Research on multi-modal data-driven AIGC graphic design generation model

Research on multi-modal data-driven AIGC graphic design generation model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research on multi-modal data-driven AIGC graphic design generation model Yanzhe Yang, Lulu Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7431399/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The integration of AI into graphic design has revolutionized the creative industry, allowing content to be generated quickly and with extreme variation. These Artificial Intelligence Generated Content (AIGC) systems are largely incapable of enabling personalization, contextual relevance, and cultural sensitivity, due to their heavy reliance on general datasets, and inputs that do not change with user interaction. To tackle these issues, the present work suggests a multi-modal data-driven AIGC graphic design framework in which both text and visuals are integrated with real-time user feedback. The focus is on creating an adaptable model that produces designs tailored to individual preferences while relying on the design survey data collected among 750 graphic design students, teachers, and professionals, from different parts of China. Five important evaluation components provided by the framework design are demographics, Artificial Intelligence (AI) awareness, preference toward prompts, design evaluation, and user satisfaction, which help to refine the design outputs. Data were collected using structured questionnaires and analyzed using SPSS to identify trends among users and expectations regarding design. The study results show that different user profiles tend to interact with AIGC solutions differently and shed light on prompt design and output satisfaction. By building human-centered feedback into the design cycle, the study advances the development of intuitive, culturally relevant, and user-centered AI-generated graphic designs. The model is a significant step in bringing user-centered creative AI solutions to reality. Among 750 participants, bachelor’s holders showed the highest AIGC satisfaction (32.2%), and students preferred visual prompts (28.9%). Teachers favored keyword prompts (31.7%). Chi-square tests showed no significant associations (p > 0.05), suggesting consistent AIGC use across groups. Graphic design generation AI generated design Text to image synthesis Prompt engineering Survey based analysis Figures Figure 1 1. Introduction Artificial Intelligence and generative models have enabled spectacular advances to take place in graphic design. Previously, graphic design was the support of manual creativity and time-intensive tasks of professionals using some visual tools [ 1 ]. However, with the newfound interest of Artificial Intelligence Generated Content (AIGC), specifically those models that fall under the domain of deep learning, the landscape is now looking more towards automation and intelligent design synthesis [ 2 ]. Multi-modal data, meaning diverse types of data such as text, images, and audio integrated together, enrich the capacity of these models to understand context and produce visual content with greater meaning [ 3 ]. This intersection of AI and design promotes a data-centric perspective wherein models learn aesthetic, semantic, and stylistic patterns from extensive datasets [ 4 ]. In the study on multi-modal AIGC mimicking design capabilities of a human, the goal is to enable the automatic generation of adaptive, scalable, and context-aware graphic content that can help speed up desktop-based design, make it widely accessible, and at the same time become data-aware [ 5 ]. Multi-modal AIGC graphic design generation models get applied in almost every industry [ 6 ]. In marketing and advertising, banner and poster creation, as well as promotional materials, are created via automatic processes by analyzing given textual prompts and integrating these with some form of visual assets, thereby speeding up the campaign development process [ 7 ]. In e-commerce, these models create product displays and product visuals dynamically in response to product descriptions, user behavior, and sentiment analysis [ 8 ]. Educational content programs also benefit, with AI creating informative infographics, diagrams, or learning visuals tailored to curriculum data. In entertainment, it helps create concept art, storyboards, and thumbnails from script or plot inputs [ 9 ]. It is about speeding up UX/UI design processes by generating interface prototypes from user flow documents. The model enables generation of graphics toward demand and in scalable manner-a huge advantage in cutting down human effort while visually promoting communication and design on the digital platforms [ 10 ]. AIGC graphic models step beyond the traditional arts to a realm of possible uses in domains emerging and specialized [ 11 ]. On the social media content creation side of things, these models can be used to cook up attractive posts, stories, and thumbnails generated according to an audience's preferences and engagement trends calculated from user data [ 12 ]. AIGC can be used in virtual and augmented reality to dynamically design scenes and interactive visual elements based on a narrative script or from sensor inputs [ 13 ]. A healthcare and scientific communication, explanatory visuals, charts, and data-driven infographics help communicate complex information to non-expert audiences [ 14 ]. And in journalism and publishing, they are used to develop engaging visual storytelling supporting a written article and, thus, engaging the reader [ 15 ]. This multi-modal input enables the models to grasp context, emotion, and function, allowing them to produce highly adaptive, efficient, and context-aware design outputs across a large number of platforms and industries [ 16 ]. To overcome these challenges identified for AIGC-based graphic design, this research incorporates user feedback into the model to ensure output relevance and perspectives. By acquiring multi-dimensional user data-an indexing across demographic age groups, gender, education, occupation, income level, and region-thereby reinforcing cultural and contextual relevance. Multi-modal data with texts and images are beneficial during prompt interpretation and subsequent design. Iterative model development and tool design will be developed based on the survey insights. Combination of cutting-edge descriptive analytics aids in making decisions based on data, together with real users' inputs, engenders confidence and satisfaction within the user base. By a day, through the aforementioned activities and efforts, the alibi for bridging the divide remains between the capabilities of AI and thus human creative intent. 1.1 Research Aim The aim of the research was to create a multi-modal, data-driven AIGC framework, which would produce contextually relevant and beautiful graphic designs from mixed user-provided visual-textual inputs. An attempt was made to close the gap existing between automated content generation, on the one hand, and human-centered design, on the other, by involving direct user feedback in the design process. This study impact of demographics, AI awareness, prompt preference, and design evaluation on user satisfaction related to designs generated by AI. Another focal point is ensuring that graphic designs can be optimized for relevance and developed on a more personalized basis through prompt optimization and multimodal analysis. The study aims to conduct numerous structured surveys among graphic design students, teachers, and practitioners from different parts of China some crucial variables affecting acceptance of designs. Further study will be made regarding how different user backgrounds and experiences influence their own expectations and interaction with AIGC tools. Further, the study will be determining whether the AI-generated output is on a par with accepted industry-level design standards. Finally, the research intends to come up with an adaptive and user-centered AIGC model for creative design applications. 1.2 Research Objectives This research aims to produce a multi-modal, data-driven AIGC framework for graphics that results from the full integration of visual and textual information. The study subjects generating personalized and aesthetically relevant design outputs from the user input and feedback. To analyze whether demographic factors, awareness of AI, prompt preferences, and design evaluation might affect adoption and satisfaction with AIGC tools. Structured surveys with students, instructors, and industry professionals identify user needs and preferences. The framework utilizes this feedback to ensure better design and contextual relevance. Another important aspect is ensuring that the generated outputs satisfy professional and user expectations. Refinement of human behavior analysis and the related AI interaction will provide grounds for enhancement in creativity and usability. It leads to an advancement of human-AI cooperation in the creative design field. 1.3 Research Questions & Hypothesis 1.3.1 Research Question How do users in China perceive and evaluate AI-generated graphic designs created using multi-modal (text and image) inputs? What are the key visual and textual design preferences among Chinese users that can influence AIGC-based graphic design generation? To what extent does the integration of user feedback improve the relevance, cultural alignment, and aesthetic quality of AI-generated graphic content? How can survey-based user data be effectively used to guide and optimize multi-modal design generation processes in AIGC systems? 1.3.2 Hypothesis H1 : The use of multi-modal inputs (combining text and image data) significantly enhances the contextual accuracy and visual appeal of AI-generated graphic designs. H2 : AI-generated graphic designs that incorporate user preferences collected through surveys are rated higher in cultural relevance and user satisfaction. H3 : There is a significant relationship between users' familiarity with design principles and their acceptance of AIGC-generated content. H4 : Graphic designs generated with user-guided prompts better reflect individual design expectations compared to those generated without user input. 1.4 Research Organization The research is organized as follows: Section 1 offers an Introduction, where the research talks about the transformation of graphic design through AI and multimodal integration for intelligent content generation. Section 2 : Literature Survey covers the existing frameworks for AIGC and developments in AI design tools and design methods. Section 3 : Hypothesis development, Section 4 : Methods and Materials describes the conceptual model, survey data collection process throughout China, and data analysis approaches. Section 5 : Results and Discussion constitutes interpretations of the users’ feedback on how multimodal inputs combined with prompt-based generation mechanisms will improve satisfaction and relevancy with designs. Section 6 : Conclusion and Future Work outlines the research findings and identifies several future directions, including real-time feedback, adaptive learning, and integration of multi-cultural inputs to better facilitate AI-human collaboration in graphic design. 2. Literature survey Zhang, Y., et al. [ 17 ] suggested a unified, data-driven image matting engine especially designed for mobile AIGC applications within photo galleries. The engine designs lightweight architecture suitable for mobile, diversely weighing matting quality against computational time. This particular framework effectively assimilates multiple matting tasks into a single engine through shared representations, thereby making it quite suitable for AIGC ecosystems for real-time and on-device photo editing. Ren, L., et al. [ 18 ] investigated AI-generated content (AIGC) for industrial time series analysis going from traditional deep generative models to large-scale generative models. GANs and VAEs have been utilized and the emerging place of foundation models, including diffusion and transformer-based generators. Issues in data quality, interpretability, and a pipeline for real-time generation are addressed by the work. Jin, J., et al. [ 19 ] presented research on AIGC and its potential to stimulate design innovation by way of evidence for the augmentation of creative process across several stages of design. The framework tries to incorporate AIGC tools for ideation, concept development, and refinement. Further, the study addresses some AIGC activities that enhance efficiency, diversity, and consumer orientation of design outputs. LIU, W. and Bezuhla, R., [ 20 ] introduced a comparative analysis between the parametric generation and AIGC, novel techniques in the fields of art and design. It studies their respective properties of strength: parametric methods for precision and control, and AIGC for creativity and adaptability. A collaborative innovation model is proposed that combines the two concepts to allow for more flexible design and creative exploration. Xiao, J., et al. [ 21 ] utilized an AIGC-based model for intelligent course design and the automatic generation of teaching resources. It applies AIGC technologies to start curriculum planning, material creation, and personalized delivery of content. This model connects the generative algorithm to the teaching objectives to improve efficiency and adaptability of the teaching process. Xiao, Y., et al. [ 22 ] focused on AI-assisted intelligent design tools for fitting traditional Dong paper-cut designs. The combination of DL and pattern culture features enables the model to grasp the stylistic nature of Dong art while allowing creative variations. It supports the automatic generation of patterns that do not compromise cultural identity and artistic integrity. Through intelligent design tools, this method shows how AIGC could breathe modern life into traditional crafts. Zhao, L., et al. [ 23 ] explained a newly forged evaluation mechanism based on AIGC for packaging design courses with an aim to better assessment systems through intelligent content generation. It incorporates AI-generated content to ensure objective, varied, and approach-based criteria tailored to any given evaluation. This model will promote meaningful feedback and create a more constructively design-oriented environment for student creativity. Li, H., et al. [ 24 ] studied a critical interpretive synthesis in regards to the application and impact of artificial intelligence upon graphic design. It looks into the changing of creative workflows and even aesthetics of design itself, or the roles of designers, through the use of AIs including AIGC. The study gives a complete grasp of the innovative role of AI in the field of graphic design. Table 1 shows the comparison of the existing methods. 2.1 Research Gap Despite the quick rise of AI in the creative fields, particularly graphic design, most AIGC systems have traditionally lacked the angle of personalization, cultural alignment, or human intent interpretation. Most models are trained on generic datasets and thus fail to adapt to individual user preferences or regional visual expectations [ 25 ]. There is much less in the literature that examines how user prompts influence directly the quality of output, and there is scant research integrating user feedback into the design generation cycle. Although multi-modal learning seems promising, very few frameworks are competent in amalgamating textual + visual cues with real-world user preferences. Also, there is minimal research into large-scale user-centered surveys in non-Western contexts, such as China. Their familiarity with AI tools and its consequent effect on users' satisfaction and trust evaluation of AI-aided artworks conspicuously remains unexamined [ 26 ]. These limitations reinforce the idea that a more inclusive, adaptive, and context-aware approach would be the way forward. This study contributes to bridging these research gaps by incorporating survey-oriented user data within the framework of a multi-modal approach, allowing for relevant user-based alignment of AIGC. Table 1 shows the hypothesis development [ 27 ]. 3. Hypothesis development Hypothesis development is that combining multi-modal data (text and image) has a significant effect on user-satisfaction, aesthetic quality, and contextual relevance of outputs of AI-generated graphic design systems. It is argued that user preferability, AI-awareness, and prompt-specificity decrease or increase the effectiveness of the generated designs. The study hypothesizes that user-centered AIGCs will yield outputs that are more pleasing and closer to personal needs. The hypothesis is tested through survey feedback and statistical analyses. Hypothesis Statement Theoretical Rationale Expected Relationship H1 The integration of multi-modal inputs (text + image) improves the quality and contextual relevance of AI-generated graphic designs. Multi-modal learning theories suggest that combining different data types enhances understanding and generation accuracy in deep learning systems. Positive correlation between multi-modal input usage and improved design quality H2 AI-generated designs tailored using survey-based user preferences will be rated higher in cultural relevance and satisfaction. User-centered design theory emphasizes that systems aligning with user expectations lead to better acceptance and usability. Positive relationship between user-informed design generation and user satisfaction. H3 Users with higher familiarity in visual design will show greater acceptance of AIGC-generated content. Familiarity bias and media literacy theory suggest that prior exposure enhances perceived value and trust in automated systems. Positive correlation between design knowledge and AIGC content acceptance. H4 signs generated using user-guided textual prompts are more aligned with user intent than designs generated without input. Prompt-based generation models rely on input cues to shape output relevance, as supported by language image alignment theories. Positive effect of guided prompts on perceived relevance and accuracy of the design. The hypotheses development Table 1 reflects the multi-modal, user-informed AIGC graphic design generation model. H1 claims that integrating both text and image data will increase contextual accuracy and design quality, under the multi-modal learning theory that posits that richer representation arises from data fusion. H2 targets a user-centered design approach hypothesizing that incorporation of user preferences elicited through survey data enhances cultural relevance and satisfy those needs. Based on media literacy theory, H3 conjectures that an individual knowledgeable in visual design is more prone to accept and put a favorable evaluation to AI-generated content due to favorable familiarity. H4 highlights the role of user-guided prompts, hypothesizing that design generation becomes increasingly aligned with users' expectations as textual input is provided by the users. These hypothesized statements from well-established precedent theories and anticipate positive correlations between user input and a variety of design quality and acceptance aspects. These combined provide grounds for evaluating the proposed AIGC framework. 4 Methods and materials The study of this research is to develop and evaluate a multi-modal, data-driven framework that integrates visual and textual inputs for AI-based graphic design. The study intends to custom-build graphic designs that are contextually accurate, culturally relevant, and appealing in terms of aesthetics by the system with feedback data-from structured surveys, gathered in China. The research aims to study the influence of user preferences on acceptance and quality of AI-generated content and evaluates whether the incorporation of multimodal data adds improvements to design results. Special emphasis is laid upon binding system outputs to actual user expectations and aesthetics. The data gathered from user feedback act as input to analyze user needs and solutions for design generation. Finally, the study aims to be a step forward in the realm of user-centered AIGC technologies for creative design. Figure 1 shows the conceptual model framework. 4.2 Methods 4.2.1 Data collection In the study, data were collected a structured questionnaire to 750 participants consisting of graphic design students, teachers, and working creative professionals throughout provinces of China. The main aim of the questionnaire was to garner quantitative essentials pertaining to five major elements: participant information, visual design liking, expectations from text-to-image, awareness of AI-generated content, and satisfaction in the existing design tools. Each item was close-ended and presented as Likert scales, multiple choice, or yes or no to perform descriptive statistical analysis on the collected data. Participation in this survey was by mere voluntary choice, with all respondents informed about the purpose of the survey, assured of their anonymity, and guaranteed full confidentiality. To allow equal opportunities and fair participation from all urban and rural areas, the questionnaire was distributed online (through Wenjuanxing and WeChat survey tools) and also in hard copy where technical access was an issue. The period for data collection lasted four weeks, after which the data were compiled, cleaned, and coded. Frequencies, percentages, means, and standard deviations were analyzed in SPSS to draw trends and interrelations between the major variables in design. Table 2 shows the Summary of survey data collection framework for AIGC graphic design. Table 2 Summary of survey data collection framework for AIGC graphic design Aspect Details Sample Size 750 participants Target Group Graphic design students, teachers, and working creative professionals Geographical Scope Various provinces across China (urban and rural regions) Data Collection Tool Structured questionnaire Distribution Method Online via Wenjuanxing and WeChat; offline via printed forms Data collection period 4 weeks Analysis Tools Data cleaned and coded; analyzed using SPSS (frequencies, percentages, means, standard deviations) 4.2.2 Survey components Demographics Information (Basic details like age, role, and design experience). AI Awareness (Knowledge and prior use of AI design tools). Prompt Preferences (User choices in text prompts and visual themes). Design Evaluation (Feedback on the quality and relevance of AI-generated designs). User Satisfaction (Overall experience and willingness to use AIGC tools again). 4.3 Study variables 4.3.1 Demographics Information Demographic information is essential to understand the background and diversity of those who contributed to the development of the AIGC graphic design model. Data were collected from 750 people, graphic design students, teachers, and practitioners from most provinces of China. Major demographic variables included age category, gender, professional job title, years in design, and urban versus rural. Such information serves to classify user perspectives and emphasize how different backgrounds affect perceptions and expectations in the AIGC environment. Having a fair idea about participant demographics paves way towards nailing down how a user type such as a student or a teacher goes about the issue of tool usability or satisfaction. It also drives how variations across preferences in design can be studied with respect to experience or geographical region. Thus gathering information pertaining to users preserves inclusivity and has wide representation from all walks of user profiles. Diversity thereby makes the study findings more generalizable and assists in making the model flexibly usable across different use cases. A demographic study lays a base for development towards correlating deeper user behavior aspects in product design generation. 4.3.2 AI Awareness AI Awareness in this study is defined as a participant's exposure to, comprehension of, or engagement with AI-generated content tools in the scope of graphic design. In the era of increasing AI-related disruption into creative workflows, it is therefore essential to weigh how design students, educators, and professionals envisage this tool or even comprehend its functioning and potential. The survey attempts to assess whether the respondents are familiar with the tools DALL·E, Midjourney, and Canva AI and, if yes, before how often they use them. Furthermore, it attempts to ascertain their understanding of the internal processes these tools employ, particularly the text-to-image generation. Formal training and previous exposure to the technology are critical in indicating preparedness for academic or professional integration This segment further identifies awareness issues standing in the way of trying-versus-adoption. It also sets the stage for designing an AI-based infoline in design education. 4.3.3 Prompt Preferences Prompt Preferences involve the generation, selection, and alteration of text-based prompts by users while interacting with AIGC tools for the purpose of graphic design. Participants' prompt-creation preferences-using simple descriptive phrases, offering detailed instructions, or defining thematic keywords-technically direct the AI toward achieving the respective visual outcome. The quality, length, and clarity of prompts directly influence how relevant and creative the output designs will be, therefore making prompt preferences a very important concern. In addition to measuring how often the participants revise their prompts and how comfortable they feel in experimenting, the survey also targets whether they prefer to input their data manually or lean more on AI-given suggestions. Another area under scrutiny is whether the design background affects how complicated their prompts become. This segment thus attempts to drill down into user behavior and creativity trends to gain insights for potential improvements to user-AI collaboration. Ultimately, prompt preferences play a pivotal role in meeting AIGC tools with users' intent-whether to bring more gratification into the design process. 4.3.4 Design Evaluation The evaluation of design is an assessment with the user in this work to determine the quality and creativity of the AIGC-generated graphic output. In conducting the analysis, the outputs were evaluated in terms of visual appeal, prompt alignment, cultural sensitivity, and functional suitability. This actually tests whether or not the users consider the designs generated usable and acceptable for professional use in the real world. In addition, it collects whatever feedback there is, in terms of clarity, color collaboration, and layout, as well as uniqueness. This evaluation is used by the study to determine the strengths and weaknesses of the AIGC model when applied to visual outputs. In aiding the study, evaluation pattern also investigates whether the judgement pattern is subjectively influenced by design experience. Evaluation of design, therefore, is feedback in the generation operation. Hence, evaluation establishes the path toward developing AI tools that are capable of generating designs contextually satisfactory and pleasing to the user. 4.3.5 User Satisfaction User Satisfaction, in this regard, measures the holistic satisfaction of the users consisting of students, designers, and professionals with respect to AI-generated graphic designs and the AIGC tool itself. It shows how well the designs generated meet their creative needs for ease of use, customization, or accuracy to their expectations. Satisfaction considers the effectiveness of the tool in interpreting prompts, integrating multimodal data, and producing aesthetically pleasing results. This construct also analyzes whether the users trust the AI as a co-creator and their willingness to continue using such software. The questions positioned in the survey evaluate satisfaction on design quality, efficiency, relevance, and time saved, just to mention a few. High satisfaction levels invariably show that the AI has succeeded in meeting human intention. Consequently, it implies a willingness among creative professionals to incorporate AI tools in real workflows. So, the better the user satisfaction, the better the usability, effectiveness, and acceptance of the AIGC model within the design ecosystem. 4 Descriptive statistics There used to be considered types of descriptive statistics in this paper, a method to analyze and summarize quantitative data obtained from 750 participants, studied by an evaluation of AI-based graphic design. These methods convey respondent information regarding frequencies, percentages, means, and standard deviations. Such statistics present opposing facts, which may serve as knowledge of the participants: for instance, some are aware of AI, whereas others are not; some prefer certain kinds of designs, whereas others do not; and some participants provide high satisfaction scores, whereas others give lower scores. Let us say that frequencies and percentages inform us of how many of these users have agreed that they have used some sort of AIGC tool, whereas means and standard deviations foster understanding of the trending evaluations on design quality and own relevance as given by the users themselves. This will allow comparison between the trending levels from monetized users such as students, teachers, working professionals, plus other jurisdictions. The descriptive analysis was carried out in SPSS software to formalize and give more reliability to the interpretations of the survey answers. This facilitates further tests related to differences in behavior and expectation according to user demographics or experience. Thus, descriptive statistics become the basis for further analysis and interpretation. 5. Results and discussion Results indicated that most participants were highly to moderately familiar with AI-generated content tools, with more than 70% having interacted with at least one AIGC tool in the past. Design-savvy users considered AI-generated images more relevant and visually appealing, particularly when given detailed prompts. The confluence of text and image inputs greatly enhanced quality and user satisfaction in generated designs. To according user background and positively correlated with design experience and prompt complexity. Overall, the study validates that personalized, prompt-guided AIGC tools improve creative output and are appreciated by users. These findings help attest to the potential inclusion of AIGC tools within educational and professional design processes. 5.1 Descriptive statics Questions Num. of statics Minimum statics Maximum statics Mean Std. Deviation Variance statics Statics Std. Error How frequently do you use AIGC tools? 750 1 5 2.95 0.051 1.403 1.969 I understand how text-to-image generation works 750 1 5 2.89 0.052 1.434 2.056 Received training/guidance on AIGC 750 1 2 1.50 0.018 0.500 0.250 Knowledge of AIGC in graphic design 750 1 5 2.99 0.053 1.456 2.120 Preferred prompt style for image generation 750 1 4 2.48 0.041 1.132 1.281 Table 3: Descriptive statics Table 3 shows the descriptive statistics analyze 750 responses on various AIGC-related topics. The average AIGC usage frequency is moderate (Mean = 2.95), with responses scattered across the scale (SD = 1.403). Understandably so-the mean responses hover around the middling point for text-to-image generation (Mean = 2.89) and AIGC for graphic design (Mean = 2.99), reflecting a moderate degree of familiarity. Only elementary training or orientation was supposed to be imparted to the respondents (Mean = 1.5, binary scale). A preference for concise or moderately explicit prompts appears to be suggested for the prompt style (Mean = 2.48). 5.2 Distribution of Participants by Academic Qualification Analysed data Academic qualification Frequency Percent Valid Percent Cumulative Percent Valid Bachelor's 187 24.9 24.9 24.9 Diploma 191 25.5 25.5 50.4 High school 168 22.4 22.4 72.8 Master's or above 204 27.2 27.2 100.0 Total 750 100.0 100.0 Table 4: Distribution of Participants by Academic Qualification Table 4 presents the academic qualifications from 750 participants. The largest cluster has Master’s degrees and above at 27.2%, followed closely by those with diplomas at 25.5% and Bachelor’s degree holders at 24.9%. The smallest group make up 22.4%, i.e., high-school graduates. The cumulative percentages steadily increase, thus reaching 100% at the highest qualification. The view suggests respondents fairly evenly distributed in their educational backgrounds. 5. 3 Age-Based Analysis of Continued AIGC Tool Usage Question Age Keywords Would you continue using AIGC? Definitely Definitely not Not sure Probably What is your age? 21–30 Count 27 28 25 25 What is your age? % 20.0% 20.7% 18.5% 18.5% Would you continue using AIGC? % 16.8% 20.4% 17.7% 17.0% Total % 3.6% 3.7% 3.3% 3.3% 31–40 Count 31 20 20 33 What is your age? % 23.8% 15.4% 15.4% 25.4% Would you continue using AIGC? % 19.3% 14.6% 14.2% 22.4% Total % 4.1% 2.7% 2.7% 4.4% 41–50 Count 27 33 30 28 What is your age? % 17.2% 21.0% 19.1% 17.8% Would you continue using AIGC? % 16.8% 24.1% 21.3% 19.0% Total % 3.6% 4.4% 4.0% 3.7% 51 or above Count 42 28 35 39 What is your age? % 24.0% 16.0% 20.0% 22.3% Would you continue using AIGC? % 26.1% 20.4% 24.8% 26.5% Total % 5.6% 3.7% 4.7% 5.2% Table 5: Age-Based Analysis of Continued AIGC Tool Usage Table 5 displays the link between age groups and their intent to continue using AIGC tools. Among respondents 51 and older, the largest percentage (26.5011%) said they would "Probably" continue, showing a somewhat strong interest in these tools coming from older users. The age bracket of 31–40 showed the highest "Definitely" response at 23.8%, indicating more certainty in that category. In contrast, younger participants between the ages of 21 and 30 and under 20 gave responses in all categories that were somewhat even but not decisive. Prima facie, the intent to continue using AIGC tools seems equally spread across all the age groups with a slightly higher commitment from middle-aged and older users. 5.4 Relationship Between Job Role and Preferred Image Prompt Format Preferred prompt style for image generation Question Qualification Keywords Count % Visual references What is your current role? Design professional Count 36 32 What is your current role? % 23.5% 20.9% Preferred prompt style for image generation 19.7% 17.1% Total % 4.8% 4.3% Other Count 43 47 What is your current role? % 23.0% 25.1% Preferred prompt style for image generation % 23.5% 25.1% Total % 5.7% 6.3% Student Count % 46 54 What is your current role? % 23.1% 27.1% Preferred prompt style for image generation % 25.1% 28.9% Total % 6.1% 7.2% Teacher Count 58 54 Table 6 : Relationship Between Job Role and Preferred Image Prompt Format Table 6 presents into how different prompt styles are preferred in the incarnation of AIGC image generators by people in various roles. Students show a higher preference for visual references (28.9%) and keywords (25.1%), being highly engaged with both types of prompts. Teachers would slightly prefer keywords, 31.6%, over visual references, 28.6%; meanwhile, design professionals chose more visual references. The "other" category tends to lean towards having an equal preference for either style. Along the board, visual references tend to be a tad more preferred-for instance, by students and general users. Chi-Square Results: Age vs. Future Use of AIGC Chi-Square Test Value Degrees of Freedom Asymptotic Significance (2-sided) Pearson Chi-Square 10.849 8 0.210 Likelihood Ratio 11.007 8 0.201 Num. of Valid Cases 750 Table 7: Chi-Square Results: Age vs. Future Use of AIGC Tools The relationship between the two categorical variables is tested through the chi-square test. The Pearson value of the Chi-square is 10.849 with 8 degrees of freedom, and its p-value is 0.210 which is above the threshold of 0.05, indicating no statistically significant association. The Likelihood Ratio has a value of 11.007 with the same degrees of freedom and a p-value of 0.201, also failing to reach significance. This implies that the observed frequency of response does indeed not differ significantly from what is expected by chance. The analysis is considered as carried out for 750 valid cases. 5.6 Overall Summary of Current Role and Preferred Prompt Style for Image Generation Question Qualifications Keywords Satisfaction with quality of AIGC outputs Total Very satisfied What is your highest academic qualification? Bachelor's Count 47 187 What is your highest academic qualification? % 25.1% 100.0% Satisfaction with quality of AIGC outputs % 32.2% 24.9% Total % 6.3% 24.9% Diploma Count 35 191 What is your highest academic qualification? % 18.3% 100.0% Satisfaction with quality of AIGC outputs % 24.0% 25.5% Total % 4.7% 25.5% High school Count 32 168 What is your highest academic qualification? % 19.0% 100.0% Satisfaction with quality of AIGC outputs % 21.9% 22.4% Total % 4.3% 22.4% Master's or above Count 32 204 Table 8: Overall Summary of Current Role and Preferred Prompt Style for Image Generation The table relates academic qualifications to satisfaction with the quality of AIGC outputs, especially those rated as "Very satisfied." The proportion of bachelor's degree holders who were very satisfied was 25.1%, with 47 respondents representing the majority for this count (32.2% of all very satisfied responses). Holders of diplomas and those with high school qualifications follow in the order, with satisfaction percentages at 24.0 and 21.9, respectively. Considering that respondents with a master's or above qualification form the highest educated group by and large, fewer reported "very satisfied" counts. This thus shows that higher qualifications may not be in tune with higher satisfaction on the quality of AIGC output. 6. Conclusion and Future work This research is based on a multi-modal, data-driven AIGC framework for personalized graphic design by integrating user-centered data from surveys. Thus, it was found that demographics, AI awareness, prompt preferences, and design evaluation affect user satisfaction and quality of design. With the use of the proposed model, there is some potential in producing pleasing output from contexts that resonate with user's expectations. Structured feedback from students, educators, and professionals has been leveraged in defining the parameters that help improve AI-human creative collaboration. The challenges remain that users might have varying levels of familiarity with AI and have varying tolerances for prompt precision. Future work will see the enlargement of the current dataset for the incorporation of cross-cultural user inputs to ensure wider applicability. Other improvements envisaged include real-time feedback and adaptive learning within the AIGC system. Integration of emotional and behavioral analytics is another prospective avenue that might enhance personalization. The end goal will be a more intelligent, inclusive, and intuitive AIGC system that can cater to the ever-changing nature of graphic designers. Declarations Conflicts of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethical Approval This study was conducted in accordance with institutional and national ethical guidelines. Ethical approval was granted by the Academic Ethics Committee of Zhejiang Yuexiu University prior to the commencement of the research. Consent to Participate Informed consent was obtained from all individual participants involved in the study. Participation was voluntary, and participants could withdraw at any time. Consent to Publication All participants were informed that the results of the study might be published in academic journals. Consent for publication was obtained during the data collection phase. Competing Interest The authors declare no competing interests. Funding Statement This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution Yanzhe Yang conceptualized the study, designed the framework, led the data analysis, and drafted the manuscript.Lulu Zhang contributed to the data collection process, literature review, and revision of the manuscript.Both authors have read and approved the final manuscript. Data Availability The data supporting the findings of this study are available from the corresponding author upon reasonable request. Due to privacy restrictions and institutional policy, raw survey data involving participant information cannot be publicly shared. References Hughes RT, Zhu L, Bednarz T. Generative adversarial networks–enabled human–artificial intelligence collaborative applications for creative and design industries: A systematic review of current approaches and trends. Front Artif Intell. 2021;4:604234. Zhu C, Cui L, Tang Y, Wang J. 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IJCAI, pp. 7183–7186, Aug. 2023. Ren L, Wang H, Li J, Tang Y, Yang C. AIGC for industrial time series: From deep generative models to large generative models. arXiv preprint, arXiv:2407.11480, 2024. Jin J, Yang M, Hu H, Guo X, Luo J, Liu Y. Empowering design innovation using AI-generated content. J Eng Des. 2025;36(1):1–18. Bezuhla R. Comparative analysis and collaborative innovation of parametric generation and AIGC. Art Des, 4, pp. 22–32, 2024. Xiao J, Wang Z, Yu Z, Xiaocong D. Exploration of an AIGC-driven intelligent course design and automated teaching resource generation model. Int J High Speed Electron Syst, p. 2540777, 2025. Xiao Y, Lin X, Ji T, Qiao J, Ma B, Gong H. AI-assisted design: Intelligent generation of Dong paper-cut patterns. Electronics. 2025;14(9):1804. Zhao L, Misri IB, Yang J, Dong R. Research on the construction of evaluation mechanism for packaging design course driven by AIGC content generation. J Humanit Arts Soc Sci, 9, 5, 2025. Li H, Xue T, Zhang A, Luo X, Kong L, Huang G. The application and impact of artificial intelligence technology in graphic design: A critical interpretive synthesis. Heliyon. 2024;10:21. Liang H. Generative AI in fashion design process. Insights from Chinese practitioners; 2025. Ma H, Li N. Exploring user behavioral intentions and their relationship with AI design tools: A future outlook on intelligent design. IEEE Access, 2024. Zhu Z, Yu T, Wang Y, Xu J. Revolutionizing design content with AIGC: User-centered challenges, opportunities, and workflow evolution. IEEE Access, 2025. Additional Declarations No competing interests reported. Supplementary Files appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":54694,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Model of Multi-Modal Data-Driven AIGC Graphic Design Generation Framework\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7431399/v1/8e820c07d61acb6e55fd96e6.png"},{"id":97250153,"identity":"4f0745d2-c2dc-44fc-a66f-fc3fb1da41b7","added_by":"auto","created_at":"2025-12-02 13:14:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1396845,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7431399/v1/269fc349-9e4e-4a9c-af2f-1cd90f892ede.pdf"},{"id":94397796,"identity":"bafcbdea-b110-455b-8aec-5c07da8e882c","added_by":"auto","created_at":"2025-10-27 13:56:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":299774,"visible":true,"origin":"","legend":"","description":"","filename":"appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-7431399/v1/57bd7dedffb2293e54979321.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on multi-modal data-driven AIGC graphic design generation model","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eArtificial Intelligence and generative models have enabled spectacular advances to take place in graphic design. Previously, graphic design was the support of manual creativity and time-intensive tasks of professionals using some visual tools [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, with the newfound interest of Artificial Intelligence Generated Content (AIGC), specifically those models that fall under the domain of deep learning, the landscape is now looking more towards automation and intelligent design synthesis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Multi-modal data, meaning diverse types of data such as text, images, and audio integrated together, enrich the capacity of these models to understand context and produce visual content with greater meaning [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This intersection of AI and design promotes a data-centric perspective wherein models learn aesthetic, semantic, and stylistic patterns from extensive datasets [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In the study on multi-modal AIGC mimicking design capabilities of a human, the goal is to enable the automatic generation of adaptive, scalable, and context-aware graphic content that can help speed up desktop-based design, make it widely accessible, and at the same time become data-aware [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMulti-modal AIGC graphic design generation models get applied in almost every industry [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In marketing and advertising, banner and poster creation, as well as promotional materials, are created via automatic processes by analyzing given textual prompts and integrating these with some form of visual assets, thereby speeding up the campaign development process [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In e-commerce, these models create product displays and product visuals dynamically in response to product descriptions, user behavior, and sentiment analysis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Educational content programs also benefit, with AI creating informative infographics, diagrams, or learning visuals tailored to curriculum data. In entertainment, it helps create concept art, storyboards, and thumbnails from script or plot inputs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. It is about speeding up UX/UI design processes by generating interface prototypes from user flow documents. The model enables generation of graphics toward demand and in scalable manner-a huge advantage in cutting down human effort while visually promoting communication and design on the digital platforms [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAIGC graphic models step beyond the traditional arts to a realm of possible uses in domains emerging and specialized [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. On the social media content creation side of things, these models can be used to cook up attractive posts, stories, and thumbnails generated according to an audience's preferences and engagement trends calculated from user data [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. AIGC can be used in virtual and augmented reality to dynamically design scenes and interactive visual elements based on a narrative script or from sensor inputs [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A healthcare and scientific communication, explanatory visuals, charts, and data-driven infographics help communicate complex information to non-expert audiences [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. And in journalism and publishing, they are used to develop engaging visual storytelling supporting a written article and, thus, engaging the reader [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This multi-modal input enables the models to grasp context, emotion, and function, allowing them to produce highly adaptive, efficient, and context-aware design outputs across a large number of platforms and industries [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo overcome these challenges identified for AIGC-based graphic design, this research incorporates user feedback into the model to ensure output relevance and perspectives. By acquiring multi-dimensional user data-an indexing across demographic age groups, gender, education, occupation, income level, and region-thereby reinforcing cultural and contextual relevance. Multi-modal data with texts and images are beneficial during prompt interpretation and subsequent design. Iterative model development and tool design will be developed based on the survey insights. Combination of cutting-edge descriptive analytics aids in making decisions based on data, together with real users' inputs, engenders confidence and satisfaction within the user base. By a day, through the aforementioned activities and efforts, the alibi for bridging the divide remains between the capabilities of AI and thus human creative intent.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Research Aim\u003c/h2\u003e\u003cp\u003eThe aim of the research was to create a multi-modal, data-driven AIGC framework, which would produce contextually relevant and beautiful graphic designs from mixed user-provided visual-textual inputs. An attempt was made to close the gap existing between automated content generation, on the one hand, and human-centered design, on the other, by involving direct user feedback in the design process. This study impact of demographics, AI awareness, prompt preference, and design evaluation on user satisfaction related to designs generated by AI. Another focal point is ensuring that graphic designs can be optimized for relevance and developed on a more personalized basis through prompt optimization and multimodal analysis. The study aims to conduct numerous structured surveys among graphic design students, teachers, and practitioners from different parts of China some crucial variables affecting acceptance of designs. Further study will be made regarding how different user backgrounds and experiences influence their own expectations and interaction with AIGC tools. Further, the study will be determining whether the AI-generated output is on a par with accepted industry-level design standards. Finally, the research intends to come up with an adaptive and user-centered AIGC model for creative design applications.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Research Objectives\u003c/h2\u003e\u003cp\u003eThis research aims to produce a multi-modal, data-driven AIGC framework for graphics that results from the full integration of visual and textual information. The study subjects generating personalized and aesthetically relevant design outputs from the user input and feedback. To analyze whether demographic factors, awareness of AI, prompt preferences, and design evaluation might affect adoption and satisfaction with AIGC tools. Structured surveys with students, instructors, and industry professionals identify user needs and preferences. The framework utilizes this feedback to ensure better design and contextual relevance. Another important aspect is ensuring that the generated outputs satisfy professional and user expectations. Refinement of human behavior analysis and the related AI interaction will provide grounds for enhancement in creativity and usability. It leads to an advancement of human-AI cooperation in the creative design field.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Research Questions \u0026amp; Hypothesis\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e1.3.1 Research Question\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eHow do users in China perceive and evaluate AI-generated graphic designs created using multi-modal (text and image) inputs?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eWhat are the key visual and textual design preferences among Chinese users that can influence AIGC-based graphic design generation?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTo what extent does the integration of user feedback improve the relevance, cultural alignment, and aesthetic quality of AI-generated graphic content?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHow can survey-based user data be effectively used to guide and optimize multi-modal design generation processes in AIGC systems?\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e1.3.2 Hypothesis\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH1\u003c/b\u003e: The use of multi-modal inputs (combining text and image data) significantly enhances the contextual accuracy and visual appeal of AI-generated graphic designs.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH2\u003c/b\u003e: AI-generated graphic designs that incorporate user preferences collected through surveys are rated higher in cultural relevance and user satisfaction.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH3\u003c/b\u003e: There is a significant relationship between users' familiarity with design principles and their acceptance of AIGC-generated content.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH4\u003c/b\u003e: Graphic designs generated with user-guided prompts better reflect individual design expectations compared to those generated without user input.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e1.4 Research Organization\u003c/h2\u003e\u003cp\u003eThe research is organized as follows: Section \u003cspan refid=\"Sec1\" class=\"InternalRef\"\u003e1\u003c/span\u003e offers an Introduction, where the research talks about the transformation of graphic design through AI and multimodal integration for intelligent content generation. Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e2\u003c/span\u003e: Literature Survey covers the existing frameworks for AIGC and developments in AI design tools and design methods. Section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e3\u003c/span\u003e : Hypothesis development, Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e4\u003c/span\u003e: Methods and Materials describes the conceptual model, survey data collection process throughout China, and data analysis approaches. Section \u003cspan refid=\"Sec22\" class=\"InternalRef\"\u003e5\u003c/span\u003e: Results and Discussion constitutes interpretations of the users\u0026rsquo; feedback on how multimodal inputs combined with prompt-based generation mechanisms will improve satisfaction and relevancy with designs. Section \u003cspan refid=\"Sec25\" class=\"InternalRef\"\u003e6\u003c/span\u003e: Conclusion and Future Work outlines the research findings and identifies several future directions, including real-time feedback, adaptive learning, and integration of multi-cultural inputs to better facilitate AI-human collaboration in graphic design.\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Literature survey","content":"\u003cp\u003eZhang, Y., et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] suggested a unified, data-driven image matting engine especially designed for mobile AIGC applications within photo galleries. The engine designs lightweight architecture suitable for mobile, diversely weighing matting quality against computational time. This particular framework effectively assimilates multiple matting tasks into a single engine through shared representations, thereby making it quite suitable for AIGC ecosystems for real-time and on-device photo editing. Ren, L., et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] investigated AI-generated content (AIGC) for industrial time series analysis going from traditional deep generative models to large-scale generative models. GANs and VAEs have been utilized and the emerging place of foundation models, including diffusion and transformer-based generators. Issues in data quality, interpretability, and a pipeline for real-time generation are addressed by the work.\u003c/p\u003e\u003cp\u003eJin, J., et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] presented research on AIGC and its potential to stimulate design innovation by way of evidence for the augmentation of creative process across several stages of design. The framework tries to incorporate AIGC tools for ideation, concept development, and refinement. Further, the study addresses some AIGC activities that enhance efficiency, diversity, and consumer orientation of design outputs. LIU, W. and Bezuhla, R., [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] introduced a comparative analysis between the parametric generation and AIGC, novel techniques in the fields of art and design. It studies their respective properties of strength: parametric methods for precision and control, and AIGC for creativity and adaptability. A collaborative innovation model is proposed that combines the two concepts to allow for more flexible design and creative exploration.\u003c/p\u003e\u003cp\u003eXiao, J., et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] utilized an AIGC-based model for intelligent course design and the automatic generation of teaching resources. It applies AIGC technologies to start curriculum planning, material creation, and personalized delivery of content. This model connects the generative algorithm to the teaching objectives to improve efficiency and adaptability of the teaching process. Xiao, Y., et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] focused on AI-assisted intelligent design tools for fitting traditional Dong paper-cut designs. The combination of DL and pattern culture features enables the model to grasp the stylistic nature of Dong art while allowing creative variations. It supports the automatic generation of patterns that do not compromise cultural identity and artistic integrity. Through intelligent design tools, this method shows how AIGC could breathe modern life into traditional crafts.\u003c/p\u003e\u003cp\u003eZhao, L., et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] explained a newly forged evaluation mechanism based on AIGC for packaging design courses with an aim to better assessment systems through intelligent content generation. It incorporates AI-generated content to ensure objective, varied, and approach-based criteria tailored to any given evaluation. This model will promote meaningful feedback and create a more constructively design-oriented environment for student creativity. Li, H., et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] studied a critical interpretive synthesis in regards to the application and impact of artificial intelligence upon graphic design. It looks into the changing of creative workflows and even aesthetics of design itself, or the roles of designers, through the use of AIs including AIGC. The study gives a complete grasp of the innovative role of AI in the field of graphic design. Table\u0026nbsp;1 shows the comparison of the existing methods.\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Research Gap\u003c/h2\u003e\u003cp\u003eDespite the quick rise of AI in the creative fields, particularly graphic design, most AIGC systems have traditionally lacked the angle of personalization, cultural alignment, or human intent interpretation. Most models are trained on generic datasets and thus fail to adapt to individual user preferences or regional visual expectations [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. There is much less in the literature that examines how user prompts influence directly the quality of output, and there is scant research integrating user feedback into the design generation cycle. Although multi-modal learning seems promising, very few frameworks are competent in amalgamating textual\u0026thinsp;+\u0026thinsp;visual cues with real-world user preferences. Also, there is minimal research into large-scale user-centered surveys in non-Western contexts, such as China. Their familiarity with AI tools and its consequent effect on users' satisfaction and trust evaluation of AI-aided artworks conspicuously remains unexamined [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These limitations reinforce the idea that a more inclusive, adaptive, and context-aware approach would be the way forward. This study contributes to bridging these research gaps by incorporating survey-oriented user data within the framework of a multi-modal approach, allowing for relevant user-based alignment of AIGC. Table\u0026nbsp;1 shows the hypothesis development [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Hypothesis development","content":"\u003cp\u003eHypothesis development is that combining multi-modal data (text and image) has a significant effect on user-satisfaction, aesthetic quality, and contextual relevance of outputs of AI-generated graphic design systems. It is argued that user preferability, AI-awareness, and prompt-specificity decrease or increase the effectiveness of the generated designs. The study hypothesizes that user-centered AIGCs will yield outputs that are more pleasing and closer to personal needs. The hypothesis is tested through survey feedback and statistical analyses.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHypothesis\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStatement\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eTheoretical Rationale\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eExpected Relationship\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eH1\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"1\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eThe integration of multi-modal inputs (text\u0026thinsp;+\u0026thinsp;image) improves the quality and contextual relevance of AI-generated graphic designs.\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e\u003ccolgroup cols=\"1\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMulti-modal learning theories suggest that combining different data types enhances understanding and generation accuracy in deep learning systems.\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ePositive correlation between multi-modal input usage and improved design quality\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eH2\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAI-generated designs tailored using survey-based user preferences will be rated higher in cultural relevance and satisfaction.\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eUser-centered design theory emphasizes that systems aligning with user expectations lead to better acceptance and usability.\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ePositive relationship between user-informed design generation and user satisfaction.\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eH3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eUsers with higher familiarity in visual design will show greater acceptance of AIGC-generated content.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eFamiliarity bias and media literacy theory suggest that prior exposure enhances perceived value and trust in automated systems.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ePositive correlation between design knowledge and AIGC content acceptance.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eH4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003esigns generated using user-guided textual prompts are more aligned with user intent than designs generated without input.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ePrompt-based generation models rely on input cues to shape output relevance, as supported by language image alignment theories.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ePositive effect of guided prompts on perceived relevance and accuracy of the design.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe hypotheses development Table\u0026nbsp;1 reflects the multi-modal, user-informed AIGC graphic design generation model. H1 claims that integrating both text and image data will increase contextual accuracy and design quality, under the multi-modal learning theory that posits that richer representation arises from data fusion. H2 targets a user-centered design approach hypothesizing that incorporation of user preferences elicited through survey data enhances cultural relevance and satisfy those needs. Based on media literacy theory, H3 conjectures that an individual knowledgeable in visual design is more prone to accept and put a favorable evaluation to AI-generated content due to favorable familiarity. H4 highlights the role of user-guided prompts, hypothesizing that design generation becomes increasingly aligned with users' expectations as textual input is provided by the users. These hypothesized statements from well-established precedent theories and anticipate positive correlations between user input and a variety of design quality and acceptance aspects. These combined provide grounds for evaluating the proposed AIGC framework.\u003c/p\u003e"},{"header":"4 Methods and materials","content":"\u003cp\u003eThe study of this research is to develop and evaluate a multi-modal, data-driven framework that integrates visual and textual inputs for AI-based graphic design. The study intends to custom-build graphic designs that are contextually accurate, culturally relevant, and appealing in terms of aesthetics by the system with feedback data-from structured surveys, gathered in China. The research aims to study the influence of user preferences on acceptance and quality of AI-generated content and evaluates whether the incorporation of multimodal data adds improvements to design results. Special emphasis is laid upon binding system outputs to actual user expectations and aesthetics. The data gathered from user feedback act as input to analyze user needs and solutions for design generation. Finally, the study aims to be a step forward in the realm of user-centered AIGC technologies for creative design. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the conceptual model framework.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Methods\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1 Data collection\u003c/h2\u003e\u003cp\u003eIn the study, data were collected a structured questionnaire to 750 participants consisting of graphic design students, teachers, and working creative professionals throughout provinces of China. The main aim of the questionnaire was to garner quantitative essentials pertaining to five major elements: participant information, visual design liking, expectations from text-to-image, awareness of AI-generated content, and satisfaction in the existing design tools. Each item was close-ended and presented as Likert scales, multiple choice, or yes or no to perform descriptive statistical analysis on the collected data. Participation in this survey was by mere voluntary choice, with all respondents informed about the purpose of the survey, assured of their anonymity, and guaranteed full confidentiality. To allow equal opportunities and fair participation from all urban and rural areas, the questionnaire was distributed online (through Wenjuanxing and WeChat survey tools) and also in hard copy where technical access was an issue. The period for data collection lasted four weeks, after which the data were compiled, cleaned, and coded. Frequencies, percentages, means, and standard deviations were analyzed in SPSS to draw trends and interrelations between the major variables in design. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the Summary of survey data collection framework for AIGC graphic design.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSummary of survey data collection framework for AIGC graphic design\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAspect\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eDetails\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSample Size\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e750 participants\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTarget Group\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGraphic design students, teachers, and working creative professionals\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGeographical Scope\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eVarious provinces across China (urban and rural regions)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eData Collection Tool\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eStructured questionnaire\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDistribution Method\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eOnline via Wenjuanxing and WeChat; offline via printed forms\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eData collection period\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e4 weeks\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAnalysis Tools\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eData cleaned and coded; analyzed using SPSS (frequencies, percentages, means, standard deviations)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2 Survey components\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eDemographics Information (Basic details like age, role, and design experience).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAI Awareness (Knowledge and prior use of AI design tools).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePrompt Preferences (User choices in text prompts and visual themes).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDesign Evaluation (Feedback on the quality and relevance of AI-generated designs).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eUser Satisfaction (Overall experience and willingness to use AIGC tools again).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Study variables\u003c/h2\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1 Demographics Information\u003c/h2\u003e\u003cp\u003eDemographic information is essential to understand the background and diversity of those who contributed to the development of the AIGC graphic design model. Data were collected from 750 people, graphic design students, teachers, and practitioners from most provinces of China. Major demographic variables included age category, gender, professional job title, years in design, and urban versus rural. Such information serves to classify user perspectives and emphasize how different backgrounds affect perceptions and expectations in the AIGC environment. Having a fair idea about participant demographics paves way towards nailing down how a user type such as a student or a teacher goes about the issue of tool usability or satisfaction. It also drives how variations across preferences in design can be studied with respect to experience or geographical region. Thus gathering information pertaining to users preserves inclusivity and has wide representation from all walks of user profiles. Diversity thereby makes the study findings more generalizable and assists in making the model flexibly usable across different use cases. A demographic study lays a base for development towards correlating deeper user behavior aspects in product design generation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e4.3.2 AI Awareness\u003c/h2\u003e\u003cp\u003eAI Awareness in this study is defined as a participant's exposure to, comprehension of, or engagement with AI-generated content tools in the scope of graphic design. In the era of increasing AI-related disruption into creative workflows, it is therefore essential to weigh how design students, educators, and professionals envisage this tool or even comprehend its functioning and potential. The survey attempts to assess whether the respondents are familiar with the tools DALL\u0026middot;E, Midjourney, and Canva AI and, if yes, before how often they use them. Furthermore, it attempts to ascertain their understanding of the internal processes these tools employ, particularly the text-to-image generation. Formal training and previous exposure to the technology are critical in indicating preparedness for academic or professional integration This segment further identifies awareness issues standing in the way of trying-versus-adoption. It also sets the stage for designing an AI-based infoline in design education.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e4.3.3 Prompt Preferences\u003c/h2\u003e\u003cp\u003ePrompt Preferences involve the generation, selection, and alteration of text-based prompts by users while interacting with AIGC tools for the purpose of graphic design. Participants' prompt-creation preferences-using simple descriptive phrases, offering detailed instructions, or defining thematic keywords-technically direct the AI toward achieving the respective visual outcome. The quality, length, and clarity of prompts directly influence how relevant and creative the output designs will be, therefore making prompt preferences a very important concern. In addition to measuring how often the participants revise their prompts and how comfortable they feel in experimenting, the survey also targets whether they prefer to input their data manually or lean more on AI-given suggestions. Another area under scrutiny is whether the design background affects how complicated their prompts become. This segment thus attempts to drill down into user behavior and creativity trends to gain insights for potential improvements to user-AI collaboration. Ultimately, prompt preferences play a pivotal role in meeting AIGC tools with users' intent-whether to bring more gratification into the design process.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e4.3.4 Design Evaluation\u003c/h2\u003e\u003cp\u003eThe evaluation of design is an assessment with the user in this work to determine the quality and creativity of the AIGC-generated graphic output. In conducting the analysis, the outputs were evaluated in terms of visual appeal, prompt alignment, cultural sensitivity, and functional suitability. This actually tests whether or not the users consider the designs generated usable and acceptable for professional use in the real world. In addition, it collects whatever feedback there is, in terms of clarity, color collaboration, and layout, as well as uniqueness. This evaluation is used by the study to determine the strengths and weaknesses of the AIGC model when applied to visual outputs. In aiding the study, evaluation pattern also investigates whether the judgement pattern is subjectively influenced by design experience. Evaluation of design, therefore, is feedback in the generation operation. Hence, evaluation establishes the path toward developing AI tools that are capable of generating designs contextually satisfactory and pleasing to the user.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e4.3.5 User Satisfaction\u003c/h2\u003e\u003cp\u003eUser Satisfaction, in this regard, measures the holistic satisfaction of the users consisting of students, designers, and professionals with respect to AI-generated graphic designs and the AIGC tool itself. It shows how well the designs generated meet their creative needs for ease of use, customization, or accuracy to their expectations. Satisfaction considers the effectiveness of the tool in interpreting prompts, integrating multimodal data, and producing aesthetically pleasing results. This construct also analyzes whether the users trust the AI as a co-creator and their willingness to continue using such software. The questions positioned in the survey evaluate satisfaction on design quality, efficiency, relevance, and time saved, just to mention a few. High satisfaction levels invariably show that the AI has succeeded in meeting human intention. Consequently, it implies a willingness among creative professionals to incorporate AI tools in real workflows. So, the better the user satisfaction, the better the usability, effectiveness, and acceptance of the AIGC model within the design ecosystem.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003e4 Descriptive statistics\u003c/h3\u003e\n\u003cp\u003eThere used to be considered types of descriptive statistics in this paper, a method to analyze and summarize quantitative data obtained from 750 participants, studied by an evaluation of AI-based graphic design. These methods convey respondent information regarding frequencies, percentages, means, and standard deviations. Such statistics present opposing facts, which may serve as knowledge of the participants: for instance, some are aware of AI, whereas others are not; some prefer certain kinds of designs, whereas others do not; and some participants provide high satisfaction scores, whereas others give lower scores. Let us say that frequencies and percentages inform us of how many of these users have agreed that they have used some sort of AIGC tool, whereas means and standard deviations foster understanding of the trending evaluations on design quality and own relevance as given by the users themselves. This will allow comparison between the trending levels from monetized users such as students, teachers, working professionals, plus other jurisdictions. The descriptive analysis was carried out in SPSS software to formalize and give more reliability to the interpretations of the survey answers. This facilitates further tests related to differences in behavior and expectation according to user demographics or experience. Thus, descriptive statistics become the basis for further analysis and interpretation.\u003c/p\u003e"},{"header":"5. Results and discussion","content":"\u003cp\u003eResults indicated that most participants were highly to moderately familiar with AI-generated content tools, with more than 70% having interacted with at least one AIGC tool in the past. Design-savvy users considered AI-generated images more relevant and visually appealing, particularly when given detailed prompts. The confluence of text and image inputs greatly enhanced quality and user satisfaction in generated designs. To according user background and positively correlated with design experience and prompt complexity. Overall, the study validates that personalized, prompt-guided AIGC tools improve creative output and are appreciated by users. These findings help attest to the potential inclusion of AIGC tools within educational and professional design processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDescriptive statics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003eQuestions\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003eNum. of statics\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cem\u003eMinimum statics\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cem\u003eMaximum statics\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cem\u003eStd.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eDeviation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cem\u003eVariance statics\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eStatics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eHow frequently do you use AIGC tools?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eI understand how text-to-image generation works\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e2.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eReceived training/guidance on AIGC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eKnowledge of AIGC in graphic design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003ePreferred prompt style for image generation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u0026nbsp;\u003c/strong\u003e\u003cu\u003eDescriptive statics\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 shows the descriptive statistics analyze 750 responses on various AIGC-related topics. The average AIGC usage frequency is moderate (Mean = 2.95), with responses scattered across the scale (SD = 1.403). Understandably so-the mean responses hover around the middling point for text-to-image generation (Mean = 2.89) and AIGC for graphic design (Mean = 2.99), reflecting a moderate degree of familiarity. Only elementary training or orientation was supposed to be imparted to the respondents (Mean = 1.5, binary scale). A preference for concise or moderately explicit prompts appears to be suggested for the prompt style (Mean = 2.48).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2\u0026nbsp;\u003c/strong\u003e\u003cu\u003eDistribution of Participants by Academic Qualification\u003c/u\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"491\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cem\u003eAnalysed data\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cem\u003eAcademic qualification\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cem\u003eFrequency\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003ePercent\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003eValid Percent\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cem\u003eCumulative Percent\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eValid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eBachelor\u0026apos;s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eDiploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e50.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e72.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eMaster\u0026apos;s or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e27.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e27.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u0026nbsp;\u003c/strong\u003e\u003cu\u003eDistribution of Participants by Academic Qualification\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 presents the academic qualifications from 750 participants. The largest cluster has Master\u0026rsquo;s degrees and above at 27.2%, followed closely by those with diplomas at 25.5% and Bachelor\u0026rsquo;s degree holders at 24.9%. The smallest group make up 22.4%, i.e., high-school graduates. The cumulative percentages steadily increase, thus reaching 100% at the highest qualification. The view suggests respondents fairly evenly distributed in their educational backgrounds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. 3\u0026nbsp;\u003c/strong\u003e\u003cu\u003eAge-Based Analysis of Continued AIGC Tool Usage\u003c/u\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuestion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKeywords\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 331px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWould you continue using AIGC?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDefinitely\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDefinitely not\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot sure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProbably\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"16\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eWhat is your age?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e21\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eWhat is your age? %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e20.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e20.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e18.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e18.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eWould you continue using AIGC? %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e16.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e20.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e17.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e17.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e3.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e3.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e3.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e31\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eWhat is your age? %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e23.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e15.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e15.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e25.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eWould you continue using AIGC? %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e19.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e14.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e14.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e22.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e4.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e2.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e4.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e41\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eWhat is your age? %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e17.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e21.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e19.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e17.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eWould you continue using AIGC? %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e16.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e24.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e21.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e19.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e3.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e4.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e3.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e51 or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eWhat is your age? %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e24.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e16.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e20.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e22.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eWould you continue using AIGC? %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e26.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e20.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e24.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e26.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e5.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e4.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e5.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5:\u003c/strong\u003e \u003cu\u003eAge-Based Analysis of Continued AIGC Tool Usage\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTable 5 displays the link between age groups and their intent to continue using AIGC tools. Among respondents 51 and older, the largest percentage (26.5011%) said they would \u0026quot;Probably\u0026quot; continue, showing a somewhat strong interest in these tools coming from older users. The age bracket of 31\u0026ndash;40 showed the highest \u0026quot;Definitely\u0026quot; response at 23.8%, indicating more certainty in that category. In contrast, younger participants between the ages of 21 and 30 and under 20 gave responses in all categories that were somewhat even but not decisive. Prima facie, the intent to continue using AIGC tools seems equally spread across all the age groups with a slightly higher commitment from middle-aged and older users.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;5.4\u0026nbsp;\u003c/strong\u003e\u003cu\u003eRelationship Between Job Role and Preferred Image Prompt Format\u003c/u\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"688\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 688px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreferred prompt style for image generation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuestion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQualification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKeywords\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCount %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVisual references\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"13\" valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003eWhat is your current role?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eDesign professional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eWhat is your current role? %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e23.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e20.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003ePreferred prompt style for image generation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e19.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e17.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e4.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e4.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eWhat is your current role? %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e23.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e25.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003ePreferred prompt style for image generation %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e23.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e25.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e5.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e6.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eStudent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eCount %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eWhat is your current role? %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e23.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e27.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003ePreferred prompt style for image generation %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e25.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e28.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e6.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e7.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eTeacher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 176px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 146px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e\u003cu\u003e: Relationship Between Job Role and Preferred Image Prompt Format\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTable 6 presents into how different prompt styles are preferred in the incarnation of AIGC image generators by people in various roles. Students show a higher preference for visual references (28.9%) and keywords (25.1%), being highly engaged with both types of prompts. Teachers would slightly prefer keywords, 31.6%, over visual references, 28.6%; meanwhile, design professionals chose more visual references. The \u0026quot;other\u0026quot; category tends to lean towards having an equal preference for either style. Along the board, visual references tend to be a tad more preferred-for instance, by students and general users.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChi-Square Results: Age vs. Future Use of AIGC\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi-Square Test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegrees of Freedom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsymptotic Significance (2-sided)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003ePearson Chi-Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e10.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eLikelihood Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e11.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eNum. of Valid Cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7:\u0026nbsp;\u003c/strong\u003e\u003cu\u003eChi-Square Results: Age vs. Future Use of AIGC Tools\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe relationship between the two categorical variables is tested through the chi-square test. The Pearson value of the Chi-square is 10.849 with 8 degrees of freedom, and its p-value is 0.210 which is above the threshold of 0.05, indicating no statistically significant association. The Likelihood Ratio has a value of 11.007 with the same degrees of freedom and a p-value of 0.201, also failing to reach significance. This implies that the observed frequency of response does indeed not differ significantly from what is expected by chance. The analysis is considered as carried out for 750 valid cases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;5.6\u0026nbsp;\u003c/strong\u003e\u003cu\u003eOverall Summary of Current Role and Preferred Prompt Style for Image Generation\u003c/u\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuestion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQualifications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKeywords\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSatisfaction with quality of AIGC outputs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVery satisfied\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"13\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eWhat is your highest academic qualification?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eBachelor\u0026apos;s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eWhat is your highest academic qualification? %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e25.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eSatisfaction with quality of AIGC outputs %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e32.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e24.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e6.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e24.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eDiploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eWhat is your highest academic qualification? %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e18.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eSatisfaction with quality of AIGC outputs %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e24.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e25.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e4.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e25.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eWhat is your highest academic qualification? %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e19.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eSatisfaction with quality of AIGC outputs %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e21.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e22.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eTotal %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e4.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e22.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eMaster\u0026apos;s or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 8:\u0026nbsp;\u003c/strong\u003eOverall Summary of Current Role and Preferred Prompt Style for Image Generation\u003c/p\u003e\n\u003cp\u003eThe table relates academic qualifications to satisfaction with the quality of AIGC outputs, especially those rated as \u0026quot;Very satisfied.\u0026quot; The proportion of bachelor\u0026apos;s degree holders who were very satisfied was 25.1%, with 47 respondents representing the majority for this count (32.2% of all very satisfied responses). Holders of diplomas and those with high school qualifications follow in the order, with satisfaction percentages at 24.0 and 21.9, respectively. Considering that respondents with a master\u0026apos;s or above qualification form the highest educated group by and large, fewer reported \u0026quot;very satisfied\u0026quot; counts. This thus shows that higher qualifications may not be in tune with higher satisfaction on the quality of AIGC output. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"6. Conclusion and Future work","content":"\u003cp\u003eThis research is based on a multi-modal, data-driven AIGC framework for personalized graphic design by integrating user-centered data from surveys. Thus, it was found that demographics, AI awareness, prompt preferences, and design evaluation affect user satisfaction and quality of design. With the use of the proposed model, there is some potential in producing pleasing output from contexts that resonate with user's expectations. Structured feedback from students, educators, and professionals has been leveraged in defining the parameters that help improve AI-human creative collaboration. The challenges remain that users might have varying levels of familiarity with AI and have varying tolerances for prompt precision. Future work will see the enlargement of the current dataset for the incorporation of cross-cultural user inputs to ensure wider applicability. Other improvements envisaged include real-time feedback and adaptive learning within the AIGC system. Integration of emotional and behavioral analytics is another prospective avenue that might enhance personalization. The end goal will be a more intelligent, inclusive, and intuitive AIGC system that can cater to the ever-changing nature of graphic designers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflicts of Interest\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthical Approval\u003c/h2\u003e\u003cp\u003e This study was conducted in accordance with institutional and national ethical guidelines. Ethical approval was granted by the Academic Ethics Committee of Zhejiang Yuexiu University prior to the commencement of the research.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003cp\u003e Informed consent was obtained from all individual participants involved in the study. Participation was voluntary, and participants could withdraw at any time.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to Publication\u003c/strong\u003e\u003cp\u003eAll participants were informed that the results of the study might be published in academic journals. Consent for publication was obtained during the data collection phase.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding Statement\u003c/h2\u003e\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYanzhe Yang conceptualized the study, designed the framework, led the data analysis, and drafted the manuscript.Lulu Zhang contributed to the data collection process, literature review, and revision of the manuscript.Both authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request. Due to privacy restrictions and institutional policy, raw survey data involving participant information cannot be publicly shared.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHughes RT, Zhu L, Bednarz T. Generative adversarial networks\u0026ndash;enabled human\u0026ndash;artificial intelligence collaborative applications for creative and design industries: A systematic review of current approaches and trends. Front Artif Intell. 2021;4:604234.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu C, Cui L, Tang Y, Wang J. The evolution and future perspectives of artificial intelligence generated content. arXiv preprint, arXiv:2412.01948, 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Z, Xu L, Zheng H, Chen L, Tolba A, Zhao L, Yu K, Feng H. Evolution and prospects of foundation models: From large language models to large multimodal models. 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Revolutionizing design content with AIGC: User-centered challenges, opportunities, and workflow evolution. IEEE Access, 2025.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Graphic design generation, AI generated design, Text to image synthesis, Prompt engineering, Survey based analysis","lastPublishedDoi":"10.21203/rs.3.rs-7431399/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7431399/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe integration of AI into graphic design has revolutionized the creative industry, allowing content to be generated quickly and with extreme variation. These Artificial Intelligence Generated Content (AIGC) systems are largely incapable of enabling personalization, contextual relevance, and cultural sensitivity, due to their heavy reliance on general datasets, and inputs that do not change with user interaction. To tackle these issues, the present work suggests a multi-modal data-driven AIGC graphic design framework in which both text and visuals are integrated with real-time user feedback. The focus is on creating an adaptable model that produces designs tailored to individual preferences while relying on the design survey data collected among 750 graphic design students, teachers, and professionals, from different parts of China. Five important evaluation components provided by the framework design are demographics, Artificial Intelligence (AI) awareness, preference toward prompts, design evaluation, and user satisfaction, which help to refine the design outputs. Data were collected using structured questionnaires and analyzed using SPSS to identify trends among users and expectations regarding design. The study results show that different user profiles tend to interact with AIGC solutions differently and shed light on prompt design and output satisfaction. By building human-centered feedback into the design cycle, the study advances the development of intuitive, culturally relevant, and user-centered AI-generated graphic designs. The model is a significant step in bringing user-centered creative AI solutions to reality. Among 750 participants, bachelor\u0026rsquo;s holders showed the highest AIGC satisfaction (32.2%), and students preferred visual prompts (28.9%). Teachers favored keyword prompts (31.7%). Chi-square tests showed no significant associations (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting consistent AIGC use across groups.\u003c/p\u003e","manuscriptTitle":"Research on multi-modal data-driven AIGC graphic design generation model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-26 01:10:07","doi":"10.21203/rs.3.rs-7431399/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7d2694e6-d50c-45cd-8b46-0f270fe11266","owner":[],"postedDate":"October 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T13:53:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-26 01:10:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7431399","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7431399","identity":"rs-7431399","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}