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What Drives the Use of Generative Artificial Intelligence to Promote Educational Sustainability? Evidence from SEM-ANN Approach

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TechTrends 2025年 1-15页

作者： Arpaci, Ibrahim Al-Emran, Mostafa Al-Qaysi, Noor Al-Sharafi, Mohammed A. Management Information Systems College of Business Administration Gulf University for Science and Technology Mishref Kuwait Department of Software Engineering Faculty of Engineering and Natural Sciences Bandirma Onyedi Eylul University Balikesir Turkey Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea Faculty of Engineering & IT The British University in Dubai Dubai United Arab Emirates Engineering Technical College Al-Bayan University Baghdad Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq IRC for Finance and Digital Economy KFUPM Business School King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

The literature on generative “Artificial Intelligence” (AI) in education primarily focuses on its immediate benefits and applications, such as personalized learning, student engagement, and content generation. However, there is a notable absence of empirical research concerning the holistic use of generative AI within educational institutions and its long-term impact on educational sustainability. This study investigates the factors that predict the use of generative AI and its subsequent influence on educational sustainability. This study enhanced the extended “Unified Theory of Acceptance and Use of Technology” (UTAUT-2) by including personal innovativeness as an external variable and educational sustainability as an outcome. A hybrid methodology, integrating “Structural Equation Modeling” (SEM) and “Artificial Neural Network” (ANN), was used to evaluate the research model using data collected from 1,011 university students. The SEM analysis confirmed all hypotheses. Specifically, a positive relationship was found between use behavior and educational sustainability. These results show that the combined variables collectively account for 63% and 66% of the variance in use behavior and educational sustainability, respectively. The sensitivity analysis identified habit as the most important predictor of use behavior. The findings offer both theoretically important and practically valuable implications for designing user-centered AI integration in education. © Association for Educational Communications & Technology 2025.

关键词： Artificial intelligence Educational sustainability SEM-ANN UTAUT-2

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Development and Evaluation of an Interactive Web Interface for BPMN Elements Identification from Natural Language Text

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Procedia computer Science 2025年 256卷 431-438页

作者： Vitor Camargo de Moura Guilherme Rego Rockembach Marcelo Soares Pimenta Isabela Gasparini Encarna Sosa-Sánchez Lucineia Heloisa Thom Institute of Informatics – Federal University of Rio Grande do Sul (UFRGS) Porto Alegre 91501-970 Brazil Departament of Computer Science University of Santa Catarina State Joinville 89219-710 Brazil Department of Computer Systems and Telematics Engineering – University of Extremadura Cáceres 10003 Spain

Process discovery represents a high percentage of the time demanded along the business process management lifecycle. Texts in natural language, or process descriptions, can be explored as a critical information source. Extracting information automatically from these documents is challenging due to their ambiguity and complexity. While various (semi-)automated solutions are proposed in the literature, functional code access is rare, and tool deployment is almost nonexistent. This research introduces an accessible web interface that allows users to upload process descriptions and interact with the text, automatically identifying the main process elements from the Business Process Model and Notation. Interactions include highlighting elements associated with specific participants. An evaluation experiment with ten potential real-world users was conducted to assess the tool’s benefits. The experiment compared performance in answering questions related to process descriptions across three scenarios: using plain text, using the proposed tool, and using ChatGPT. Results indicated an advantage in using the tool, particularly regarding time consumption. Data collected on ChatGPT usage also provided insights into how users leverage this technology for problem-solving.

关键词： BPMN process description natural language Web interface development evaluation

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Blinding the Wiretapper: RIS-Enabled User Occultation in the ISAC Era

arXiv

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arXiv 2025年

作者： Rexhepi, Getuar Rou, Hyeon Seok de Abreu, Giuseppe Thadeu Freitas Alexandropoulos, George C. School of Computer Science and Engineering Constructor University Bremen Campus Ring 1 Bremen28759 Germany Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens16122 Greece Department of Electrical and Computer Engineering University of Illinois ChicagoIL60601 United States

An undesirable consequence of the foreseeable proliferation of sophisticated integrated sensing and communications (ISAC) technologies is the enabling of spoofing, by malicious agents, of situational information (such as proximity, direction or location) of legitimate users of wireless systems. In order to mitigate this threat, we present a novel ISAC scheme that, aided by a reconfigurable intelligent surface (RIS), enables the occultation of the positions of user equipment (UE) from wiretappers, while maintaining both sensing and desired communication performance between the UEs and a legitimate base station (BS). To that end, we first formulate an RIS phase-shift optimization problem that jointly maximizes the sum-rate performance of the UEs (communication objective), while minimizing the projection of the wiretapper’s effective channel onto the legitimate channel (hiding objective), thereby disrupting the attempts by a wiretapper of localizing the UEs. Then, in order to efficiently solve the resulting non-convex joint optimization problem, a novel manifold optimization algorithm is derived, whose effectiveness is validated by numerical results, which demonstrate that the proposed approach preserves legitimate ISAC performance while significantly degrading the wiretapper’s sensing capability. Copyright © 2025, The Authors. All rights reserved.

关键词： Sensitive data

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Exploring Children’s Strategies in Response to Robot’s Advice During a Group Task with iCub and Nao 16th

Exploring Children’s Strategies in Response to Robot’s A...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Pusceddu, Giulia Sangineto, Mariapia Cocchella, Francesca Bogliolo, Michela Belgiovine, Giulia Lastrico, Linda Casadio, Maura Rea, Francesco Gena, Cristina Sciutti, Alessandra Robotics Brain and Cognitive Science Department Italian Institute of Technology Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Department of Computer Science University of Turin Turin Italy Cognitive Architecture for Collaborative Technologies Unit Italian Institute of Technology Genoa Italy Scuola di Robotica Genoa Italy

ISBN: (纸本)9789819635184

This study investigates children’s trust in two humanoid robots, Nao and iCub, through a cooperative game designed to elicit spontaneous behaviors and group dynamics. We investigate whether participants change their choices after hearing advice from a robot, considering rounds where the advice is correct or incorrect, with the robot apologizing for its mistakes. Results reveal that, in most cases, participants prefer not to change their moves after the robot’s advice. Additionally, after the robots made mistakes, participants are less likely to actively follow their advice and more likely to reject it. No significant differences in strategies among the iCub and Nao samples are evidenced. Furthermore, we examine the influence of group dynamics, identifying "Proactive Players" who consistently initiated moves. A significant association between the strategies of this player and their teammates is found, suggesting potential influence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Adaptive iterated local search algorithm for dynamic patient admission scheduling problems

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Soft Computing 2025年 1-11页

作者： Turky, Ayad Sabar, Nasser R. Song, Andy Hussain, Abir Liatsis, Panos Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia School of Computing Technologies RMIT University Melbourne Australia Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

Healthcare resource management is essential for ensuring the quality of patient care. However, it can be a complex and costly task. This work addresses the patient admission scheduling (PAS) problem, a complex aspect of healthcare resource management. PAS aims to allocate patients to hospital beds within a planning horizon, subject to a variety of healthcare constraints. The goal is to maximize management efficiency and patient comfort to improve medical treatment. In this work, we consider a practical variant of PAS known as dynamic PAS (DPAS). DPAS considers several factors and constraints, such as the daily registration of new patients, urgent patients, uncertainties in stay lengths, operating theatre resources, and the surgery scheduling process. An effective and efficient adaptive iterated local search (AILS) algorithm is proposed to solve DPAS. To enable the search to explore the search space efficiently, the proposed AILS adaptively integrates a number of components. Two adaptive perturbation strategies are devised to locate unexplored areas in the search space. To exploit the newly discovered areas effectively, we propose an adaptive local search mechanism as an intensification strategy to find a high-quality solution. The proposed AILS algorithm is compared to benchmark problems used by existing algorithms. The experimental results demonstrate the effectiveness and efficiency of the proposed approach. Specifically, out of 30 tested instances, AILS obtains 17 of the best-known results using less computational time. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Resource allocation

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Disruptive attacks on artificial neural networks: A systematic review of attack techniques, detection methods, and protection strategies

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Intelligent Systems with Applications 2025年 26卷

作者： Alobaid, Ahmad Bonny, Talal Alrahhal, Maher Research Institute of Sciences and Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

This paper provides a systematic review of disruptive attacks on artificial neural networks (ANNs). As neural networks become increasingly integral to critical applications, their vulnerability to various forms of attack poses significant security challenges. This review categorizes and analyzes recent advancements in attack techniques, detection methods, and protection strategies for ANNs. It explores various attacks, including adversarial attacks, data poisoning, fault injections, membership inference, model inversion, timing, and watermarking attacks, examining their methodologies, limitations, impacts, and potential improvements. Key findings reveal that while detection and protection mechanisms such as adversarial training, noise injection, and hardware-based defenses have advanced significantly, many existing solutions remain vulnerable to adaptive attack strategies and scalability challenges. Additionally, fault injection attacks at the hardware level pose an emerging threat with limited countermeasures. The review identifies critical gaps in defense strategies, particularly in balancing robustness, computational efficiency, and real-world applicability. Future research should focus on scalable defense solutions to ensure effective deployment across diverse ANN architectures and critical applications, such as autonomous systems. Furthermore, integrating emerging technologies, including generative AI models and hybrid architectures, should be prioritized to better understand and mitigate their vulnerabilities. © 2025 The Author(s)

关键词： Deep neural networks

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A Survey on Anomaly Detection with Few-Shot Learning 8th

A Survey on Anomaly Detection with Few-Shot Learning

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8th International Conference on Cognitive Computing, ICCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Chen, Junyang Wang, Changbo Hong, Yifan Mi, Rui Zhang, Liang-Jie Wu, Yirui Wang, Huan Zhou, Yue College of Computer Science and Software Engineering Shenzhen University Shenzhen China Center of Health Administration and Development Studies Hubei University of Medicine Shiyan China College of Informatics Huazhong Agricultural University Wuhan China Hohai University Nanjing China Department of Obstetrics and Gynecology Tianyou Hospital Affiliated to Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9783031779534

The primary objective of anomaly detection is to identify abnormal or unusual patterns within a dataset, where the number of normal samples typically exceeds that of abnormal samples. Due to the scarcity of labeled abnormal samples, traditional methods face challenges when dealing with anomaly detection. To overcome these limitations, few-shot learning has emerged as a promising solution. By leveraging a limited number of labeled anomaly samples, few-shot learning enables the construction of models that enhance anomaly detection performance and generalization. This paper provides a comprehensive investigation of anomaly detection, covering its definition, fundamental principles, methods, and challenges. Furthermore, it introduces few-shot learning as a solution and explores its principles, applications, and technical categorization, including meta-learning, transfer learning, generative models, prototypical learning, and siamese networks. The paper explores the utilization of few-shot learning in anomaly detection across diverse data types This paper delves into significance across different domains. Additionally, it addresses the challenges faced by few-shot learning in the field of anomaly detection and proposes future directions for development. This comprehensive analysis aims to provide profound insights and guidance for prospective research and application in anomaly detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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Dynamic Client Selection for Over-the-Air Federated Learning Network

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IEEE Internet of Things Journal 2025年

作者： Shi, Fang Lin, Weiwei Peng, Chaoda Zhong, Cankun Wang, Dong Cheng, Mingyue South China Agricultural University College of Mathematics and Informatics Guangzhou510006 China South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Pengcheng Laboratory Shenzhen518000 China

As a privacy-preserving solution, Federated Learning (FL) demonstrates great potential in distributed model training, but limited bandwidth, particularly in NFC-based systems, emerges as a key bottleneck by restricting the number of participating clients. To address this challenge, over-the-air FL leverages the superposition property of wireless multiple-access channels, enabling faster model training and accommodating more clients, even in bandwidth-constrained scenarios like NFC. However, due to its analog-integrated nature, the FL performance is also affected by other factors, such as channel noise. These motivate us to consider how the selected client set and channel noise affect FL performance. To explore this concern, in this paper, we consider an over-the-air FL system with analog gradient aggregation and analyze the impact of the selected client set and channel noise on FL training performance. The theoretical analysis effectively shows the importance of the clients' number and the power scaling factor to the FL training performance. Based on the theoretical analysis, we transform the global optimization problem into the client selection problem and propose a dynamic client selection scheme to optimize the training performance under the aggregation error constraint. Experimental results demonstrate that our proposed scheme can boost FL by speeding up the convergence of the global model (at least 35%) and saving energy consumption. © 2025 IEEE.

关键词： Mean square error

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Towards Responsible AI Music: an Investigation of Trustworthy Features for Creative Systems

arXiv

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arXiv 2025年

作者： de Berardinis, Jacopo Porcaro, Lorenzo Meroño-Peñuela, Albert Cangelosi, Angelo Buckley, Tess Department of Computer Science University of Liverpool United Kingdom Department of Computer Control and Management Engineering Sapienza University of Rome Italy Department of Informatics King’s College London United Kingdom Department of Computer Science University of Manchester United Kingdom Association of AI Ethicists

Generative AI is radically changing the creative arts, by fundamentally transforming the way we create and interact with cultural artefacts. While offering unprecedented opportunities for artistic expression and commercialisation, this technology also raises ethical, societal, and legal concerns. Key among these are the potential displacement of human creativity, copyright infringement stemming from vast training datasets, and the lack of transparency, explainability, and fairness mechanisms. As generative systems become pervasive in this domain, responsible design is crucial. Whilst previous work has tackled isolated aspects of generative systems (e.g., transparency, evaluation, data), we take a comprehensive approach, grounding these efforts within the Ethics Guidelines for Trustworthy Artificial Intelligence produced by the High-Level Expert Group on AI appointed by the European Commission – a framework for designing responsible AI systems across seven macro requirements. Focusing on generative music AI, we illustrate how these requirements can be contextualised for the field, addressing trustworthiness across multiple dimensions and integrating insights from the existing literature. We further propose a roadmap for operationalising these contextualised requirements, emphasising interdisciplinary collaboration and stakeholder engagement. Our work provides a foundation for designing and evaluating responsible music generation systems, calling for collaboration among AI experts, ethicists, legal scholars, and artists. This manuscript is accompanied by a website: https://***/raim-framework/. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Optimizing reinforcement learning for large action spaces via generative models: Battery pattern selection

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Pattern Recognition 2025年 160卷

作者： Hu, Jingwei Li, Xinjie Li, Xiaodong Hou, Zhensong Zhang, Zhihong School of Informatics Xiamen University Xiamen China School of Electrical and electronic engineering North China Electric Power University Beijing China Department of Computer Science The University of Hong Kong Hong Kong China

Intrinsic and environmental factors contribute to variability in the performance of cells within a battery pack, affecting the lifespan and safety of battery systems. To solve this problem, active and passive equalization methods are proposed. However, existing passive equalization methods suffer from energy loss and low efficiency among batteries, while existing active equalization methods necessitate complex expert knowledge and control algorithms. We propose an active equalization model that leverages a generative model (GM) to assist in pattern selection for a reinforcement learning (RL) scheme, tailored for Dynamic Reconfigurable Battery (DRB) systems. The proposed model overcomes the pattern selection challenge in large-scale discrete action spaces by employing a Variational Autoencoder (VAE) for dimensionality reduction and latent space mapping, actively balancing DRB systems. Moreover, the use of pattern subgraphs diminishes dependence on expert knowledge, enabling the model to recognize structural information and adjust the system's stability. The experimental setup adheres to the laws of physics and tests the model's functionality on a simulation system. Results show that the proposed Generative Model-based Reinforcement Learning (GMRL) approach effectively addresses decision-making challenges in large-scale spaces. It can learn the structured features of the battery network, thus balancing the energy storage system and maximizing discharge efficiency gains. © 2024

关键词： Generative adversarial networks

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