Relevant bibliographies by topics / Generative Artificial Intelligence (GenAI)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Generative Artificial Intelligence (GenAI)'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Generative Artificial Intelligence (GenAI)"

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Hasan, A. K. M. Kamrul. "Governance of Generative Artificial Intelligence." International Journal of Knowledge Management 21, no. 1 (2025): 1–21. https://doi.org/10.4018/ijkm.383061.

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The field of knowledge management and knowledge management systems is evolving and dynamic. In the era of developed information technology systems, the dynamics of knowledge creation and dissemination have also changed. Generative artificial intelligence (GenAI)—an embedded entity in the knowledge management system—has become a prominent area of research nowadays, while accountability, transparency, and ethics are common research agendas in institutional economics related to GenAI. The research in this paper has investigated the convictions behind GenAI adoption and how to develop a GenAI governance framework. The research adopts a qualitative approach to investigate the problem and surveys undergraduate students to explore their motive for using GenAI. The study sheds analytical light on institutional economists' view on the governance of GenAI. The study has found a positive relationship between perceived benefits and the adoption of GenAI in education by students. The theoretical model will have a considerable impact on the ongoing debate on the governance of GenAI and knowledge management systems.
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Dimitrieska, Savica. "Generative Artificial Intelligence and Advertising." Trends in Economics, Finance and Management Journal 6, no. 1 (2024): 23–34. http://dx.doi.org/10.69648/eyzi2281.

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Generative artificial intelligence, the new buzzword in technology, is the next step in the evolution of traditional artificial intelligence. Unlike traditional AI that excels in data analyzing and automating processes, generative AI (GenAI) is a pioneer in creating new and original content. GenAI is very close to human intelligence, capable of logical thinking, imitating human behavior and armed with decision making capabilities. Generative AI creates new texts, images, music, 3D designs and codes, thus strongly influencing the activities, strategies, and consumer interactions of various industries. Key industries most affected by GenAI are banking and finance, retail and consumer goods, medicine and pharmaceuticals, education, media and marketing. In marketing, generative AI is significant in the process of personalization, content creation, audience engagement and interactions, performing the STP strategy (segmentation, targeting, positioning), market research, etc. Although it has great advantages, GenAI also has significant limitations, such as unresolved ethical issues, the spread of outdated or imprecise data, lack of legal regulation and control, etc. This paper, with the aid of secondary research, is aimed at exploring the possibilities of GenAI and its impact on marketing, especially advertising.
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Alavi, Maryam, Dorothy E. Leidner, and Reza Mousavi. "Knowledge Management Perspective of Generative Artificial Intelligence." Journal of the Association for Information Systems 25, no. 1 (2024): 1–12. http://dx.doi.org/10.17705/1jais.00859.

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In this editorial, revisiting Alavi and Leidner (2001) as a conceptual lens, we consider the organizational implications of Generative Artificial Intelligence (GenAI) from a knowledge management (KM) perspective. We examine how GenAI impact the processes of knowledge creation, storage, transfer, and application, highlighting both the opportunities and challenges this technology presents. In knowledge creation, GenAI enhances information? processing and cognitive functions, fostering individual and organizational learning. However, it also introduces risks like AI bias and reduced human socialization, potentially marginalizing junior knowledge workers. For knowledge storage and retrieval, GenAI’s ability to quickly access vast knowledge bases significantly changes employee interactions with KM systems. This raises questions about balancing human-derived tacit knowledge with AI-generated explicit knowledge. The paper also explores GenAI’s role in knowledge transfer, particularly in training and cultivating a learning culture. Challenges include an over-reliance on AI and risks in disseminating sensitive information. In terms of knowledge application, GenAI is seen as a tool to boost productivity and innovation, but issues like knowledge misapplication, intellectual property, and ethical considerations are critical. Conclusively, the paper argues for a balanced approach to integrating GenAI into KM processes. It advocates for harmonizing GenAI’s capabilities with human insights to effectively manage knowledge in contemporary organizations, ensuring both technological advances and ethical responsibility.
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Hombana, Thamsanqa. "Knowledge Generation Through Open Access and Generative Artificial Intelligence." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40817.

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The amount of data generated by scientific knowledge is growing exponentially, much faster than natural intelligence, making it increasingly more challenging for researchers to manage their data. Reputable Generative AI (GenAI) is seen as a crucial instrument to help modern researchers facilitate ethical publications and knowledge production. This study looks at how South African researchers can move the development of ethical knowledge using artificial intelligence by utilizing open access methodologies and practices. GenAI has completely changed the scientific research landscape by providing advanced tools for knowledge generation, data management, and ethical publishing. In South Africa, researchers are using GenAI more and more to handle the complexity of modern research data. The goal of this paper is to demonstrate how functional knowledge can be produced and utilized through AI tools, emphasizing the advantages of open access to scientific data and its moral ramifications. When ethical GenAI and Open Access (OA) are incorporated into South African higher education, the implications for universal access to knowledge are profound. The creation and distribution of scientific knowledge are significantly impacted by OA since it reduces the time needed to share research findings and broadens the pool of potential knowledge recipients (Bernius 2010b). Key Words: Open Access, Ethical Knowledge Creation, Artificial intelligence-generated Scientific Content, South Africa, Research Ethics, Open Science, Knowledge Equity
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Ittefaq, Muhammad, Ali Zain, Rauf Arif, Taufiq Ahmad, Laeeq Khan, and Hyunjin Seo. "Factors influencing international students’ adoption of generative artificial intelligence." Journal of International Students 15, no. 7 (2025): 127–54. https://doi.org/10.32674/fnwdpn48.

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The present study examines the factors influencing international students’ intentions to use generative artificial intelligence (GenAI). Our results showed that attitude toward GenAI use, perceived ease of use, perceived usefulness, enjoyment, subjective norms, novelty, trust in technology, perceived value, and AI literacy were positively associated with intention to use GenAI. Fear of plagiarism had a negative relationship with intention to use GenAI. Our mediation analysis suggested that trust in technology, perceived ease of use, fear of plagiarism, perceived usefulness, and AI literacy indirectly influenced GenAI usage intention via attitude and perceived value, underscoring both the appeal and concerns of GenAI in learning. This study contributed to the TPB, VAM, and TAM frameworks by incorporating fear of plagiarism, trust in technology, and AI literacy to demonstrate how cognitive, affective, and value-based factors collectively influence the adoption of GenAI technologies among international students.
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Aja, Lucy, Muhammad Tukur, Aquila Modupe Otitoju, and Mohammad Lubega. "Generative artificial intelligence tools in education research: Applications, and methodological enhancements." KIU Journal of Education 5, no. 1 (2025): 183–94. https://doi.org/10.59568/kjed-2025-5-1-23.

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This opinion paper discusses the rapid development of generative artificial intelligence (GenAI) tools, which have significantly impacted educational research. This study examines the diverse applications of GenAI in educational settings, highlighting how it can enhance data analysis, automate literature reviews using generative AI tools, and facilitate personalized learning. By incorporating GenAI techniques such as automated content generation, researchers may expedite the data collecting process, generate insights from large-scale datasets, and develop adaptive learning materials that respond to student needs individually. This study also demonstrates methodological improvements made possible by GenAI, such as enhanced research design and the promotion of collaboration across disciplines. The researchers highlight best practices and potential pitfalls related to using GenAI tools in education research through a review of recent literature and case studies. As generative AI continues to impact the educational system, researchers and educators must exercise caution to maximize its potential. This paper's ultimate goal is to give researchers and educators a framework for efficiently utilizing GenAI technology, stressing the value of data integrity and ethical issues in promoting creative research approaches.
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Huo, Xuenan, and Keng Leng Siau. "Generative Artificial Intelligence in Business Higher Education." Journal of Global Information Management 32, no. 1 (2024): 1–21. https://doi.org/10.4018/jgim.364093.

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This research investigates the opportunities and challenges of integrating generative artificial intelligence (GenAI) into business higher education, drawing insights from an asynchronous focus group research study with doctoral students who serve dual roles as both learners and educators. Key opportunities identified through thematic analysis include knowledge acquisition, intelligent co-ideation, supportive augmentation, and personalized learning. Challenges identified include AI trustworthiness, cognitive dependency, human value, policy and instruction, assessment integrity, and identity management. This study clarifies GenAI's specific role in business education and provides practical insights for effectively integrating GenAI to enhance learning outcomes and address emerging challenges. An analysis theory on the opportunities and challenges of GenAI on business higher education is developed and described in the paper. The potential impact of Agentic Artificial Intelligence (autonomous AI agents) and Artificial General Intelligence (AGI) on education is also discussed.
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Bodini, Matteo. "Generative Artificial Intelligence and Regulations: Can We Plan a Resilient Journey Toward the Safe Application of Generative Artificial Intelligence?" Societies 14, no. 12 (2024): 268. https://doi.org/10.3390/soc14120268.

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The rapid advancements of Generative Artificial Intelligence (GenAI) technologies, such as the well-known OpenAI ChatGPT and Microsoft Copilot, have sparked significant societal, economic, and regulatory challenges. Indeed, while the latter technologies promise unprecedented productivity gains, they also raise several concerns, such as job loss and displacement, deepfakes, and intellectual property violations. The present article aims to explore the present regulatory landscape of GenAI across the major global players, highlighting the divergent approaches adopted by the United States, United Kingdom, China, and the European Union. By drawing parallels with other complex global issues such as climate change and nuclear proliferation, this paper argues that the available traditional regulatory frameworks may be insufficient to address the unique challenges posed by GenAI. As a result, this article introduces a resilience-focused regulatory approach that emphasizes aspects such as adaptability, swift incident response, and recovery mechanisms to mitigate potential harm. By analyzing the existing regulations and suggesting potential future directions, the present article aims to contribute to the ongoing discourse on how to effectively govern GenAI technologies in a rapidly evolving regulatory landscape.
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PUGACHEVA, O. V. "GENERATIVE ARTIFICIAL INTELLIGENCE IN ACTION: ENHANCING PRODUCTIVITY AND MANAGEMENT EFFICIENCY." Herald of Omsk University. Series: Economics 22, no. 3 (2024): 24–32. http://dx.doi.org/10.24147/1812-3988.2024.22(3).24-32.

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This article presents a critical analysis of the influence of generative artificial intelligence (GenAI) on the productivity of key organizational leaders (KOLs), regardless of industry. Specific attention is paid to exploring the potential application of GenAI in enhancing managerial functions, streamlining certain tasks, and integrating seamlessly into existing processes. The research aims to classify GenAI and evaluate its potential impact on productivity. It also aims to describe tested instances of GenAI application in KOL workflows for improved efficiency and output, and to propose a step-by-step guide for the implementation of GenAI tools. This study is relevant due to the need to incorporate advanced technologies into organizational processes to enhance organizational competitiveness in the face of a rapidly evolving business landscape. The study employs a systematic and comparative analysis of analytical reports and academic databases from the period 2023-2024 in order to review the use and impact of GenAI on productivity across various functions and industries. The analysis also includes modeling methods for evaluating the proposed recommendations. As a result of this analytical review, a classification of modern approaches to the use of GenAI in management was developed. Options for using GenAI for day-to-day KOL work are proposed. Additionally, an approach to implementing GenAI tools has been suggested.
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Fleischmann, Katja. "Generative Artificial Intelligence in Graphic Design Education: A Student Perspective." Canadian Journal of Learning and Technology 50, no. 1 (2024): 1–17. http://dx.doi.org/10.21432/cjlt28618.

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Generative Artificial Intelligence (GenAI) is re-defining the way higher education design is taught and learned. The explosive growth of GenAI in design practice demands that design educators ensure students are prepared to enter the design profession with the knowledge and experience of using GenAI. To facilitate GenAI’s introduction in a project-based context, it is suggested that design educators use critical engagement as a starting point to assure students understand the strengths and weakness of GenAI in the creative design process. There is little guidance on how to systematically integrate GenAI in design studio practice while maintaining a critical perspective of the ethical issues it has engendered. This research explores student attitudes toward GenAI, frequency of its use, and student perception of its impact on their future design careers. A survey of a representative cohort of graphic design students (n = 17) reveals a pragmatic acceptance that GenAI will change how design is practiced and a concurrent willingness to learn more on how to use it effectively and ethically. The survey validates the need for design educators to engage and guide students critically in their understanding and use of GenAI within studio and professional practice.
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Dissertations / Theses on the topic "Generative Artificial Intelligence (GenAI)"

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Benedetti, Riccardo. "From Artificial Intelligence to Artificial Art: Deep Learning with Generative Adversarial Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18167/.

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Neural Network had a great impact on Artificial Intelligence and nowadays the Deep Learning algorithms are widely used to extract knowledge from huge amount of data. This thesis aims to revisit the evolution of Deep Learning from the origins till the current state-of-art by focusing on a particular prospective. The main question we try to answer is: can AI exhibit artistic abilities comparable to the human ones? Recovering the definition of the Turing Test, we propose a similar formulation of the concept, indeed, we would like to test the machine's ability to exhibit artistic behaviour equivalent to, or indistinguishable from, that of a human. The argument we will analyze as a support for this debate is an interesting and innovative idea coming from the field of Deep Learning, known as Generative Adversarial Network (GAN). GAN is basically a system composed of two neural network fighting each other in a zero-sum game. The ''bullets'' fired during this challenge are simply images generated by one of the two networks. The interesting part in this scenario is that, with a proper system design and training, after several iteration these fake generated images start to become more and more closer to the ones we see in the reality, making indistinguishable what is real from what is not. We will talk about some real anecdotes around GANs to spice up even more the discussion generated by the question previously posed and we will present some recent real world application based on GANs to emphasize their importance also in term of business. We will conclude with a practical experiment over an Amazon catalogue of clothing images and reviews with the aim of generating new never seen product starting from the most popular existing ones.
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ABUKMEIL, MOHANAD. "UNSUPERVISED GENERATIVE MODELS FOR DATA ANALYSIS AND EXPLAINABLE ARTIFICIAL INTELLIGENCE." Doctoral thesis, Università degli Studi di Milano, 2022. http://hdl.handle.net/2434/889159.

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For more than a century, the methods of learning representation and the exploration of the intrinsic structures of data have developed remarkably and currently include supervised, semi-supervised, and unsupervised methods. However, recent years have witnessed the flourishing of big data, where typical dataset dimensions are high, and the data can come in messy, missing, incomplete, unlabeled, or corrupted forms. Consequently, discovering and learning the hidden structure buried inside such data becomes highly challenging. From this perspective, latent data analysis and dimensionality reduction play a substantial role in decomposing the exploratory factors and learning the hidden structures of data, which encompasses the significant features that characterize the categories and trends among data samples in an ordered manner. That is by extracting patterns, differentiating trends, and testing hypotheses to identify anomalies, learning compact knowledge, and performing many different machine learning (ML) tasks such as classification, detection, and prediction. Unsupervised generative learning (UGL) methods are a class of ML characterized by their possibility of analyzing and decomposing latent data, reducing dimensionality, visualizing the manifold of data, and learning representations with limited levels of predefined labels and prior assumptions. Furthermore, explainable artificial intelligence (XAI) is an emerging field of ML that deals with explaining the decisions and behaviors of learned models. XAI is also associated with UGL models to explain the hidden structure of data, and to explain the learned representations of ML models. However, the current UGL models lack large-scale generalizability and explainability in the testing stage, which leads to restricting their potential in ML and XAI applications. To overcome the aforementioned limitations, this thesis proposes innovative methods that integrate UGL and XAI to enable data factorization and dimensionality reduction to improve the generalizability of the learned ML models. Moreover, the proposed methods enable visual explainability in modern applications as anomaly detection and autonomous driving systems. The main research contributions are listed as follows: • A novel overview of UGL models including blind source separation (BSS), manifold learning (MfL), and neural networks (NNs). Also, the overview considers open issues and challenges among each UGL method. • An innovative method to identify the dimensions of the compact feature space via a generalized rank in the application of image dimensionality reduction. • An innovative method to hierarchically reduce and visualize the manifold of data to improve the generalizability in limited data learning scenarios, and computational complexity reduction applications. • An original method to visually explain autoencoders by reconstructing an attention map in the application of anomaly detection and explainable autonomous driving systems. The novel methods introduced in this thesis are benchmarked on publicly available datasets, and they outperformed the state-of-the-art methods considering different evaluation metrics. Furthermore, superior results were obtained with respect to the state-of-the-art to confirm the feasibility of the proposed methodologies concerning the computational complexity, availability of learning data, model explainability, and high data reconstruction accuracy.
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Konuko, Goluck. "Low Bitrate Face Video Compression with Generative Animation Models." Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPAST014.

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Cette thèse aborde le défi de réaliser une compression vidéo à ultra-faible débit pour la vidéoconférence, en se concentrant sur la préservation d'une haute qualité visuelle tout en minimisant la bande passante de transmission. Les codecs traditionnels comme HEVC et VVC éprouvent des difficultés à très bas débits, particulièrement pour représenter avec précision les expressions faciales dynamiques, les mouvements de tête et les occlusions, qui sont essentiels pour le réalisme et la précision dans la communication en face à face. Pour surmonter ces limitations, cette recherche développe une méthode de compression basée sur l'apprentissage utilisant des modèles génératifs profonds et améliore leurs performances grâce à la transmission d'informations auxiliaires ou au codage prédictif à bas débit.Le Deep Animation Codec (DAC) est introduit comme une solution qui utilise des modèles génératifs pour encoder les mouvements faciaux liés à la parole via une représentation compacte des points clés de mouvement, réalisant ainsi des réductions substantielles du débit binaire. Pour traiter les limitations du DAC avec des poses de tête complexes et des occlusions, le Multi-Reference DAC (MRDAC) utilise plusieurs images de référence et l'apprentissage contrastif pour améliorer la précision de reconstruction dans des conditions difficiles. En s'appuyant sur cela, le Hybrid Deep Animation Codec (HDAC) intègre des codecs vidéo traditionnels avec des cadres génératifs pour atteindre une qualité adaptative, encore améliorée par l'apprentissage à débit binaire variable et un mécanisme de transfert des hautes fréquences (HF) pour une reconstruction détaillée. Enfin, nous avons exploré une approche de codage prédictif à ultra-faible débit, mettant en évidence les défis associés et les outils d'optimisation qui peuvent être utilisés pour apprendre efficacement un codage résiduel compact à bas débit. Plus précisément, le cadre de codage prédictif proposé (RDAC) exploite les dépendances temporelles et l'apprentissage résiduel conditionnel pour atteindre un compromis robuste entre la perte d'information et l'évolutivité de la qualité dans les contraintes du codage à faible débit.Collectivement, ces contributions font progresser le domaine en permettant une compression vidéo robuste et de haute qualité à ultra-faible débit, améliorant la faisabilité de la vidéoconférence et les applications potentielles en réalité virtuelle ainsi que le stockage efficace de contenu vidéo de type « talking-head »<br>This thesis addresses the challenge of achieving ultra-low bitrate video compression for video conferencing by focusing on the preservation of high visual quality while minimizing transmission bandwidth. Traditional codecs like HEVC and VVC struggle at very low bitrates, particularly with accurately representing dynamic facial expressions, head movements, and occlusions, which are important for realism and accuracy in face-to-face communication. To overcome these limitations, this research develops learning-based compression method using deep generative models and enhances their performance through side information transmission or predictive coding at low bitrates.The Deep Animation Codec (DAC) is introduced as a solution that uses generative models to encode speech-related facial motion through a compact representation of motion keypoints, achieving substantial bitrate reductions. To address DAC's limitations with complex head poses and occlusions, the Multi-Reference DAC (MRDAC) uses multiple reference frames and contrastive learning to enhance reconstruction accuracy under challenging conditions. Building on this, the Hybrid Deep Animation Codec (HDAC) integrates traditional video codecs with generative frameworks to achieve adaptive quality, further improved by variable bitrate learning and a High-Frequency (HF) shuttling mechanism for detailed reconstruction. Finally, we explored an approach to predictive coding at ultra-low bitrates showing the associated challenges and optimization tools that can be used to effectively learn compact residual coding at low bitrates. Specifically, the proposed predictive coding framework (RDAC) exploits temporal dependencies and conditional residual learning to achieve a robust trade-off between information loss and quality scalability within the constraints of low bitrate coding. Collectively, these contributions advance the field by enabling robust, high-quality video compression at ultra-low bitrates, enhancing the feasibility of video conferencing and potential applications in virtual reality and efficient storage of talking-head video content
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Griffith, Todd W. "A computational theory of generative modeling in scientific reasoning." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/8177.

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Navaroli, Nicholas Martin. "Generative Probabilistic Models for Analysis of Communication Event Data with Applications to Email Behavior." Thesis, University of California, Irvine, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3668831.

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<p> Our daily lives increasingly involve interactions with others via different communication channels, such as email, text messaging, and social media. In this context, the ability to analyze and understand our communication patterns is becoming increasingly important. This dissertation focuses on generative probabilistic models for describing different characteristics of communication behavior, focusing primarily on email communication. </p><p> First, we present a two-parameter kernel density estimator for estimating the probability density over recipients of an email (or, more generally, items which appear in an itemset). A stochastic gradient method is proposed for efficiently inferring the kernel parameters given a continuous stream of data. Next, we apply the kernel model and the Bernoulli mixture model to two important prediction tasks: given a partially completed email recipient list, 1) predict which others will be included in the email, and 2) rank potential recipients based on their likelihood to be added to the email. Such predictions are useful in suggesting future actions to the user (i.e. which person to add to an email) based on their previous actions. We then investigate a piecewise-constant Poisson process model for describing the time-varying communication rate between an individual and several groups of their contacts, where changes in the Poisson rate are modeled as latent state changes within a hidden Markov model. </p><p> We next focus on the time it takes for an individual to respond to an event, such as receiving an email. We show that this response time depends heavily on the individual's typical daily and weekly patterns - patterns not adequately captured in standard models of response time (e.g. the Gamma distribution or Hawkes processes). A time-warping mechanism is introduced where the absolute response time is modeled as a transformation of effective response time, relative to the daily and weekly patterns of the individual. The usefulness of applying the time-warping mechanism to standard models of response time, both in terms of log-likelihood and accuracy in predicting which events will be quickly responded to, is illustrated over several individual email histories.</p>
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Franceschelli, Giorgio. "Generative Deep Learning and Creativity." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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“Non ha la presunzione di originare nulla; può solo fare ciò che noi sappiamo ordinarle di fare”. Così, oltre 150 anni fa, Lady Lovelace commentava la Macchina Analitica di Babbage, l’antenato dei nostri computer. Una frase che, a distanza di tanti anni, suona quasi come una sfida: grazie alla diffusione delle tecniche di Generative Deep Learning e alle ricerche nell’ambito della Computational Creativity, sempre più sforzi sono stati destinati allo smentire l’ormai celebre Obiezione della Lovelace. Proprio a partire da questa, quattro domande formano i capisaldi della Computational Creativity: se è possibile sfruttare tecniche computazionali per comprendere la creatività umana; e, soprattutto, se i computer possono fare cose che sembrino creative (se non che siano effettivamente creative), e se possono imparare a riconoscere la creatività. Questa tesi si propone dunque di inserirsi in tale contesto, esplorando queste ultime due questioni grazie a tecniche di Deep Learning. In particolare, sulla base della definizione di creatività proposta da Margaret Boden, è presentata una metrica data dalla somma pesata di tre singole componenti (valore, novità e sorpresa) per il riconoscimento della creatività. In aggiunta, sfruttando tale misura, è presentato anche UCAN (Unexpectedly Creative Adversarial Network), un modello generativo orientato alla creatività, che impara a produrre opere creative massimizzando la metrica di cui sopra. Sia il generatore sia la metrica sono stati testati sul contesto della poesia americana del diciannovesimo secolo; i risultati ottenuti mostrano come la metrica sia effettivamente in grado di intercettare la traiettoria storica, e come possa rappresentare un importante passo avanti per lo studio della Computational Creativity; il generatore, pur non ottenendo risultati altrettanto eccellenti, si pone quale punto di partenza per la definizione futura di un modello effettivamente creativo.
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Nilsson, Alexander, and Martin Thönners. "A Framework for Generative Product Design Powered by Deep Learning and Artificial Intelligence : Applied on Everyday Products." Thesis, Linköpings universitet, Maskinkonstruktion, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-149454.

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In this master’s thesis we explore the idea of using artificial intelligence in the product design process and seek to develop a conceptual framework for how it can be incorporated to make user customized products more accessible and affordable for everyone. We show how generative deep learning models such as Variational Auto Encoders and Generative Adversarial Networks can be implemented to generate design variations of windows and clarify the general implementation process along with insights from recent research in the field. The proposed framework consists of three parts: (1) A morphological matrix connecting several identified possibilities of implementation to specific parts of the product design process. (2) A general step-by-step process on how to incorporate generative deep learning. (3) A description of common challenges, strategies andsolutions related to the implementation process. Together with the framework we also provide a system for automatic gathering and cleaning of image data as well as a dataset containing 4564 images of windows in a front view perspective.
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Delacruz, Gian P. "Using Generative Adversarial Networks to Classify Structural Damage Caused by Earthquakes." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2158.

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The amount of structural damage image data produced in the aftermath of an earthquake can be staggering. It is challenging for a few human volunteers to efficiently filter and tag these images with meaningful damage information. There are several solution to automate post-earthquake reconnaissance image tagging using Machine Learning (ML) solutions to classify each occurrence of damage per building material and structural member type. ML algorithms are data driven; improving with increased training data. Thanks to the vast amount of data available and advances in computer architectures, ML and in particular Deep Learning (DL) has become one of the most popular image classification algorithms producing results comparable to and in some cases superior to human experts. These kind of algorithms need the input images used for the training to be labeled, and even if there is a large amount of images most of them are not labeled and it takes structural engineers a large amount of time to do it. The current data earthquakes image data bases do not contain the label information or is incomplete slowing significantly the advance of a solution and are incredible difficult to search. To be able to train a ML algorithm to classify one of the structural damages it took the architecture school an entire year to gather 200 images of the specific damage. That number is clearly not enough to avoid overfitting so for this thesis we decided to generate synthetic images for the specific structural damage. In particular we attempt to use Generative Adversarial Neural Networks (GANs) to generate the synthetic images and enable the fast classification of rail and road damage caused by earthquakes. Fast classification of rail and road damage can allow for the safety of people and to better prepare the reconnaissance teams that manage recovery tasks. GANs combine classification neural networks with generative neural networks. For this thesis we will be combining a convolutional neural network (CNN) with a generative neural network. By taking a classifier trained in a GAN and modifying it to classify other images the classifier can take advantage of the GAN training without having to find more training data. The classifier trained in this way was able to achieve an 88\% accuracy score when classifying images of structural damage caused by earthquakes.
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Mallik, Mohammed Tariqul Hassan. "Electromagnetic Field Exposure Reconstruction by Artificial Intelligence." Electronic Thesis or Diss., Université de Lille (2022-....), 2023. https://pepite-depot.univ-lille.fr/ToutIDP/EDENGSYS/2023/2023ULILN052.pdf.

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Le sujet de l'exposition aux champs électromagnétiques a fait l'objetd'une grande attention à la lumière du déploiement actuel du réseaucellulaire de cinquième génération (5G). Malgré cela, il reste difficilede reconstituer avec précision le champ électromagnétique dans unerégion donnée, faute de données suffisantes. Les mesures in situ sontd'un grand intérêt, mais leur viabilité est limitée, ce qui renddifficile la compréhension complète de la dynamique du champ. Malgré legrand intérêt des mesures localisées, il existe encore des régions nontestées qui les empêchent de fournir une carte d'exposition complète. Larecherche a exploré des stratégies de reconstruction à partird'observations provenant de certains sites localisés ou de capteursdistribués dans l'espace, en utilisant des techniques basées sur lagéostatistique et les processus gaussiens. En particulier, desinitiatives récentes se sont concentrées sur l'utilisation del'apprentissage automatique et de l'intelligence artificielle à cettefin. Pour surmonter ces problèmes, ce travail propose de nouvellesméthodologies pour reconstruire les cartes d'exposition aux CEM dans unezone urbaine spécifique en France. L'objectif principal est dereconstruire des cartes d'exposition aux ondes électromagnétiques àpartir de données provenant de capteurs répartis dans l'espace. Nousavons proposé deux méthodologies basées sur l'apprentissage automatiquepour estimer l'exposition aux ondes électromagnétiques. Pour la premièreméthode, le problème de reconstruction de l'exposition est défini commeune tâche de traduction d'image à image. Tout d'abord, les données ducapteur sont converties en une image et l'image de référencecorrespondante est générée à l'aide d'un simulateur basé sur le tracédes rayons. Nous avons proposé un réseau adversarial cGAN conditionnépar la topologie de l'environnement pour estimer les cartes d'expositionà l'aide de ces images. Le modèle est entraîné sur des images de cartesde capteurs tandis qu'un environnement est donné comme entréeconditionnelle au modèle cGAN. En outre, la cartographie du champélectromagnétique basée sur le Generative Adversarial Network estcomparée au simple Krigeage. Les résultats montrent que la méthodeproposée produit des estimations précises et constitue une solutionprometteuse pour la reconstruction des cartes d'exposition. Cependant,la production de données de référence est une tâche complexe car elleimplique la prise en compte du nombre de stations de base actives dedifférentes technologies et opérateurs, dont la configuration du réseauest inconnue, par exemple les puissances et les faisceaux utilisés parles stations de base. En outre, l'évaluation de ces cartes nécessite dutemps et de l'expertise. Pour répondre à ces questions, nous avonsdéfini le problème comme une tâche d'imputation de données manquantes.La méthode que nous proposons prend en compte l'entraînement d'un réseauneuronal infini pour estimer l'exposition aux champs électromagnétiques.Il s'agit d'une solution prometteuse pour la reconstruction des cartesd'exposition, qui ne nécessite pas de grands ensembles d'apprentissage.La méthode proposée est comparée à d'autres approches d'apprentissageautomatique basées sur les réseaux UNet et les réseaux adversairesgénératifs conditionnels, avec des résultats compétitifs<br>The topic of exposure to electromagnetic fields has received muchattention in light of the current deployment of the fifth generation(5G) cellular network. Despite this, accurately reconstructing theelectromagnetic field across a region remains difficult due to a lack ofsufficient data. In situ measurements are of great interest, but theirviability is limited, making it difficult to fully understand the fielddynamics. Despite the great interest in localized measurements, thereare still untested regions that prevent them from providing a completeexposure map. The research explored reconstruction strategies fromobservations from certain localized sites or sensors distributed inspace, using techniques based on geostatistics and Gaussian processes.In particular, recent initiatives have focused on the use of machinelearning and artificial intelligence for this purpose. To overcome theseproblems, this work proposes new methodologies to reconstruct EMFexposure maps in a specific urban area in France. The main objective isto reconstruct exposure maps to electromagnetic waves from some datafrom sensors distributed in space. We proposed two methodologies basedon machine learning to estimate exposure to electromagnetic waves. Forthe first method, the exposure reconstruction problem is defined as animage-to-image translation task. First, the sensor data is convertedinto an image and the corresponding reference image is generated using aray tracing-based simulator. We proposed an adversarial network cGANconditioned by the environment topology to estimate exposure maps usingthese images. The model is trained on sensor map images while anenvironment is given as conditional input to the cGAN model.Furthermore, electromagnetic field mapping based on the GenerativeAdversarial Network is compared to simple Kriging. The results show thatthe proposed method produces accurate estimates and is a promisingsolution for exposure map reconstruction. However, producing referencedata is a complex task as it involves taking into account the number ofactive base stations of different technologies and operators, whosenetwork configuration is unknown, e.g. powers and beams used by basestations. Additionally, evaluating these maps requires time andexpertise. To answer these questions, we defined the problem as amissing data imputation task. The method we propose takes into accountthe training of an infinite neural network to estimate exposure toelectromagnetic fields. This is a promising solution for exposure mapreconstruction, which does not require large training sets. The proposedmethod is compared with other machine learning approaches based on UNetnetworks and conditional generative adversarial networks withcompetitive results
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Busatta, Gianluca. "Italian Retrieval-Augmented Generative Question Answering System for Legal Domains." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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A typical scenario involves a user searching an information about something and obtaining a list of documents from an information retrieval system. The retrieved documents may be more or less relevant and it could happen that the information sought is contained in several documents. This would possibly leave the task of searching the information in different documents to the user. In this thesis, it is has been developed an Italian question answering system for legal domains with a Retrieval-Augmented Generation (RAG) approach that aims to directly satisfy the information need of the user. The model is composed of a retriever and a generator both of which are based on Transformer and it has been trained firstly in a self-supervised way on the library of Gruppo Maggioli company, and then in a supervised way on a novel Italian question answering dataset build on purpose. Once the user has provided an input, the model automatically retrieves possibly relevant documents from the knowledge base and use them to condition the generation of an appropriate answer.
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Books on the topic "Generative Artificial Intelligence (GenAI)"

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Poot, Matthijs, and Daniel Samaan. Rethinking production processes with AI and avoiding the innovator’s dilemma. ILO, 2024. http://dx.doi.org/10.54394/blss4423.

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This brief explores the challenges traditional companies face in fully harnessing the transformative potential of Artificial Intelligence (AI). Drawing parallels with historical shifts like the assembly line, the brief argues that AI adoption requires a holistic rethinking of work processes rather than incremental improvements. It highlights the multi-modal capabilities of Generative AI (GenAI) and underscores the need for further research to better understand its impact on productivity and business operations.
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Solanki, Shivam R., and Drupad K. Khublani. Generative Artificial Intelligence. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0403-8.

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Kovač, Mitja. Generative Artificial Intelligence. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-65514-2.

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Paliszkiewicz, Joanna, Ireneusz Dąbrowski, and Leila Halawi. Trust in Generative Artificial Intelligence. Routledge, 2025. https://doi.org/10.4324/9781003586937.

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Elkhodr, Mahmoud, and Ergun Gide. Generative Artificial Intelligence Empowered Learning. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003422433.

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Kumar, Rajendra, Shankar Ramamoorthy, Vishal Jain, Utku Köse, and Ong Eng Tek. Generative Artificial Intelligence in Healthcare. CRC Press, 2025. https://doi.org/10.1201/9781003488255.

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Fan, Yizhou. Learning with Generative Artificial Intelligence. Routledge, 2025. https://doi.org/10.4324/9781003632146.

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Marino, Domenico, and Melchiorre Alberto Monaca, eds. Generative Artificial Intelligence and Fifth Industrial Revolution. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-73880-7.

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Vajjhala, Narasimha Rao, Sanjiban Sekhar Roy, Burak Taşcı, and Muhammad Enamul Hoque Chowdhury, eds. Generative Artificial Intelligence (AI) Approaches for Industrial Applications. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-76710-4.

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Tu, Yiliu Paul, and Maomao Chi, eds. E-Business. Generative Artificial Intelligence and Management Transformation. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-94187-0.

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Book chapters on the topic "Generative Artificial Intelligence (GenAI)"

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Liu, Xi, Newman Lau, and Tulio Maximo. "GenAI in Special Education." In Generative Artificial Intelligence Empowered Learning. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003422433-10.

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Divya, G. S. "Impact of GenAI on Student Outcomes." In Generative Artificial Intelligence Empowered Learning. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003422433-7.

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Zhang, Yuyue, Huaiya Liu, Xinyu Li, and Yizhou Fan. "Learners' Emotion and Motivation while Learning with GenAI." In Learning with Generative Artificial Intelligence. Routledge, 2025. https://doi.org/10.4324/9781003632146-4.

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Nair, Harshith B. "Building Teacher Capacity for Effective Integration of GenAI into Classroom." In Generative Artificial Intelligence Empowered Learning. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003422433-9.

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Fandrejewska, Alicja. "Trust and transparency as facilitators of GenAI-driven transformation of businesses." In Trust in Generative Artificial Intelligence. Routledge, 2025. https://doi.org/10.4324/9781003586937-18.

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Tang, Luzhen, Kejie Shen, and Yizhou Fan. "Enhance Assessing Students' Learning with GenAI: Challenges, Opportunities and Future Directions." In Learning with Generative Artificial Intelligence. Routledge, 2025. https://doi.org/10.4324/9781003632146-7.

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Yu, Zixian, Mengtong Xiang, and Yizhou Fan. "Enhancing Learning Through Interaction with GenAI: Opportunities, Challenges and Future Directions." In Learning with Generative Artificial Intelligence. Routledge, 2025. https://doi.org/10.4324/9781003632146-3.

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Shen, Yuan, Zhanghan Wang, and Yizhou Fan. "Ethical Issues and Value Tensions in the Context of GenAI-Assisted Learning." In Learning with Generative Artificial Intelligence. Routledge, 2025. https://doi.org/10.4324/9781003632146-8.

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Wang, Zhanghan, Yuan Shen, and Yizhou Fan. "The Impacts of GenAI on Learning: What Works and What Falls Short?" In Learning with Generative Artificial Intelligence. Routledge, 2025. https://doi.org/10.4324/9781003632146-5.

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Li, Zijian, and Yizhou Fan. "How GenAI Affects Metacognition in Self-Regulated Learning: Between Enhancement and Inhibition." In Learning with Generative Artificial Intelligence. Routledge, 2025. https://doi.org/10.4324/9781003632146-6.

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Conference papers on the topic "Generative Artificial Intelligence (GenAI)"

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Bosser, Anne-Gwenn, Pasquale Cascarano, Jérémy Lacoche, Shirin Hajahmadi, Ana Stanescu, and Gábor Sörös. "Preface to the First Workshop on GenAI-XR: Generative Artificial Intelligence meets Extended Reality." In 2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). IEEE, 2025. https://doi.org/10.1109/vrw66409.2025.00033.

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Barrera, Cristina, María Luisa Castelló, Jorge García, Lucía Seguí, and Ana Belén Heredia. "INTRODUCING GENERATIVE ARTIFICIAL INTELLIGENCE (GENAI) IN CHALLENGE-BASED LEARNING TO ENHANCE THE CRITICAL THINKING IN AGRI-FOOD AND BIOTECHNOLOGY STUDENTS." In 19th International Technology, Education and Development Conference. IATED, 2025. https://doi.org/10.21125/inted.2025.1157.

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Alimin, Achmad Anggawirya, Dominik P. Goldstein, Lukas Schulze Balhorn, and Artur M. Schweidtmann. "Talking like Piping and Instrumentation Diagrams (P&IDs)." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.159477.

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We propose a methodology that allows communication with Piping and Instrumentation Diagrams (P&amp;IDs) using natural language. In particular, we represent P&amp;IDs through the DEXPI data model as labeled property graphs and integrate them with Large Language Models (LLMs). The approach consists of three main parts: 1) P&amp;IDs are cast into a graph representation from the DEXPI format using our pyDEXPI Python package. 2) A tool for generating P&amp;ID knowledge graphs from pyDEXPI. 3) Integration of the P&amp;ID knowledge graph to LLMs using graph-based retrieval augmented generation (graph-RAG). This approach allows users to communicate with P&amp;IDs using natural language. It extends LLM�s ability to retrieve contextual data from P&amp;IDs and mitigate hallucinations. Leveraging the LLM's large corpus, the model is also able to interpret process information in P&amp;IDs, which could help engineers in their daily tasks. In the future, this work will also open up opportunities in the context of other generative Artificial Intelligence (genAI) solutions on P&amp;IDs, and AI-assisted HAZOP studies.
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Jang Bahadur, Sunil Kumar, Gopala Dhar, and Lavi Nigam. "GenAI Security: Outsmarting the Bots with a Proactive Testing Framework." In 2025 IEEE Conference on Artificial Intelligence (CAI). IEEE, 2025. https://doi.org/10.1109/cai64502.2025.00112.

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Komaclo, Emmanuelle, and Tad Gonsalves. "GenAI for the Synthesis of Enteric Methane Data." In 2024 6th International Workshop on Artificial Intelligence and Education (WAIE). IEEE, 2024. https://doi.org/10.1109/waie63876.2024.00071.

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Matak, Michał, and Jarosław Chudziak. "GAIus: Combining Genai with Legal Clauses Retrieval for Knowledge-Based Assistant." In 17th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013191800003890.

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Heimburg, Vincent, Maximilian Schreieck, and Manuel Wiesche. "Generative Artificial Intelligence Platform Ecosystems." In 2024 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC). IEEE, 2024. https://doi.org/10.1109/ice/itmc61926.2024.10794416.

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Pilaniwala, Pankaj. "Artificial Intelligence in Product Management: Enhancing User Experience Personalization through GenAI." In 2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS). IEEE, 2024. https://doi.org/10.1109/icacrs62842.2024.10841633.

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Zhu, Hong, Xiaoli Li, Junhua Ding, Emese Bari, and Yang Liu. "Plenary Panel at IEEE CISOSE 2024; GenAI, Where are You Going?" In 2024 IEEE International Conference on Artificial Intelligence Testing (AITest). IEEE, 2024. http://dx.doi.org/10.1109/aitest62860.2024.00008.

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Basha, Thippaluri Yahid, Barma Kalyani, and Yelisetti Sandeep. "Generative Artificial Intelligence in Legal Drafting." In 2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST). IEEE, 2024. http://dx.doi.org/10.1109/iccigst60741.2024.10717541.

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Reports on the topic "Generative Artificial Intelligence (GenAI)"

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McDonald, Paula, Stephen Hay, Abby Cathcart, and Alicia Feldman. Apostles, Agnostics and Atheists: Engagement with Generative AI by Australian University Staff. Queensland University of Technology, 2024. http://dx.doi.org/10.5204/rep.eprints.252079.

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The rapid evolution of Generative Artificial Intelligence (GenAI) technologies presents significant opportunities and challenges for higher education institutions. This report, prepared by researchers from Queensland University of Technology and Griffith University, investigates the current adoption and impact of GenAI tools among university staff across Australia. The study provides comprehensive baseline data on the prevalence, purposes, and effects of AI usage across staff groupings. These findings may help inform strategic, operational and investment decisions in the future.
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Azzutti, Alessio, Mark Cummins, Iain MacNeil, and Chuks Otioma. Simplifying Compliance: The Role of AI and RegTech. University of Glasgow and University of Strathclyde, 2025. https://doi.org/10.36399/gla.pubs.351604.

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The Financial Regulation Innovation Lab (FRIL) is dedicated to simplifying compliance through emerging technologies, with Artificial Intelligence (AI) representing the latest evolution in regulatory technology (RegTech). Building on previous research and industry engagement—including workshops, blogs, webinars, and a micro-credential course—this White Paper presents key considerations for the conceptualisation, design, and implementation of AI-driven compliance systems. We begin by examining the nature of regulatory rules and the compliance process before exploring the complexities that challenge AI deployment. The discussion then shifts to Generative AI (GenAI) as a cutting-edge innovation, analysing its capabilities and relevance to compliance functions. A focused use case on GenAI in robo-advisory services illustrates AI’s potential in asset management, where conventional AI is already well-established. Finally, we consider the broader organisational implications of AI adoption, emphasising the opportunity to view compliance as an embedded and adaptive function able to evolve and respond to changing stakeholder expectations and regulatory frameworks.
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Roberts, Kamie. Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile. National Institute of Standards and Technology, 2024. http://dx.doi.org/10.6028/nist.ai.600-1.

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Stark, Cameron. Generative Artificial Intelligence Tools for Red Teams. Office of Scientific and Technical Information (OSTI), 2024. https://doi.org/10.2172/2516859.

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Latorre, Lucia, Valentín Muro, Eduardo Rego, Mariana Gutierrez, Ignacio Cerrato, and Jose Daniel Zarate. Tech Report Artificial Intelligence. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0013015.

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This report provides a comprehensive overview of AI, from its fundamentals to its practical applications, covering topics such as its definition, evolution, and implementation. It also delves into various applications, such as machine learning, natural language processing, computer vision, and generative AI, providing specific examples and use cases across sectors like healthcare, logistics, environment, and security.
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Miller, Tess, and Prosper Habada. The Use of Generative Artificial Intelligence in Higher Education. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.c1cov3ww.1.

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Miller, Tess, and Prosper Habada. The Use of Generative Artificial Intelligence in Higher Education. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.wznaufbr.1.

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Miller, Tess, and Prosper Habada. The Use of Generative Artificial Intelligence in Higher Education. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/rhj.j0pita1t.

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Miller, Tess, and Prosper Habada. The Use of Generative Artificial Intelligence in Higher Education. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/rhj.ql8ttmax.

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Miller, Tess, and Prosper Habada. The Use of Generative Artificial Intelligence in Higher Education. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/rhj.chgt2e44.

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