technology capable of emulating or surpassing human intelligence. This article advocates for a more nuanced analysis of AI imaginaries, distinguishing “strong AI narratives,” i.e., narratives that envision futurable AI technologies that are virtually indistinguishable from humans, from "weak" AI narratives, i.e., narratives that discuss and make sense of the functioning and implications of existing AI technologies. Drawing on the academic literature on AI narratives and imaginaries and examining examples drawn from the debate on Large Language Models and public policy, we underscore the critical role and interplay of weak and strong AI across public/private and fictional/non-fictional discourses. The resulting analytical framework aims to empower approaches that are more sensitive to the heterogeneity of AI narratives while also advocating normalising AI narratives, i.e., positioning weak AI narratives more firmly at the center stage of public debates about emerging technologies.

Generative AI tools and empowerment in L2 academic writing

The rise of generative artificial intelligence (GenAI), particularly large language models (LLMs), presents both opportunities and challenges for education, necessitating the development of GenAI literacy among pre-service teachers. This study examined the effects of using GenAI as a collaborator within a novel LLM-based educational website design task on pre-service teachers’ task performance, design thinking, and GenAI literacy. In a quasi-experimental design, 52 pre-service teachers were compared based on their self-reported approach: seven experimental groups (n = 28) utilized GenAI for the design task, while six control groups (n = 24) completed the task without GenAI support. Qualitative responses detailed the various ways GenAI was used throughout the design process and highlighted perceived advantages and disadvantages. Quantitative analyses indicated that the experimental groups achieved significantly higher scores in task performance, overall perceived design thinking competence, particularly in the define and ideate phases, and perceived GenAI literacy compared to the control groups. These findings offered implications for using authentic, complex collaborative design tasks as a pedagogical strategy to cultivate the critical and practical competencies pre-service teachers needed to effectively and responsibly integrate GenAI into their future educational practice.

Generative Artificial Intelligence (GenAI) applies models and algorithms such as Large Language Model (LLM) and Foundation Model (FM) to generate new data. GenAI, as a promising approach, enables advanced capabilities in various applications, including text generation and image processing. In current practice, GenAI algorithms run mainly on the cloud server, leading to high latency and raising security concerns. Consequently, these challenges encourage the deployment of GenAI algorithms directly on edge devices. However, the large size of such models and their significant computational resource requirements pose obstacles when deploying them in resource-constrained systems. This survey provides a comprehensive overview of recent proposed techniques that optimize GenAI for efficient deployment on resource-constrained edge devices. For this aim, this work highlights three main categories for bringing GenAI to the edge: software optimization, hardware optimization, and frameworks. The main takeaways for readers of this survey will be a clear roadmap to design, implement, and refine GenAI systems for real-world implementation on edge devices.

Background: Qualitative data is a rich source of information about the student experience. However, the amount of work necessary for effective analyses can make it less accessible for directing broad educational decisions. A wide array of software has been developed to provide quick summaries of qualitative data, but has yet to provide the in-depth analysis afforded by trained coders and educational experts employing established methodology such as thematic analyses. Recent research has suggested that current Generative Artificial intelligence (GenAI) offers a potential of quick and effective qualitative analyses comparable to human researcher. Aims: The present study partially replicated recent step-wise thematic analysis procedures (Naeem et al., 2023; Nyaaba et al., 2025), employing two separate GenAI (Chat GPT 4.5, Gemini 2.5). This partial replication aimed to test GenAI's ability to analyse students' short answer feedback regarding the use of a recently developed educational platform. Methods: 580 students (grades three to six) from one primary school in Japan participated in this study. The present study followed procedures developed (Naeem et al., 2023) and validated by recent research comparing GenAI and human coders (Nyaaba et al., 2025). Consistent with this research, students' open-ended responses to a request for feedback regarding learning experiences with a bespoke digital learning-to-read were analysed employing two GenAI. Results: Comparison of coding from each GenAI suggested robust overlap, but the potential for strong analysis through the integration of codes (15 in total). A random subset (20 feedbacks) was coded by each GenAI, coder inter-rated reliability was tested and suggested sufficient reliability (Cohen’s Kappa &amp;gt; .61). Review of the final coding by ChatGPT4.5, suggested coherency and consistency. Results from ChatGPT4.5 for each of the remaining thematic analysis steps (themes, conceptualisation, conceptual model) were reviewed by the researchers. The final conceptual models describing the student experiences were used to construct directions for further refinement of the digital learning-to-read platform. The partial replication of Nyaaba et al.'s GenAI thematic analysis suggested that with careful supervision, GenAI are a powerful tool for efficient and substantive thematic analysis. Given the enormous amount of feedback collected through a range of different forums within education, using GenAI to make sense of this information an invaluable is opportunity for many educators and researchers.

Purpose Generative Artificial Intelligence (GenAI) is rapidly transforming business strategy, innovation processes and governance practices. While prior research has focused primarily on technological implementation and performance outcomes, limited attention has been paid to how senior executives interpret, frame and adapt to GenAI as a strategic and ethical phenomenon. This study aims to explore executives’ sensemaking and adaptive responses to GenAI and to develop a conceptual model that captures this process. Design/methodology/approach The study adopts a qualitative exploratory design based on semi-structured interviews with 15 senior executives from diverse industries in Greece. Data were analyzed using thematic analysis to identify recurring patterns in executives’ perceptions, decision rationales and ethical considerations related to GenAI adoption and integration. Findings The analysis reveals four interrelated dimensions shaping executive adaptation: strategic integration and decision-making, business value and innovation, human–AI collaboration and workforce transformation and ethics, governance and adoption barriers. Cross-thematic synthesis indicates that executives perceive GenAI as a decision-support and innovation amplifier rather than an autonomous decision-maker. These findings inform an Emergent Integrative Model of Executive Adaptation to GenAI, conceptualized as a dynamic cycle comprising strategic sensemaking (Curate), operational experimentation (Create) and ethical consolidation (Consolidate). Research limitations/implications Several executives cited technical and resource limitations as obstacles to effective GenAI implementation. Challenges include legacy systems, lack of skilled artificial intelligence (AI) engineers and limited integration between AI platforms and enterprise software. Practical implications This model advances understanding of executive cognition and adaptive intelligence in the AI era, positioning leadership as a process of continuous learning, sensemaking and ethical stewardship. Practically, the research offers a roadmap for organizations and policymakers for aligning GenAI-driven innovation with responsible governance and leadership development. Social implications By highlighting the role of executive stewardship, the study underscores how ethical leadership in GenAI adoption influences public trust, workforce well-being and organizational legitimacy. Originality/value Existing research on AI in business has predominantly focused on technological implementation, efficiency gains and economic outcomes. Studies emphasize measurable benefits such as productivity enhancement, improved decision-making speed and customer experience optimization. However, fewer studies explore how executive cognition and strategic reasoning shape the trajectory of AI adoption – particularly regarding GenAI technologies that introduce new forms of creative automation. The study advances leadership and sensemaking research by shifting the focus from GenAI adoption outcomes to executive cognition and ethical stewardship. It offers a novel integrative model that explains how strategy, innovation and governance co-evolve in the GenAI era.

This study investigates the adoption and impact of Generative Artificial Intelligence (GenAI) tools on English as a Foreign Language (EFL) learners, focusing on self-efficacy, motivation, and predicted exam performance. Using a mixed-methods approach, the research combines survey data (N = 595) and interviews (N = 8) from Chinese university students preparing for the College English Test 4. Quantitative analysis shows that positive attitudes, perceived usefulness, and ease of use predict GenAI usage frequency, while intrinsic motivation negatively correlates with use. GenAI use frequency did not significantly affect predicted test scores or self-efficacy levels. Younger students used GenAI more frequently. Qualitative data reveals that students primarily use GenAI as supplementary learning resources, which, while beneficial, do not substantially enhance self-efficacy or predicted exam performance. The study founds a knowledge gap in the effectiveness of these tools in EFL contexts. It suggests strategies for optimizing GenAI in EFL learning, including blending with traditional methods, providing user training, balancing technology with traditional approaches, ensuring equal access, and customizing GenAI for education. The findings imply a need for educators to supervise GenAI use and for institutions to facilitate effective integration. Future research should examine long-term and cultural effects of GenAI use in education.

Generative Artificial Intelligence (GenAI) is changing the landscape of higher education and profoundly affecting college students' learning performance and learning psychology. This study explored the types of behaviors that college students use GenAI and the mechanisms that influence academic burnout. The data were from 369 full-time undergraduate students through a questionnaire, and the hypotheses were analyzed using SPSS 27.0 and the Amos 28.0 plug-in. Results showed that undergraduates demonstrated high levels of GenAI use and acceptance in their academic endeavors, with GenAI assisted homework receiving the highest scores. Significant differences in the performance of GenAI users were observed on gender, academic achievement, and major category. SEM revealed that plagiarized GenAI use and GenAI-assisted classes directly increased academic burnout. In contrast, the effects of GenAI alternative search tasks, GenAI assisted homework, and GenAI evaluation feedback on academic burnout were indirect, mediated through plagiarized GenAI use and GenAI assisted classroom responses. Specifically, there are complex mechanisms of influence between GenAI use behaviors and college student burnout. This study brings new perspectives to the existing literature and articulates valuable insights around the use of GenAI in higher education practice.