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A tea packaging design method based on artificial intelligence generated content

JOURNAL OF ENGINEERING DESIGN 2025年

作者： Gao, Zhan Li, Zhenyu Jiangnan Univ Sch Design 1800 Lihu Ave Wuxi Jiangsu Peoples R China

This study proposes an AI-based product packaging design method, utilising artificial intelligence generated content (AIGC) to improve packaging design efficiency, with empirical analysis conducted on jasmine tea packaging design. Firstly, the study collects user reviews related to packaging from e-commerce platforms through Python web scraping, aiming to uncover potential user needs. Next, the Biterm Topic Model (BTM) is employed to analyse the collected textual data, identifying key demands in packaging design. The Hierarchical Analysis Process (AHP) is then applied to classify and prioritise these demands, clarifying the core needs. Subsequently, ChatGPT is utilised to transform the key requirements into design language suitable for the Midjourney platform, automatically generating creative packaging design proposals, thereby enhancing design efficiency and ensuring the proposals better meet user needs. To further optimise the designs, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is applied for comprehensive evaluation, selecting the optimal design, and optimisation is performed through computer-aided design techniques. The results indicate that this method effectively integrates big data analysis, generative artificial intelligence, and multi-criteria decision-making, enhancing both the efficiency and quality of packaging design, providing a new technological framework for future packaging design research.

关键词： Packaging design tea packaging artificial intelligence generated content biterm topic model midjourney

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artificial intelligence generated content Neural Network Model for Enterprise Management Decision-Making System 24

Artificial Intelligence Generated Content Neural Network Mod...

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Proceedings of the 2024 4th International Conference on Computational Modeling, Simulation and Data Analysis

作者： Xueling Wei Tao Lin Guangxi Engineering Vocational College Baise Guangxi China

ISBN: (纸本)9798400711831

Research was conducted on artificial intelligence generated content neural network model solutions for the technical issues of enterprise decision-making systems. The enterprise decision-making system utilizes artificial intelligence generated content, neural network technology, and algorithms to establish modules for neural network data preprocessing, anomaly detection, predictive analysis, decision support, adaptive machine learning, and visual decision-making models, integrating the generative and controllable aspects of artificial intelligence into the decision-making system. In simulation experiments, this system improved output accuracy by 21.6% compared to general information systems and provided 89.7 times more effective reference data, indicating that the proposed technical solution is feasible.

关键词： artificial intelligence generated content

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Application of artificial intelligence generated content in Medical Examinations

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ADVANCES IN MEDICAL EDUCATION AND PRACTICE 2025年 16卷 331-339页

作者： Li, Rui Wu, Tong Huazhong Univ Sci & Technol Tongji Hosp Tongji Med Coll Emergency Dept Wuhan Peoples R China Huazhong Univ Sci & Technol Tongji Hosp Tongji Med Coll Natl Clin Res Ctr Obstetr & Gynecol Dis Wuhan Peoples R China Huazhong Univ Sci & Technol Tongji Hosp Tongji Med Coll Key Lab Canc Invas & MetastasisMinist Educ Wuhan Peoples R China Huazhong Univ Sci & Technol Tongji Hosp Tongji Med Coll Dept Obstet & Gynecol 1095 Jiefang Ave Wuhan 430030 Peoples R China

As the rapid development of large language model, artificial intelligence generated content (AIGC) presents novel opportunities for constructing medical examination questions. However, it is unclear about the way of effectively utilizing AIGC for designing medical questions. AIGC is characterized by its rapid response capabilities and high efficiency, as well as good performance in mimicking clinical realities. In this study, we revealed the limitations inherent in paper-based examinations, and provided a streamlined instruction for generating questions using AIGC, with a particular focus on multiple-choice questions, case study questions, and video questions. Manual review remains necessary to ensure the accuracy and quality of the generated content. Future development will be benefited from technologies like retrieval augmented generation, multi-agent system, and video generation technology. As AIGC continues to evolve, it is anticipated to bring transformative changes to medical examinations, enhancing the quality of examination preparation, and contributing to the effective cultivation of medical students.

关键词： artificial intelligence generated content medical education multiple-choice question large language model

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Binary Choice Probit Model for Enterprise Management Decision-making System Based on artificial intelligence generated content 24

Binary Choice Probit Model for Enterprise Management Decisio...

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4th Asia-Pacific artificial intelligence and Big Data Forum, AIBDF 2024

作者： Wei, Xueling Lin, Tao Guangxi Engineering Vocational College Guangxi Baise China

ISBN: (纸本)9798400710865

From the perspective of high-quality and efficient intelligent problem solving in business decision-making, a artificial intelligence generated content solution is proposed. In order to solve this problem, a artificial intelligence generated content binary choice Probit model is used. The solution proposes feature engineering, model assumptions, variable selection, feedback iteration, and security and compliance technical methods to effectively solve the business decision-making process. A business decision data simulation experiment was conducted by using this model. Compared with the general decision-making system without artificial intelligence generated content, the reference data volume of this business decision-making system increased by 86.63 times, and the decision efficiency increased by 51.21%. The experimental values are key indicators such as return on equity and market value. The results show that this technical method has high accuracy and practical value in evaluating the stability of corporate business decisions and predicting debt repayment capability. © 2024 Copyright held by the owner/author(s).

关键词： artificial intelligence generated content

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Catalyst for future education: An empirical study on the Impact of artificial intelligence generated content on college students' innovation ability and autonomous learning

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EDUCATION AND INFORMATION TECHNOLOGIES 2024年 1-20页

作者： Wang, Dongxuan Liu, Yu Jing, Xin Liu, Qi Lu, Qingjiao Hebei Agr Univ Coll Sci & Technol Huanghua 061100 Hebei Peoples R China Hebei Agr Univ Dept Basic Courses Huanghua 061100 Hebei Peoples R China

With the rapid advancement of artificial intelligence (AI) technology, particularly in its application within the field of education, artificial intelligence generated content (AIGC) has become a focal point of academic inquiry. This paper aims to explore the application of AIGC technology in education and its impact on university students' critical thinking (CT), learning attitudes (LA), innovation and entrepreneurship abilities (IEA), and autonomous learning capabilities (ALA). Using structural equation modeling (SEM) and mediation models (MM), this study analyzes the pathways of interaction and mediation effects among these variables. The research identifies that CT and LA positively influence the use of AIGC, which in turn fosters the development of IEA and ALA. Additionally, IEA acts as a mediator between the use of AIGC and ALA. The study also discusses potential issues associated with the application of AIGC in education, including academic integrity concerns and the potential decline in students' higher-order cognitive skills. The findings underscore the importance of judiciously integrating AIGC and cultivating students' innovation and autonomous learning capabilities in education. The study recommends that educators guide students in the prudent use of AI technologies, discourage over-reliance, and nurture critical thinking and positive learning attitudes.

关键词： artificial intelligence generated content Innovation and entrepreneurship abilities Autonomous learning ability Structural equation model

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Factors affecting the use of artificial intelligence generated content by subject librarians: A qualitative study

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HELIYON 2024年第8期10卷 e29584页

作者： Yang, Xiaowen Ding, Jingjing Chen, Haibo Ji, Hanzhen Nanjing Med Univ Lib 101 Longmian Ave Nanjing 211166 Jiangsu Prov Peoples R China Nanjing Med Univ Inst Med Educ Res Nanjing 211166 Jiangsu Peoples R China Nantong Univ Hosp Lib Nantong Peoples Hosp 3 Affiliated Nantong Hosp 3 Nantong 226006 Jiangsu Peoples R China

To explore the factors affecting the use of artificial intelligence generated content (AIGC) by subject librarians through understanding their perceptions of AIGC. Interpretive phenomenological analysis (IPA) and technology acceptance model (TAM) were used in semi-structured interviews to explore the external variables of perceived ease of use and perceived usability of AIGC application in subject librarians. The perceptions of subject librarians towards AIGC included performance, risk perceptions, ability enhancement, and affective attitude. Attentions were paid to AIGC's performances in providing customized services, optimizing collection resources and improving cost efficiency. The risk perception involved technical stability, data security, user acceptance and occupational risk, the ability enhancement involved the improvement of personal literacy, innovative ability, and self-confidence through the use of AIGC technology, and the affective attitudes included not only excitement and anticipation for the technical potential of AIGC, but also concerns and skepticism about it, and critical attitudes toward its application in academic settings and the ethical issues it may raise. TAM analysis on the factors affecting the use of AIGC by subject librarians indicates that the external influencing factors of perceived ease of use include personal literacy, innovative ability, self-confidence enhancement and affective attitude;the external influencing factors of perceived usability include precise service, collection resource optimization, cost-effectiveness, technological risk, user acceptance and occupational risk. These factors constitute a theoretical framework for understanding and promoting the acceptance and effective use of AIGC by subject librarians. TAM analysis combined with IPA exploration on the external variables of perceived ease of use and perceived usability of AIGC application can identify the key factors affecting subject librarians' perceptions of AIG

关键词： Subject librarians artificial intelligence generated content Interpretive phenomenological analysis Technology acceptance model Influencing factor

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Privacy-preserving vertical federated broad learning system for artificial intelligence generated image content

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2024年第1期21卷 14-14页

作者： Li, Fengyin Ge, Junrong Wang, Xiaojiao Zhao, Gang Yu, Xilong Li, Xinru Qufu Normal Univ Sch Comp Sci Rizhao Peoples R China Rizhao Adm Examinat & Approval Serv Bur Rizhao Peoples R China

artificial intelligence generated image content generates desired image content according to given instructions. It is widely applied in digital marketing, video game design, and movie production. How to efficiently detect the quality of the generated images is one of the key problems. A broad learning system is a feasible solution, because it requires no complex parameter calculation and depth expansion. However, the broad learning system is built by centralized learning, so it may not take advantage of isolated feature data, which prevents broad learning systems from detecting isolated AL-generated image content. To address the above problem, this paper proposes a privacy-preserving vertical federated broad learning system for artificial intelligence generated image content (PVF-BLS). In PVF-BLS, the vertical federated architecture is introduced into the broad learning system to utilize isolated feature data and interactive data are masked by matrix masks for security. To further improve the security of PVF-BLS, this paper proposes a secure incremental learning algorithm based on matrix masks (ILA-MM) to update PVF-BLS. In ILA-MM, the interaction data are processed by matrix masks, which makes PVF-BLS more secure. Experimental results show that PVF-BLS outperforms the traditional broad learning systems when the data are isolated.

关键词： Broad learning system Vertical federation Incremental learning algorithm Isolated data artificial intelligence generated content

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Unraveling the Impact of Explainability of artificial intelligence-generated content(AIGC) on Design Style Transfer Effects 23

Unraveling the Impact of Explainability of Artificial Intell...

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Proceedings of the 2023 9th International Conference on Communication and Information Processing

作者： Zhilin Ren Xiangang Qin Bixuan Wang School of Digital Media and Design Arts Beijing University of Posts and Telecommunications Beijing China China School of Digital Media and Design Arts Beijing University of Posts and Telecommunications Beijing China China and Beijing Key Laboratory of Network System and Network Culture Beijing University of Posts and Telecommunications Beijing China China

ISBN: (纸本)9798400708909

The advent of artificial intelligence-generated content(AIGC) has revolutionized the traditional creative activities in the design field, the workflow of which includes idea generation, sketching, solution determination, low-fidelity prototype interaction, detail tuning, high-fidelity prototyping, front-end slicing, and deployment. By contrast, AIGC streamlines the process of design into idea generation, design model prompting, image dataset collection, AIGC tool use, model fine-tuning, and tool optimization. The designer's focus, the subject, and the object of attention have been changed accordingly. Traditional design activities without AIGC focus on realizing individual and team creativity; the ones with AIGC introduce AI models, algorithms, and AI-HMIs as collaborators in the creative process. The quality of the created content is no longer determined by the designer alone due to the involvement of artificial intelligence(AI) of in the prediction and decision-making of the created content. There is also a growing need for designers to have a self-explanatory and responsible intelligent system. This study investigates the role of explainability in four AIGC tools and its impact on creative design. It aims to assess how explainability influences user satisfaction and computer vision metrics in the context of style transfer for iOS mobile interface design. The results demonstrate the adaptations and limitations of these AIGC tools. As observed in CycleGAN, a high level of explainability involves various factors, including superior model performance, manageable datasets, extensive fine-tuning of model parameters, user-centered human-computer interaction, explainability results presentation, and input methods. This high degree of explainability leads to the highest levels of user satisfaction and computer vision evaluation. However, it has drawbacks, as it has limited control over design *** tools with a medium level of explainability, such as Stable Diffusi

关键词： artificial intelligence generated content Computer Vision Assessment Explainable artificial intelligence Human-Computer Interaction Style Transfer User Satisfaction

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A Study on Factors Influencing Designers' Behavioral Intention in Using AI-generated content for Assisted Design: Perceived Anxiety, Perceived Risk, and UTAUT

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INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION 2025年第2期41卷 1064-1077页

作者： Li, Weiyi Loughborough Univ Inst Design Innovat London England

This study aims to comprehensively understand the intention to use artificial intelligence generated Assistance in Design Tools (AIGC) among design students and practitioners, along with its influencing factors. Utilizing Smart-PLS software and Partial Least Squares Structural Equation Modeling (PLS-SEM) technique, we constructed a comprehensive research model. Based on 404 valid questionnaire responses, we systematically analyzed the underlying mechanisms of designers' attitudes towards AIGC tools. The sample encompasses diverse schools and levels of professional experience, ensuring the wide applicability of research outcomes. In the data analysis process, professional statistical analysis methods, including path analysis and standardized path coefficients, were employed to ensure a profound exploration of research questions. The results indicate that performance expectancy, effort expectancy, social influence, and facilitating conditions significantly positively influence the willingness to use AIGC tools, while perceived anxiety and perceived risk exert negative impacts. This study, by integrating traditional and novel factors, provides crucial theoretical and practical guidance for the actual application of AIGC technology in the design field, offering profound insights for the future development and education of design technology.

关键词： artificial intelligence generated content artificial intelligence design industry behavioral intention UTAUT

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Exploring Collaborative Diffusion Model Inferring for AIGC-Enabled Edge Services

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2025年第2期11卷 946-960页

作者： Feng, Weijia Zhang, Ruojia Zhu, Yichen Wang, Chenyang Sun, Chuan Zhu, Xiaoqiang Li, Xiang Taleb, Tarik Tianjin Normal Univ Sch Comp & Informat Engn Tianjin 300387 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Guangdong Lab Artificial Intelligence & Digital Ec Shenzhen 518000 Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore 639798 Singapore Beijing Jiaotong Univ Sch Software Engn Beijing 100044 Peoples R China Beijing Jiaotong Univ Beijing Key Lab Secur & Privacy Intelligent Transp Beijing 100044 Peoples R China Ruhr Univ Bochum Fac Elect Engn & Informat Technol D-44780 Bochum Germany

With the advancements in AI-generated content (AIGC) technologies and edge cloud networks, the demand for AIGC services is increasing. The diffusion model stands out for its ability to generate images from text. However, its high computational requirements challenge user devices, leading to ongoing efforts to improve inferring speed while preserving image quality. In this study, we propose a novel edge-user collaborative inferring framework. By capitalizing on collaboration among the devices in edge and user, the proposed framework optimizes the service delay, network resource consumption, and user device computing resource consumption of the user's AIGC service, and improves the user's QoE. To demonstrate the efficiency of our proposed framework, we conduct comparative experiments and ablation experiments on a variety of datasets. Experimental results show that the proposed framework achieves better-generated image quality and reduces a large number of computing resources and network consumption, improving user QoE.

关键词： Image edge detection Collaboration Diffusion models Noise Servers Computational modeling Training artificial intelligence Load modeling Noise reduction Edge computing task offloading knowledge distillation artificial intelligence generated content

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