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A study on students' behavioural intention and use behaviour of artificial intelligence-generated content in physical education: Employing an extended the unified theory of acceptance and use of technology model

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JOURNAL OF HOSPITALITY LEISURE SPORT & TOURISM EDUCATION 2025年 36卷

作者： Wu, Qianjin Li, Shanshan Xin, Shuang Hou, Qian Li, Ping Shandong Univ Sch Phys Educ Jinan 250061 Shandong Peoples R China Sichuan Univ Sch Phys Educ Chengdu 610065 Sichuan Peoples R China Zhejiang Gongshang Univ Sch Tourism & Urban Rural Planning Hangzhou 310018 Zhejiang Peoples R China Hunan Normal Univ Coll Phys Educ Changsha 410012 Hunan Peoples R China

This study examines factors affecting AIGC acceptance among physical education students using the UTAUT model and perceived risk. Analyzing 414 Chinese students via PLS-SEM reveals that performance expectancy, social influence, facilitating conditions, and perceived risk significantly influence behavioural intention, whereas effort expectancy does not. Both facilitating conditions and behavioural intention positively influence use behaviour, with the impact of facilitating conditions being partially mediated by behavioural intention. Gender does not moderate these relationships. Findings suggest promoting AIGC in sports teaching by enhancing performance expectancy, leveraging social influence, optimizing facilitating conditions, and improving risk education.

关键词： artificial intelligence-generated content Physical education Unified theory of acceptance and use of technology Partial least squares structural equation modelling

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IMFL-AIGC: Incentive Mechanism Design for Federated Learning Empowered by artificial intelligence generated content

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第12期23卷 12603-12620页

作者： Huang, Guangjing Wu, Qiong Li, Jingyi Chen, Xu Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Guangdong Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

Federated learning (FL) has emerged as a promising paradigm that enables clients to collaboratively train a shared global model without uploading their local data. To alleviate the heterogeneous data quality among clients, artificial intelligence-generated content (AIGC) can be leveraged as a novel data synthesis technique for FL model performance enhancement. Due to various costs incurred by AIGC-empowered FL (e.g., costs of local model computation and data synthesis), however, clients are usually reluctant to participate in FL without adequate economic incentives, which leads to an unexplored critical issue for enabling AIGC-empowered FL. To fill this gap, we first devise a data quality assessment method for data samples generated by AIGC and rigorously analyze the convergence performance of FL model trained using a blend of authentic and AI-generated data samples. We then propose a data quality-aware incentive mechanism to encourage clients' participation. In light of information asymmetry incurred by clients' private multi-dimensional attributes, we investigate clients' behavior patterns and derive the server's optimal incentive strategies to minimize server's cost in terms of both model accuracy loss and incentive payments for both complete and incomplete information scenarios. Numerical results demonstrate that our proposed mechanism exhibits highest training accuracy and reduces up to 53.34% of the server's cost with real-world datasets, compared with existing benchmark mechanisms.

关键词： Training Data models Costs Data integrity Servers Distributed databases Accuracy Federated learning incentive mechanism crowdsourcing artificial intelligence-generated content

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Research on the Innovation in the Model of Cultivating Sci-Tech Translation Talents in Universities of Science and Engineering from the Perspective of artificial intelligence-generated content

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Journal of Contemporary Educational Research 2024年第11期8卷 137-144页

作者： Gelan Zhang Wenjing Liao School of Foreign Languages Hunan University of TechnologyZhuzhou 412007Hunan ProvinceChina

New technologies represented by ChatGPT have transformed traditional translation work modes and brought unprecedented changes to translation *** on the development of artificial intelligence-generated content(AIGC)technology,this article analyzes the challenges it brings to sci-tech translation research in the new era,reshapes the value goals of the“ideological and political guidance+technological empowerment”model of cultivating sci-tech translation talents,proposes tentative approaches about the innovation in the model of sci-tech translation talents in universities of science and engineering,and strengthens the talents’translation competence,language proficiency,and interdisciplinary ability,aiming to provide new perspectives and thoughts for the development of translation research in the AIGC era.

关键词： artificial intelligence-generated content Sci-tech translation Talent cultivation Interdisciplinary

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Controllable image synthesis methods, applications and challenges: a comprehensive survey

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artificial intelligence REVIEW 2024年第12期57卷 336页

作者： Huang, Shanshan Li, Qingsong Liao, Jun Wang, Shu Liu, Li Li, Lian Chongqing Univ Sch Big Data & Software Engn Chongqing 400000 Peoples R China Hefei Univ Technol Sch Comp Sci Informat Engn Hefei 230601 Peoples R China Southwest Univ Sch Mat & Energy Chongqing 400715 Peoples R China

Controllable Image Synthesis (CIS) is a methodology that allows users to generate desired images or manipulate specific attributes of images by providing precise input conditions or modifying latent representations. In recent years, CIS has attracted considerable attention in the field of image processing, with significant advances in consistency, controllability and harmony. However, several challenges still remain, particularly regarding the fine-grained controllability and interpretability of synthesized images. In this paper, we comprehensively and systematically review the CIS from problem definition, taxonomy and evaluation systems to existing challenges and future research directions. First, the definition of CIS is given, and several representative deep generative models are introduced in detail. Second, the existing CIS methods are divided into three categories according to the different control manners used and discuss the typical work in each category critically. Furthermore, we introduce the public datasets and evaluation metrics commonly used in image synthesis and analyze the representative CIS methods. Finally, we present several open issues and discuss the future research direction of CIS.

关键词： Controllable image synthesis Deep generative model Causal learning GAN inversion Interpretable representation learning artificial intelligence-generated content

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ChatGPT as a life coach for professional identity formation in medical education: A self-regulated learning perspective

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EDUCATIONAL TECHNOLOGY & SOCIETY 2024年第3期27卷 374-389页

作者： Huang, Hsin Lin, Hui-Chen Natl Taipei Univ Nursing & Hlth Sci Sch Nursing Taipei Taiwan Taipei Med Univ Sch Nursing Coll Nursing Taipei Taiwan Taipei Med Univ Wan Fang Hosp Res Ctr Nursing Clin Practice Taipei Taiwan

Professional Identity Formation (PIF) is considered a crucial process in medical education. It involves how medical students identify their role as physicians, discover their professional positioning, and gradually develop their professional identity through social interactions. This qualitative descriptive study adopted the phenomenological method;it proposed the DSCOR (Diverse thinking, Seeking advice, Construction, Organizing and sharing, and Reflection) model based on the ChatGPT as a life coach (ChatGPT-LC) selfregulated learning (SRL) approach to design a PIF course for six medical students. Data collection included digital storytelling created using AI-generated techniques, learning sheets, direct observations, reflective feedback forms, and semi-structured interviews. The data transcription and analysis were conducted using Colaizzi's method. The results revealed three benefits of Generative AI (i.e., ChatGPT), namely "increasing motivation for planning PIF," "strengthening the mastery of PIF," and "broadening the perspectives of PIF." Moreover, the ChatGPT-LC SRL approach had a positive influence on students, helping them understand the significance of PIF in their personal development at the early stage. The artificial intelligence-generated content provided positive guidance and supportive learning, offering specific suggestions and assistance. This brought about benefits for learning, and provided initial evidence for the application of ChatGPT-LC in medical education.

关键词： Professional identity formation artificial intelligence-generated content Medical students Self-regulated learning

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CLIP-MEDFAKE: SYNTHETIC DATA AUGMENTATION WITH AI-generated content FOR IMPROVED MEDICAL IMAGE CLASSIFICATION 31

CLIP-MEDFAKE: SYNTHETIC DATA AUGMENTATION WITH AI-GENERATED ...

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2024 International Conference on Image Processing

作者： Chen, Honghui Zhao, Baoquan Yue, Guanghui Liu, Weide Lv, Chenlei Wang, Ruomei Zhou, Fan Sun Yat Sen Univ Sch Comp Sci & Engn Beijing Peoples R China Sun Yat Sen Univ Sch Artificial Intelligence Beijing Peoples R China Shenzhen Univ Sch Biomed Engn Shenzhen Peoples R China Harvard Univ Harvard Med Sch Cambridge MA USA Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Data augmentation is serving as a critical and fundamental technology to improve model generalization and performance in a wide spectrum of machine learning tasks. Despite the increasing interest in developing various pathways to artificially generate new data to reduce the overfitting issue during model training, enriching the diversity of training data in the field of medicine remains facing enormous challenges. By virtue of recent advancements in generative artificial intelligence, we present a novel data augmentation framework, CLIP-MedFake, to address the shortage of training data used in medical image classification. The proposed method first employs the Stable Diffusion model to generate new fake data based on a small amount of training data, and then adopts the paradigm of few-shot learning and uses the CLIP architecture as the backbone to pre-train the model with synthetic data and then fine-tune it with real medical images. Extensive experiment results on two publicly available datasets demonstrate the effectiveness of the proposed method in promoting medical image classification.

关键词： Medical image classification data augmentation artificial intelligence-generated content

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Joint Communication and Computation Scheduling for MEC-enabled AIGC Services Based on Generative Diffusion Model 22

Joint Communication and Computation Scheduling for MEC-enabl...

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22nd International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks

作者： Liu, Huaizhe Wu, Jiaqi Zhuang, Xinyi Wu, Hongjia Gao, Lin Harbin Inst Technol Sch Elect & Informat Engn Shenzhen Peoples R China Harbin Inst Technol Guangdong Prov Key Lab Aerosp Commun & Networking Shenzhen Peoples R China

ISBN: (纸本)9798331508722;9783903176652

artificial intelligence-generated content (AIGC) based on Generative Diffusion Model (GDM) has emerged as a promising paradigm of content generation, revolutionizing the creation of diverse contents and driving significant technological advancements. Due to the low latency requirements of AIGC services, mobile edge computing (MEC) has become a crucial enabling technology for these services. In this work, we consider an MEC-enabled GDM-based AIGC network, which consists of multiple GDMs with varying sizes and capabilities deployed on edge computing servers (ES), and multiple mobile users (UEs) with diverse latency and accuracy requirements requesting AIGC services from ES through wireless access points (APs). In such a scenario, we are interested in the joint communication and computation scheduling problem for UEs, which involves selecting the appropriate APs (along with the communication bandwidth allocation) and the appropriate ES (together with the computation resource allocation and model inference optimization) for UEs, considering both the UEs' heterogeneous requirements and the GDMs' heterogeneous capabilities. To address the problem in a practical scenario with decentralized, autonomous, and self-interested UEs, we formulate a non-cooperative game, called the Joint User Association and Computation Offloading (JUACO) game, where each UE acts as a game player, selecting the best AP (for communication) as well as the best ES and the best GDM model inference step (for computation), aiming to minimize the inference time while meeting the specified inference accuracy requirement. We prove that the proposed JUACO game is a potential game, thus guaranteeing the existence of Nash equilibrium (NE) and the convergence of simple best response-based distributed algorithms to NE. Simulation results demonstrate that the proposed JUACO game approach significantly reduces the inference time and meets the required accuracy compared to traditional methods, validating the effec

关键词： artificial intelligence-generated content Generative Diffusion Model Mobile Edge Computing

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Generative AI-enabled Sensing and Communication Integration for Urban Air Mobility 99

Generative AI-enabled Sensing and Communication Integration ...

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IEEE 99th Vehicular Technology Conference (VTC-Spring)

作者： Sha, Zifan Yue, Wenwei Wang, Shuo Cheng, Nan Wu, Jiaming Li, Changle Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China Chalmers Univ Technol Dept Architecture & Civil Engn Gothenburg Sweden

ISBN: (纸本)9798350387414

The deepening process of urbanization poses formidable challenges to the current transportation carrying capacity. The utilization of near-ground space (NGS) and urban air mobility (UAM) greatly enhance spatial dimensions and traffic flexibility of the transportation system. However, the current limited sensing capability falls short in meeting the real-time collaborative environmental sensing and intelligent control requirements of aerial transportation. Integrated sensing and communication (ISAC) combines the sensing system of UAM with 6G communication technologies, enabling them to collaborate and achieve data sensing, transmission, processing, and decision control. The use of artificial intelligence-generated content (AIGC) facilitates real-time data fusion and decision-making, adapting to dynamic and unpredictable environments. In this paper, we first model and analyze the traffic flow in three-dimensional space, achieving knowledge embedding based on artificial potential energy field theory. Next, we design a multimodal data fusion neural network structure, which utilizes the Variational Autoencoder (VAE) to generatively achieve feature fusion and compression. Finally, we construct a UAM digital simulation platform using AirSim, which generates considerable aerial data. The simulation results demonstrate that our proposed approach achieves a feature recognition accuracy of 90.38%. The total latency is below 0.6ms, which exhibits high real-time performance.

关键词： Urban air mobility 6G Integrated sensing and communication artificial intelligence-generated content

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Research on the Possibility of Application of Generative artificial intelligence in Open Source Threat intelligence Grid Field

Research on the Possibility of Application of Generative Art...

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International Conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Sun, Yue Hou, Cong Su, Yuyue Hu, Xinrui Lei, Huibing Ning, Haoxuan Xia, Yu State Grid Jibei Electric Power Co. Ltd. Research Institute Beijing China School of Electrical and Control Engineering Shaanxi University of Science and Technology Shaanxi Xi’an China

ISBN: (纸本)9789819618675

The potential of generative artificial intelligence (GenAI) in open source threat intelligence, especially in grid security, was explored in this paper. Large language model (LLM) as an innovative tool coupled with its powerful natural language processing capabilities, is expected to play a key role in the production of open source threat intelligence, improve intelligence quality and operational efficiency, and bring smarter and more efficient solutions for cybersecurity defense. In this study, a practical model example of using LLM for open source threat intelligence collection was described in detail, demonstrating that the potential of introducing LLM can significantly enhance the security and responsiveness of the current power monitoring system in the absence of a unified threat intelligence system. Despite the powerful information processing capabilities of LLMS, the realization of highly accurate, highly specialized, and unified structured models requires long-term, in-depth professional training. With the maturity of LLM model in open source threat intelligence field, power system will usher in the era of truly automated threat intelligence production. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： artificial intelligence-generated content Large language models open source intelligence power systems

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Integration of AI generated content (AIGC) in the Development of Digital Human in the Metaverse

Integration of AI Generated Content (AIGC) in the Developmen...

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2024 International Conference on Language Technology and Digital Humanities, LTDH 2024

作者： Wang, Ning Zhang, Hongyi Zhejiang Ren Travel technology development Co. LTD City Zhejiang310000 China Ren travel vision Technology development Co. LTD Zhejiang310000 China

ISBN: (纸本)9798350380187

This paper discusses the AIGC in digital content creation, interaction experience enhanced, application scenario development path of integration. Driven by AIGC personalized appearance, action expression, speech dialogue content generation, as well as natural language interaction, emotion recognition, intelligent, and so on multimodal interaction can be all-round development and application to digital. The deep integration of AIGC and digital human development is expected to further improve the content productivity and interactive experience of the metaverse, and promote the development of the metaverse industry. ©2024 IEEE.

关键词： artificial intelligence-generated content content generation digital man interactive experience metaverse

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