The semantics of space and time of the far right. A neopragmatic study of landscape interpretation by generative AI

Background Generative AI (GenAI) such as ChatGPT can take over tasks that previously could only be done by humans. Although GenAI provides many educational opportunities, it also poses risks such as invalid assessments and irrelevant learning outcomes. This article presents a broadly applicable method to (1) determine current assessment validity, (2) assess which learning outcomes are impacted by student GenAI use and (3) decide whether to alter assessment formats and/or learning outcomes. This is exemplified by the case-study on our medical informatics curriculum. We developed a five-step method to evaluate and address the impact of GenAI. In a collaborative manner, the courses in a curriculum are analysed on their assessment plans and together with the teachers, the courses are adapted to address the impact of GenAI usage. Results 57% of assessments, especially in writing and programming, were at risk of reduced validity and relevance. GenAI impact on assessment validity was closer related to the content and structure of assessments than their complexity according to Bloom’s taxonomy. During educational retreats, lecturers discussed the relevance of impacted learning outcomes and whether students should be able to achieve them with or without GenAI. Furthermore, the results led to a plan to increase GenAI literacy and use over the years of study. Subsequently the coordinators were asked to either adjust either their assessments formats to preclude GenAI use, or to alter the learning outcomes and include GenAI use and literacy. For 64% of the impacted assessments the assessment format was adapted and for 36% the learning outcomes were adapted. Conclusion The majority of assessments in our curriculum were at risk of reduced assessment validity and relevance of learning outcomes, leading us to adapt either the assessments or learning outcomes. This method and case-study offer a potential blueprint for educational institutions facing similar challenges.

Thus, the appearance of the Generative Artificial Intelligence opened up a great turn in many areas, including education and creative industries. This paper seeks to understand the deep impact that Generative AI is going to have on learning and creating processes for the social context of Generation Z (Gen Z) students – born in digital culture. The work looks into the possibilities and challenges that Gen Z in collaboration with Generative AI leads to the future of learning and creativity. This paper is relevant as it offers some understanding of the ongoing changes in the education and creativity together with the escalating growth in technology. The nature of the association between the members of Generation Z and the Generative AI needs to be known by the educational stakeholders, policymakers, and business executives to leverage value from the existing and upcoming technologies together with dealing with possible negative impacts. The purpose of this study was to explore the nature and uses of Generative AI, and its effects on the learning and creativity of Gen Z, in addition to identifying the advantages, disadvantages, opportunities, and risks/partities’ concerns that are commensurate with the integration of this technology in teaching/learning and creative processes. To achieve the objectives of the study the following research methodology was used: The research used both a literature review and documentary research. The materials used included academic publications, Industry reports, books and other credible internet sources on Generative AI and its impact on the education and creativity of the Gen Z. The document analysis included policy papers, educational technology reports, case studies and white papers from academic and professional bodies as well as other industries that involve Generative AI. Several insights show that using Generative AI can positively impact learners’ experiences, engagement, and creativity. However, there was some controversy about the excessive usage of AI and claimed that because of it people may get worse at critical thinking. The following were noted to be major concerns; Ethical Issues: they included issues to do with bias in the algorithms as well as the right to privacy of data. Thus, the findings of this research point to a three-way settlement with respect to the use of Generative AI in education and creative industries. It underlines the guideline of how human creativity and critical thinking ought to be sustained, while using AI tools. Proposals include, the need to teach critical thinking alongside AI use, fostering ethical AI consciousness, surged AI education, appropriate non-ethnical AI data set, strong AI policies and pro positive AI inspires and creative constructive use. The research implications for future studies include studying the changes in the achievement of learning outcomes over a period of time, wherein Generative AI has been incorporated and understanding how this technology influences different learning styles and needs, the issues of ethical and privacy concern, the requirement of professional development to educators in relation to Generative AI and finally, the comparison information and communication technology for learning between different cultures. Related to that, further studies on the effectiveness of AI in approaches like collaborative learning, its potential on preparing learners for employment, and on the psychology of students would be helpful in informing the future advancement of Generative AI in school and particular creative areas.

ABSTRACTThe widespread applications of generative AI (GenAI) have sparked significant interest, with many organisations eager to leverage its transformative potential. Rather than focusing on individual organisations, this study examines GenAI integration within enterprise platforms, which are extensively adopted by many organisations and thus amplify both the benefits and risks of GenAI. We offer targeted recommendations for enterprise platform owners and their complementors, addressing challenges they face when integrating GenAI into these platforms. Drawing on a case study of Salesforce's experience, we recommend actions in three foundational areas – platform capability, architecture and governance – ensuring that our guidance is broadly applicable across enterprise platforms. In platform capability, we advise developing a unified GenAI stack built on existing platform services, offering generic and industry‐specific GenAI use cases to accelerate customer adoption and providing tools for customisation and creation of new use cases to enhance GenAI's transformational impact. For platform architecture, we recommend adding new layers for accommodating diverse GenAI foundation models and creating a trusted environment for secure data access, privacy and content monitoring. We also recommend implementing a prompt architecture to improve content relevance and accuracy. In platform governance, we recommend establishing new mechanisms to mitigate GenAI risks. Partnerships with GenAI providers and proactive investments in GenAI are essential to retain critical GenAI technologies. Personalised consultancy and training along with joint design and implementation with platform customers are also recommended. These combined actions, pursued in parallel across capability, architecture and governance, form a sustainable roadmap for GenAI integration in enterprise platforms.

This chapter elucidates what the major effects of generative artificial intelligence are: It changes things a lot. It first discusses the overall of what generative AI can do using one kind of generative AI, then it considers what generative AI does to make our defences against new things that can go wrong much more effective, and then it reflects on the major effects of generative AI on detecting malware and things that are subtly making our networks vulnerable. It emphasizes generative AI's ability to detect malware that was created to be hard to see with many examples of real things that attempt led to infection. Then it continues to discuss how generative AI interacts with threat intelligence feeds. It brings up that it is a way we can find out about cyber-attacks before they try to intrude. It goes on to relate to meaning how generative AI helps us with behavioural analysis and user authentication. It explains how we can protect privacy when learning about things that can go wrong using networks and threats. It focuses on collaborative learning and differential privacy. The next is to do with how well generative AI systems can handle ‘attacks' – what professionals call adversarial attacks. It wonders if they are scalable for cybersecurity, and lastly it stretches discussion on ethical and legal concerns. In conclusion this chapter suggests Generative AI has potential benefits which can be tapped for better security and privacy of the seamless digital devices connected online apart from it the chapter suggests collaboration among all the stakeholders connected to this network for good using better defense mechanisms which Gen AI can provide against intrusions and anomalies that can infect our networks. At the end this chapter wind up the discussion concluding that this chapter is aimed at specialists working in cybersecurity, researchers, and policymakers.

This research explores the innovative application of Generative AI (GenAI) in the context of Mars missions, drawing key insights from Mars analogue missions. Given the unique challenges of Mars exploration, including extreme environmental conditions, resource constraints, and the need for autonomous decision-making, GenAI emerges as a pivotal technology. We begin by introducing GenAI's capabilities, followed by an analysis of Mars analogue missions to understand the parallels and challenges relevant to actual Martian expeditions. We then delve into a variety of potential GenAI applications, ranging from data analysis, habitat design, and crew health monitoring, to psychological support and communication enhancements. Key case studies from analogue missions are presented to illustrate how GenAI could effectively address specific challenges faced during these simulations. Technical and ethical considerations are discussed to provide a comprehensive view of the implementation requirements and potential concerns. The paper also highlights how feedback from analogue missions can be instrumental in refining GenAI for future Mars missions. The concluding section of the paper speculates on the future trajectory of GenAI in space exploration and its role in advancing Mars colonization efforts. This research aims to bridge the gap between theoretical AI potential and practical application in space exploration, offering insightful recommendations for integrating GenAI into future Mars missions.

Starting today and due to the advent of generative AI, traditional enterprise portfolio management (EPM) is seriously disrupted, creating new opportunities for organizations to accelerate strategic alignment, optimize resource allocation, and foster innovation. This paper explores how generative AI will have the transformative power to create actionable insights and improve decision-making in EPM. Therefore, organizations can surpass traditional portfolio management's limitations and adapt to dynamic and real-time adjustments enabled by the use of predictive analytics and scenario modeling through AI-driven tools. AI's role in enhancing portfolio optimization, risk management, and project prioritization is discussed in this article, alongside challenges centered on data quality, workforce, and ethical considerations. It further explains the integration between AI and the existing enterprise system to provide a more encompassing, flexible approach to portfolio management. The paper applies generative AI in the real world through case studies from some leading organizations, which show how generative AI has been used to optimize portfolio performance, manage risks, and allocate resources efficiently. The paper ends with the vision for the future of EPM through generative AI, where generative AI powers more agile and data-driven decision-making processes. With each growing step of AI technology, it follows that businesses need to invest money to benefit from AI tools and train their staff for the transformation of AI-enhanced portfolio management.

A comprehensive exploration of the intersection between Generative Artificial Intelligence (GAI) and Wireless Sensor Networks (WSNs) , two transformative technologies reshaping data-driven systems. This book examines how generative AI can enhance wireless networks through advanced data analysis, anomaly detection, predictive modeling, and optimization, while also addressing the security risks and ethical challenges of its deployment. Beginning with an overview of GAI and its evolution, the book guides readers through real-world examples, case studies, and frameworks that demonstrate how generative AI can unlock new levels of efficiency and performance in WSNs. Key Features - Examines how Generative AI enhances wireless networks through advanced data analysis, anomaly detection, predictive modeling, and optimization - Demonstrates methods to improve network efficiency, reliability, and adaptability using AI-driven approaches - Addresses critical security risks and ethical challenges linked to the deployment of Generative AI - Explores innovative applications that integrate Generative AI into next-generation wireless systems - Evaluates the impact of AI-driven decision-making on corporate governance and technological adoption - Provides practical insights and case studies illustrating real-world implementations

Fintech players have always adopted the latest technology that evolve in the market to stay ahead in this competitive industry. In that adoption curve, we are now standing in the place where we are going to witness the adoption of the Generative AI as the next technological phenomenon that will shape the future of Fintech industry. In this chapter, the authors explore the following areas to getter a deeper insight into how Generative AI tends to play in Fintech industry: 1) Generative AI technologies involved in Fintech, 2) application of Generative AI in Fintech, 3) benefits of adopting Generative AI in Fintech, 4) case studies where Generative AI is adopted, 5) challenges and risks in adopting Generative AI, 6) mitigation strategies for the above said challenges and risks, and 7) future trends and developments.

This paper addresses the need for a positive and effective learning environment for engineering students in non-computer science fields to grasp Generative AI principles while navigating the intricate balance between its application and developmental insights. Drawing from pedagogical theories and cognitive science, especially Leinenbach and Corey (2004)’s Universal Design for Learning, this study proposes a framework tailored to the unique needs and backgrounds of engineering students. The framework emphasizes active learning strategies, collaborative problem-solving, and real-world applications to engage learners in meaningful experiences with Generative AI concepts. The central learning context is a M.Sc. program in management engineering with a course/training opportunity in Machine Learning Fundamentals using Python based on Google Collab. The introduction of Generative AI is based on selected Google libraries for Python. Furthermore, this paper explores various instructional approaches and tools to scaffold students' understanding of Generative AI, including hands-on projects, case studies, and interactive simulations. It also addresses ethical considerations and societal implications associated with Generative AI deployment, encouraging students to critically reflect on the broader impacts of their technical decisions. Through a synthesis of pedagogical best practices and AI development principles, this paper contributes to the ongoing discourse on effective AI education for non-computer science disciplines. By embracing a holistic approach that integrates theory with practical application, educators can empower engineering students to harness the transformative potential of Generative AI while navigating its complexities responsibly and ethically.

In 2022, high-performance generative AI — such as Stable Diffusion and ChatGPT — were reported upon and released amid increasing momentum for the utilization of such generative AI. In the field of architecture, generative AI is expected to not only be used for task automation, but as a means of diverging ideas as well, especially in the planning stage of architectural design. However, effective application methods have not yet been reported.Therefore, this study proposes a concept-making method that combines generative AI and ways to diverge ideas in architectural design and proposes a tuning method for ChatGPT to enable more effective dialogue. In the proposed method, ChatGPT is involved as a member in group work settings that aims to create concepts and initial designs using the fishbone diagram, one of the ways to list and categorize factors and ideas to achieve goals. In addition, ChatGPT is tuned to obtain more effective factors and ideas, particularly those related to spatial composition and shapes by inputting text regarding architectural design and specific architects.The proposed method was tested via case studies that created concepts and initial designs for an actual architectural competition. The results show that external ideas obtained from generative AI inspire the fishbone diagram process. The concepts and designs created seem imaginative and appropriate for competition.

This paper presents a comprehensive survey of the applications, challenges, and limitations of Generative AI (GenAI) in enhancing threat intelligence within cybersecurity, supported by real-world case studies. We examine a wide range of data sources in Cyber Threat Intelligence (CTI), including security reports, blogs, social media, network traffic, malware samples, dark web data, and threat intelligence platforms (TIPs). This survey provides a full reference for integrating GenAI into CTI. We discuss various GenAI models such as Large Language Models (LLMs) and Deep Generative Models (DGMs) like Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models, explaining their roles in detecting and addressing complex cyber threats. The survey highlights key applications in areas such as malware detection, network traffic analysis, phishing detection, threat actor attribution, and social engineering defense. We also explore critical challenges in deploying GenAI, including data privacy, security concerns, and the need for interpretable and transparent models. As regulations like the European Commission’s AI Act emerge, ensuring trustworthy AI solutions is becoming more crucial. Real-world case studies, such as the impact of the WannaCry ransomware, the rise of deepfakes, and AI-driven social engineering, demonstrate both the potential and current limitations of GenAI in CTI. Our goal is to provide foundational insights and strategic direction for advancing GenAI’s role in future cybersecurity frameworks, emphasizing the importance of innovation, adaptability, and ongoing learning to enhance resilience against evolving cyber threats. Ultimately, this survey offers critical insights into how GenAI can shape the future of cybersecurity by addressing key challenges and providing actionable guidance for effective implementation.

Generative AI (GenAI) has the potential to affect artists’ control over their own music due to the illegal usage of copyrighted material for training. However, GenAI also creates exciting opportunities for artists to expand their material and working processes. Artists working with GenAI and documenting their outcomes can assist other artists as well as wider society in understanding how GenAI operates and can benefit human artistic output. This paper provides an autoethnographic case study into how a new GenAI tool influenced an established composing practice during the writing of the experimental musical work, Control Yourself (2023). The Koup Music prototype by Kopi Su Studio was trained on vocal inputs by the author and subsequently generated bespoke sonic material. While identifiably true to the author’s musical—and literal—voice, the outputs were novel and perceived as imbued with emotion, leading to subsequent anthropomorphising of the AI. Written by a former AI sceptic, this paper details how the emotive power of the AI’s non-verbal, human-like sounds informed the narrative and structure of the resulting work and imparted a sense of collaboration, rather than solo authorship. Furthermore, the influence of the AI was felt beyond its actual involvement, with the project taking on a more playful approach less centred on the artistic control of the human composer. Following these observations, this paper discusses how GenAI served as a tool for musical experimentation and exploring creative ‘blind spots.’ These insights are also contextualised by current discourse on the perception and use of GenAI in the arts, the role of artistic control in human–AI co-creation, and how anthropomorphism has manifested in past human–AI partnerships.

This case study summarised the findings from the first academic presentation of the module ‘Cómo entrenar tu dragon (How to train your dragon)’, which is part of the development and training programme offered by the Universidad de Las Palmas de Gran Canaria to new and experienced teaching staff. The module aims to equip participants with a theoretical understanding and practical skills to use Generative AI (GenAI) multimodal languages to enhance learning and to develop a critical awareness of the umbrella term ‘AI literacy’. The module covers topics such as GenAI and data protection, copyright issues, regulatory frameworks, getting familiar with GenAI large language models (LLM) and the impact of GenAI on assessment. The case study provides information on the type of learning activities undertaken by participants and the final summative assessment. Participants found the module insightful, giving it a total score of 4.813 out of 5 on a Likert scale. Participant feedback suggests that institutional guidance and policies are needed to introduce GenAI tools at the Universidad de Las Pal mas de Gran Canaria so they can use GenAI tools confidently with their students.

Abstract—Generative AI has emerged as a promising solution for automated analysis and validation of the final outgate quality in semiconductor manufacturing. This review explores the potential of leveraging generative AI models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers, to address the challenges faced by traditional quality control methods in the semiconductor industry. These models offer unique capabilities for image analysis, defect detection, and process optimization, enabling more accurate and efficient quality control processes. Applications of generative AI in semiconductor manufacturing include defect classification, anomaly detection, predictive maintenance, and process simulation. By learning complex data distributions and generating synthetic data, generative AI can enhance the robustness and generalization of defect-detection models, capture subtle defect patterns, and discover novel defect types without explicit labeling. However, implementing generative AI in real-time manufacturing environments presents challenges related to the computational requirements, model interpretability, and integration with existing workflows. Addressing these challenges requires careful consideration of the data quality, model architecture, and deployment strategies. Case studies demonstrated the significant benefits of generative AI in improving defect detection, increasing yield, reducing time-to-market, and lowering manufacturing costs. As technology continues to evolve, future research should focus on emerging trends such as the AI-driven design of new materials and devices, while addressing ethical considerations and potential workforce impacts. This review provides a comprehensive overview of the current state and future directions of generative AI in semiconductor manufacturing, offering valuable insights for researchers and practitioners in the field. Keywords—semiconductor manufacturing, Generative AI, quality control, defect detection, final outgate quality, process optimization, anomaly detection

Data storytelling combines communication and visualization techniques with storytelling principles to convey insights meaningfully and engagingly. This paper introduces the AI‐DIKW framework, an extension of the Story Data–Information–Knowledge–Wisdom (S‐DIKW) hierarchy, designed to support the co‐creation of data‐driven stories by combining human expertise with Generative AI. The AI‐DIKW framework extends the S‐DIKW model through the application of the journalistic 5‐Ws, which guide Generative AI to provide consistent and contextually relevant outputs. This study describes how Generative AI can contribute to the iterative construction of a story's key components while ensuring alignment with ethical principles. Applying Generative AI at the AI‐Data stage involves extracting insights from the data. Applying Generative AI at the AI‐Information stage means enriching the insight with relevant context, while at the AI‐Knowledge stage, Generative AI helps the data storyteller to enrich the story with meaningful next steps, anchored to a selected ethical framework. Finally, at the AI‐Wisdom stage, Generative AI helps the data storyteller to tailor the story to specific audiences. At each stage of the AI‐DIKW framework, Generative AI acts as a co‐designer that helps the data storyteller to frame and edit the story. The article also describes a case study that applies the concepts described practically.