The Impact of Artificial Intelligence on the Evolution of Graphic Design: Current Practices and Challenges

ShengDong Zhou , Eakachat Joneuraratana, Veerawat Sirvesmas and Pairoj Jamuni
Faculty of Decorative Arts, Silpakorn University, Bangkok, Thailand
Correspondence to: ShengDong Zhou, shengdongzhou27@gmail.com

DOI: https://doi.org/10.70389/PJS.100151

Additional information

• Ethical approval: This research did not include human subjects or proprietary data sets. All studies were performed utilising publicly accessible information and artificial intelligence techniques. No human data was gathered, and the study did not utilise private datasets or confidential information.
• Consent: N/a
• Funding: No industry funding
• Conflicts of interest: N/a
• Author contribution: ShengDong Zhou, Eakachat Joneuraratana, Veerawat Sirvesmas and Pairoj Jamuni – Conceptualization, Writing – original draft, review and editing
• Guarantor: ShengDong Zhou
• Provenance and peer-review: Unsolicited and externally peer-reviewed
• Data availability statement: The data that support the findings of this study are available on request from the corresponding author.

Keywords: Generative design algorithms, ai-driven image generation, Human–ai co-creation, Copyright implications, Stylistic homogenization.

Peer Review
Received: 13 August 2025
Last revised: 29 September 2025
Accepted: 4 October 2025
Version accepted: 4
Published: 31 October 2025

Plain Language Summary Infographic

Introduction

Artificial intelligence (AI) is increasingly changing approaches to graphic design, causing controversy about the future of this profession. Process automation raises the question of the role of the designer in an environment where algorithms can independently create visual content. Some researchers argue that technological changes will lead to the gradual disappearance of traditional design practices. Preliminary research, such as that conducted by Matthews et al., highlights the notion of “designer dematerialisation,” wherein conventional, manual design responsibilities are progressively supplanted by AI algorithms that autonomously oversee and produce design components.¹ This transition, they contend, requires a novel skill set for future designers, who must acclimatise to the evolving environment by mastering AI interaction and strategic decision-making.

The impact of AI transcends simple automation.² It fundamentally alters the essence of creativity in the creative process. Yousif and Vermisso present the notion of a “expanded design space,” wherein AI technologies not only facilitate technical chores but also augment the creative process, enabling designers to investigate novel alternatives and diminish the limitations of traditional thinking.³ The increased function of AI facilitates a wider array of creative results, enhancing both the efficacy and variety of design alternatives. In a similar vein, Jiang examines the impact of generative AI tools on multimodal composition, proposing that AI can function as a co-author in design.⁴ Through the generation of visual compositions and structures derived from textual descriptions or alternative inputs, AI allows designers to concentrate on improving and modifying these creations, hence expediting the process of concept development. These tools are especially advantageous during the first phases of design, when numerous iterations are required to evaluate diverse creative alternatives.

Nonetheless, amid these advancements, apprehensions over the ramifications of AI integration persist. Tomić et al. emphasise the possibility of standardisation in design results, as AI frequently depends on established datasets and algorithms, which may result in repetitious or homogeneous stylistic solutions.⁵ This tendency prompts enquiries over the distinctiveness of AI-generated designs, as widespread utilisation of analogous methods may lead to a diminution of individuality in creative expression. Another important challenge is legal issues related to the use of algorithms in graphic design. Buick examined the use of AI tools in creative processes and their impact on copyright.⁶ The researcher pointed to the fact that AI is trained on databases containing the works of designers, which creates a legal conflict: can the result of AI activity be considered an original work if it is based on the analysis of existing works? This question remains open, as legislative mechanisms do not yet keep up with the rapid development of technology. Chen et al. considered human-AI collaboration in the creative process, emphasising that such interaction can improve design efficiency and expand the creative boundaries of designers.⁷ They analysed the ZhuoluFantasie project, where 7,000 images were created using AI, of which 27 were selected for the final design, demonstrating the potential of AI in generating a variety of visual solutions.

In turn, Hu et al. conducted a qualitative study aimed at investigating the impact of AI on digital design professions.⁸ They focused on professionals’ perception of changes in work practices related to the introduction of AI and found that although AI is already integrated at different stages of design, attitudes to its use vary. Some designers expressed enthusiasm for the new features, while others expressed scepticism and concern about the potential loss of control over the creative process. Mustafa explored the impact of AI on the graphic design industry, focusing on automating routine tasks and generating new design concepts.⁹ The researcher noted that AI can increase efficiency and creativity, but there are risks associated with the loss of the human element and possible job cuts. He also stressed the need for an ethical approach to implementing AI in creative processes to maintain a balance between technology and human creativity.

Moreover, whereas AI tools facilitate idea generation, they frequently overlook the intricate emotional and contextual elements that are fundamental to human creativity. Wang and Hu’s framework for creativity support tools also acknowledges this constraint by highlighting the necessity of human control in the creative process.¹⁰ They contend that although creativity tools might facilitate idea production, the fundamental creative decisions must be human-driven to maintain originality and emotional depth. Despite the expanding volume of research on AI in graphic design, substantial gaps persist, especially concerning the practical incorporation of AI tools into designers’ everyday workflows and the ethical and legal dilemmas that arise from such integration. Despite extensive research on the technological capabilities of AI, there is a paucity of understanding on the interaction of these tools with the creative process in practical design settings. Moreover, scholars have not comprehensively investigated matters concerning copyright, authorship, and the influence of AI on the originality of creative works. This paper examines the collaboration between designers and AI in creative processes, emphasising the practical, ethical, and legal aspects of these relationships.

The main aim of the study is to explore the impact of AI on graphic design practices, analysing alterations in methodologies, tools, and processes associated with visual content creation. The study aims to explore the obstacles and constraints associated with AI implementation in visual design, specifically addressing the technological, ethical, and legal issues encountered by designers. This research examines prospective advancements in AI, evaluating its influence on the creation of new professions and novel methods of creativity. This study utilises a systematic review methodology to synthesise existing material, providing a thorough overview of the issue and identifying areas for additional research and practical application.

Materials and Methods

This study employs a systematic review methodology to assess the influence of artificial intelligence (AI) technologies on graphic design, incorporating evidence from scientific literature and prominent software applications used in modern design practice. The assessment involved a thorough examination of AI solutions that substantially impact the design process, focusing on automation, picture generation, process optimisation, and adaptable content. The review specifically assesses prominent AI tools, including Adobe Sensei, DALL-E, Runway ML, and Autodesk’s Generative Design tool, each of which enhances different facets of the design process, such as automatic colour correction, image retouching, creating unique images from textual descriptions, and optimising graphic object structures.¹¹ The study examines platforms that incorporate AI for generating diverse compositions and adaptive content, including Artbreeder, a neural network-driven image generation tool, and Canva, which offers automated solutions for modifying graphic content across various formats. Mobile applications and neural models facilitating interactive visual design were also investigated. The selection of these tools was influenced by their prevalent application in professional design practices and their embodiment of significant trends in the incorporation of AI into the discipline.

1.  A comprehensive and transparent search strategy was employed to guarantee methodological rigour. Scientific papers were obtained from esteemed international scientometric databases, such as Scopus, Web of Science, Google Scholar, and IEEE Xplore. These databases were chosen for their esteemed scientific reputation, extensive coverage of AI and design-related research, and the accessibility of peer-reviewed articles. Google Scholar lacks advanced filtering and indexing functionalities compared to databases such as Scopus or Web of Science, resulting in a higher likelihood of retrieving diverse sources, including grey literature and non-peer-reviewed materials. To mitigate this constraint, strict inclusion criteria were implemented, resulting in the manual exclusion of non-peer-reviewed materials, including preprints, theses, and reports. In addition, results from Google Scholar were cross-checked with more precise databases to confirm the relevance and quality of the chosen studies. The inquiry was performed on 16 February 2025, utilising the subsequent search queries:

2.  Scopus (February 16, 2025, at 10:00 AM UTC): TITLE-ABS-KEY(“artificial intelligence” AND “graphic design”) OR TITLE-ABS-KEY(“generative AI” AND “visual arts”) OR TITLE-ABS-KEY(“machine learning” AND “graphic content”) OR TITLE-ABS-KEY(“AI-driven image editing”) OR TITLE-ABS-KEY(“neural networks” AND “design tools”) OR TITLE-ABS-KEY(“automation” AND “digital design”) OR TITLE-ABS-KEY(“AI-assisted creativity”) OR TITLE-ABS-KEY(“computer vision” AND “design”);

3.  Web of Science (February 16, 2025, at 10:30 AM UTC): TS=(“artificial intelligence” AND “graphic design”) OR TS=(“generative AI” AND “visual arts”) OR TS=(“AI-driven image editing” AND “machine learning”) OR TS=(“neural networks” AND “graphic content”) OR TS=(“AI tools” AND “design”) OR TS=(“digital design” AND “automation”) OR TS=(“AI-assisted creativity” AND “artificial intelligence”) OR TS=(“computer vision” AND “visual design”);

4.  IEEE Xplore (February 16, 2025, at 11:00 AM UTC): “artificial intelligence” AND “graphic design” OR “machine learning” AND “image editing” OR “AI-driven design tools” OR “neural networks” AND “graphic content” OR “generative AI” AND “visual arts” OR “automation” AND “digital design” OR “AI creativity” AND “design” OR “computer vision” AND “design”;

5.  Google Scholar (February 16, 2025, at 11:30 AM UTC): “artificial intelligence” AND “graphic design” OR “generative AI” AND “visual arts” OR “AI-driven image editing” OR “machine learning” AND “graphic content” OR “AI tools” AND “design” OR “neural networks” AND “design” OR “automation” AND “digital design” OR “AI-assisted creativity” AND “design” OR “computer vision” AND “design”.

Studies were chosen according to these inclusion criteria:

• peer-reviewed articles, conference papers, and book chapters published from 2022 to 2025;
• studies focused on AI tools in graphic design, including AI-assisted workflows, creative design processes, and generative AI tools;
• studies that directly address AI in the context of graphic design, such as AI tools used for image editing, content generation, and design automation;
• studies that involve empirical research, including surveys, experiments, or case studies, or theoretical analyses relevant to AI in design.

Publications were omitted if they:

• were published before 2022;
• focused on AI applications outside the field of graphic design;
• were non-peer-reviewed or sources that are not widely recognised (e.g., blogs, white papers).

The final review included 31 studies, selected according to stringent inclusion criteria (Figure: 1). The research was derived from esteemed databases and concentrated on the application of AI tools in graphic design, particularly investigating AI-assisted workflows, generative design methodologies, and image editing techniques. No grey literature was included in the review. Each study was meticulously evaluated for its pertinence to the research question, methodological rigour, and empirical substantiation. The study design was evaluated to ascertain its suitability for addressing the research issue. The methodology of each study was examined. This entailed evaluating the clarity of the methodology’s description and identifying any potential biases in data collection, analysis, or reporting. The data validity criterion assessed the reliability of the data collecting and analysis procedures, as well as the presence of concerns like selective reporting or incomplete data that could influence the study’s outcomes. A significant factor was its relation to graphic design. The ethical considerations were reviewed to confirm that ethical norms and approval processes were sufficiently addressed.

Screening was performed in duplicate, with two independent reviewers evaluating each of the 53 records according to the established inclusion and exclusion criteria. Discrepancies or conflicts among reviewers were handled through discussion and consensus or by consulting a third reviewer as necessary. A standardised coding framework was employed to classify studies, ensuring uniform data extraction throughout all studies. Inter-rater reliability was assessed using a Cohen’s Kappa coefficient of 0.85, signifying nearly perfect agreement among the reviewers. The papers were systematically coded and classified into principal themes according to the study topics. The coding methodology concentrated on various significant areas. The AI tool type was initially identified, classifying the tools into categories such as generative design tools, image editing tools, and automated design systems. The application domain was delineated, emphasising the utilisation of these AI capabilities in graphic design, encompassing creative workflows, content development, picture enhancement, and interactive design.

The following evaluation examined the influence of design, emphasising the role of AI tools in promoting creativity, automating workflows, and improving efficiency. The review also addressed the obstacles identified in the studies, including technical constraints, ethical concerns, and issues pertaining to user adoption of AI tools. The projected future directions from the studies were examined to discern prospective trends or enhancements in AI tools for visual design. A qualitative synthesis was performed for the thematic analysis, categorising analogous findings and deriving conclusions regarding the prevailing tendencies. Quantitative analysis was conducted on studies incorporating empirical data to assess the impact of AI tools, emphasising variables such as tool efficacy and user happiness. This integrated methodology offered an extensive understanding of the functions of AI tools in visual design.

The data obtained from this research were methodically classified into four primary themes: automation in design workflows, picture production, process optimisation, and adaptive design in visual content. A comparative study was conducted to evaluate the benefits and drawbacks of various AI technologies and to discern trends within the studies. A content analysis methodology was employed to assess the predominant themes arising from the articles, concentrating on the evolution of generative models, the influence of automation on creativity, and the incorporation of AI into interactive content design. This systematic review adheres to PRISMA principles, ensuring transparency and replicability in the review process (Appendix 1).¹²

The current study was reported in line with the PRISMA criteria.¹³ The PRISMA flowchart depicts the study selection process, enumerating the records detected, screened, included, and excluded, as well as the rationale for exclusion at each phase. The quality of the studies in the review was evaluated using risk of bias assessment, which examined each study’s methodological rigour, sample size, and relevance to the research topics. A protocol for this review was not formally registered, but all search and selection procedures followed predefined criteria to ensure reproducibility and minimise bias. The level of compliance with AMSTAR is moderately high.

The reliability of evidence in this analysis was evaluated by a thorough strategy that accounts for both methodological rigour and bias risk among the included studies. Each study underwent critical appraisal utilising the Joanna Briggs Institute (JBI) risk-of-bias (RoB) tool, facilitating the evaluation of evidence quality according to established criteria (Appendix 2). The criteria encompassed elements such as the clarity of the research question, the suitability of the study design, data collection techniques, and the management of confounding variables. A standardised checklist was utilised across all study types (empirical, conceptual, and literature reviews) to guarantee a consistent and transparent evaluation of evidence quality. The reliability of evidence was evaluated by combining quantitative and qualitative data. In empirical investigations, focus was directed towards sample size, methodological rigour, and data transparency. Conversely, in conceptual and qualitative studies, the clarity of the conceptual framework, coherence of findings, and depth of analysis were prioritised. Research with little bias resulted in a greater certainty rating, whereas studies with moderate bias, attributable to small sample numbers or incomplete reporting, were assigned a moderate certainty rating.

The JBI critical evaluation results classify the certainty of evidence for this review as moderate. This indicates the overall quality of the research used in the synthesis, with several studies exhibiting shortcomings regarding sample size, methodological rigour, or clarity in reporting. These constraints may affect the generalisability of the conclusions. Nevertheless, they do not substantially compromise the internal validity of the evidence. Moreover, whereas the empirical investigations yielded substantial findings, the conceptual studies exhibited greater variability, indicative of divergent theoretical frameworks and analytical depth. To enhance the evaluation of certainty, the uniformity of the findings across research was also taken into account. The consistent results from both empirical and qualitative research bolstered trust in the conclusions. However, considerable heterogeneity in the conceptual studies necessitated a moderate level of certainty.

Results

Transformation of Graphic Design Processes under the Influence of AI

This systematic review analysed 31 works to assess their relevance and value to understanding the integration of artificial intelligence (AI) techniques in visual design. Appendix 3 delineates the details of each study, including its research type, subject matter, methodology, and a risk of bias assessment. A meticulous evaluation procedure was implemented to identify studies relevant to the research inquiry. The following PRISMA table offers a comprehensive summary of the studies omitted at different phases of the review process, along with the justifications for their exclusion (Appendix 4). These studies were not incorporated into the synthesis and were utilised solely as background research.^2,14–34 This transparent procedure ensures the reliability and validity of the findings by documenting the selection process and criteria employed. The integration of AI into graphic design significantly transforms traditional processes, especially by automating routine tasks. This includes concept generation, automatic editing, and workflow optimisation, resulting in increased efficiency and reduced time spent. One of the key aspects of AI implementation is automating tasks such as image processing, retouching, and colour correction. Traditionally, these processes required significant time and labour resources from designers.³⁵

Contemporary AI-driven solutions, exemplified by Adobe Sensei, demonstrate significant capability in automating picture analysis and adjustments, enabling designers to concentrate on the more creative dimensions of their tasks. Research contrasting pre- and post-implementation mistake rates has revealed a notable decrease in design faults, with AI-driven tools enhancing image quality and processing speed. Many designers reported enhanced efficiency and fewer errors when using tools such as Adobe Sensei, particularly in repetitive editing tasks.³⁶ Nonetheless, although these technologies provide substantial enhancements in workflow, they do not obviate the necessity for meticulous verification. Designers must stay alert, as AI systems can still generate errors, occasionally systematic, especially in intricate or subtle design situations.¹⁵

Concept generation is another area where AI has a significant impact. Tools like OpenAI’s DALL-E are capable of creating unique images based on text descriptions, opening up new horizons for designers to find inspiration and develop new ideas. This is especially useful in the initial stages of design, when a variety of options need to be quickly generated for further development. Automated editing has also undergone significant improvements due to AI. Tools such as Runway ML provide the ability to automatically apply stylistic changes to images or videos using pre-trained models. This allows designers to quickly adapt content to different stylistic requirements without the need for manual intervention in each element. Optimisation of workflows using AI manifests itself in reducing the time and cost of performing design tasks.^16–18 This is achieved by automating repetitive processes and quickly analysing large amounts of data, which previously required significant human resources. For example, tools like TimeHero allow automatic scheduling and tracking of tasks by creating timesheets that compare actual and estimated time, which helps to improve team productivity and performance.

The use of AI in creating variable compositions allows designers to generate many unique design options based on specified parameters.¹⁹ This is especially useful when designing logos, posters, or other visual elements, where it is important to have a wide range of ideas to choose the best solution. For example, the Artbreeder platform uses Generative Adversarial Nets (GAN) to create new images by mixing and modifying existing ones, giving designers the opportunity to experiment with different styles and shapes. Image stylisation using AI allows automatically applying artistic effects to photos or illustrations, transforming them into the styles of famous artists or creating new visual effects.²⁰ Algorithms such as neural style transfer analyse the style of one image and apply it to another while preserving the content of the original. Augmented reality (AR) and interactive design have also undergone significant changes due to the introduction of AI. AI-based tools can automatically recognise objects and spatial relationships, helping to create more realistic and interactive AR applications.¹⁰

Collaboration between humans and AI in generative design leads to hybrid approaches, where AI algorithms generate design solutions based on given parameters, and the designer acts as a curator, selecting and improving the best ones. This approach allows combining the computing power of AI with a creative human vision, which leads to the creation of innovative and effective design solutions. For example, Autodesk has developed the Generative Design tool, which generates a variety of design options based on input data, optimised for various parameters such as weight, strength, or production cost.¹¹ The designer can choose the most suitable options and adapt them to their needs, which significantly speeds up the development process and increases its efficiency.

The influence of AI on the style and personality of authors’ works is a complex and multifaceted issue. On the one hand, the use of AI algorithms allows designers to experiment with new styles and techniques, expanding their creative arsenal. On the other hand, there is a risk of unifying styles and losing uniqueness, as many designers may use the same algorithms and tools. To maintain individuality, it is important that designers not only rely on the results generated by AI but also actively participate in the process, adding their own vision and creativity. Thus, AI can serve as a tool for expanding creative possibilities, but the final result depends on the skill and imagination of the designer.

Mass production of visual content using generative models of AI has become a key element of modern commercial strategies in the fields of social networks, advertising, and marketing. This approach allows creating high-quality visual materials with minimal time and resources, which is crucial in a fast-paced digital environment. Generative AI is a technology that automatically creates a variety of content, such as texts, images, programme code, video, and audio, based on pre-trained information in accordance with user instructions. One of the most famous examples is ChatGPT, released by OpenAI in November 2022. In the context of creating visual content for commercial purposes, generative AI models such as OpenAI’s DALL-E can create original images based on text descriptions. This opens up new opportunities for brands to develop unique visual content, allowing marketers to generate product images in different styles or contexts, adapting them to specific audiences or advertising campaigns. This is especially useful for social networks, where constant content updates are necessary to maintain audience engagement.

Different AI tools show different performance depending on the tasks in graphic design, which requires comparing them based on key parameters. Adobe Sensei is one of the most accurate in the field of automatic image enhancement, but it does not perform creative functions. DALL-E is distinguished by the ability to create concepts from text descriptions but has limitations in the detail of complex scenes. Runway ML is effective in styling images and videos but may be inferior in processing speed due to high computational requirements. TimeHero significantly enhances organisational processes within design teams. However, it does not influence the generation of visual content itself. Autodesk’s Generative Design centres on optimising the parameters of physical objects, making it beneficial for engineering design but less applicable to artistic endeavours. Adobe Firefly and Canva enable users to create designs quickly without requiring extensive skills, which renders them convenient for widespread use, though this convenience restricts the potential for professional adaptation. ARKit opens up prospects in the field of augmented reality, although its application requires specialised knowledge. Thus, the choice of tool is determined by needs: Adobe Sensei and Runway ML are optimal for automating routine processes, DALL-E and Generative Design are optimal for generating concepts, and ARKit is optimal for augmented reality.

This systematic review examined 31 papers, uncovering significant themes like routine task automation, creative concept generation, and design process optimisation. The results demonstrate a notable transformation in design processes as AI tools automate mundane chores and facilitate creative discovery. AI solutions such as Adobe Sensei are often referenced for automating activities including picture processing, retouching, and colour correction.^1,3 These tools markedly enhance efficiency by enabling designers to concentrate on the more creative facets of design, hence augmenting output and minimising human error. Moreover, AI tools such as TimeHero have been acknowledged for enhancing design team workflows through the automation of task scheduling and performance monitoring.⁴ Generative design systems, such as DALL-E and Runway ML, are noted for their ability to generate novel pictures from simple textual cues.^5,6 These tools are especially advantageous at the first stages of design, enabling designers to rapidly and efficiently produce numerous design alternatives, hence augmenting creative flexibility and exploration. Numerous studies highlight that AI’s capacity to produce conceptual designs renders it an effective instrument for swift prototyping in creative domains.^7,8

Generative AI solutions such as Stable Diffusion and Midjourney are transforming graphic design processes by allowing designers to create intricate visual material using textual cues.⁶ These AI models facilitate swift iteration, broadening creative potential without necessitating conventional design expertise. The use of these tools transforms the creative process, allowing designers to concentrate on conceptualisation while delegating monotonous or repetitive duties to AI. Nonetheless, the implementation of these technologies poses hurdles, especially concerning intellectual property issues and the authorship of AI-generated material. Designers must traverse these complications, reconciling the advantages of AI automation with the necessity for originality and creative proprietorship. The role of AI in improving design processes is apparent in various domains, such as the automation of design iterations and concept discovery. For example, generative design systems, such as Autodesk’s Generative Design, are recognised for their capacity to rapidly provide design options based on defined parameters, including weight, strength, and cost.⁹ This technology enhances the prototyping phase, rendering the design process more efficient and economical, especially in architecture and industrial design.

The potential of AI for picture stylisation has been examined in research using technologies such as Artbreeder and neural style transfer algorithms, which allow designers to alter photos into other artistic styles or create completely novel visual effects.^11,35 This capacity provides substantial creative autonomy and experimentation, allowing designers to investigate various stylistic options. Studies emphasise AI’s importance in AR/VR design, particularly its capacity to generate dynamic and immersive user experiences. AI techniques such as ARKit facilitate object detection and spatial relationship analysis, hence enhancing the development of interactive augmented reality applications that adjust in real time to user interactions.^37,38 Nonetheless, AI’s ability to comprehend the emotional and cultural nuances of a user’s experience is constrained, which is a crucial factor in the development of emotionally immersive AR/VR environments.^39,40

Ethnographic studies of designer work habits offer critical insights into the adoption and integration of AI inside creative processes by designers.^39,41 These studies underscore the synergistic interaction between designers and AI technologies, demonstrating how AI impacts decision-making, ideation, and problem-solving in design processes. Designers frequently encounter conflict between human creativity and AI-generated recommendations when adapting to the changing function of AI as a creative collaborator rather than simply a tool. Comprehending these behaviours is essential for discerning the dynamics of human-AI collaboration, which are transforming the execution of design work and the interactions between design professionals and AI technology. The incorporation of AI in design has resulted in the creation of new career positions that connect creativity with technology. These encompass AI Trainers, Data Curators, and Generative Design Specialists, roles that are gaining significance in the design sector.^42,43 As AI tools advance, there is an increasing demand for designers proficient in both creative and technical fields, highlighting the expanding convergence of design and AI technology.^36,44

Although AI is being utilised for producing design solutions, apprehensions exist about the homogeneity of AI-generated designs. Numerous studies have highlighted concerns about the superficiality, emotional detachment, and lack of originality in AI-generated works, since these technologies often reproduce prevailing patterns instead of promoting creativity.^45,46 Moreover, AI-generated content persists in presenting legal dilemmas, especially with authorship and copyright issues. Numerous studies emphasise the intricacies involved in establishing ownership of AI-generated works, as disparate legal frameworks across jurisdictions engender ambiguity.^47,48 AI tools have been extensively utilised for branding and logo design. Applications such as Adobe Firefly enable designers to swiftly create logos and branding components, resulting in considerable time efficiency in commercial design endeavours.^41,49 Nonetheless, the capacity of AI tools to produce genuinely distinctive designs that embody a brand’s identity is constrained, as these techniques frequently provide more generic or templated outcomes.^50,51 Generative AI has been seen as especially advantageous in architecture and industrial design, where it optimises design factors like cost, weight, and strength. AI tools such as Autodesk’s Generative Design may autonomously generate design alternatives that adhere to established criteria.⁵² This method markedly expedites the design process, facilitating quick iteration of design alternatives that would otherwise necessitate considerable time and effort to produce manually.

The incorporation of AI into the design process has enhanced overall design efficiency. AI solutions such as Runway ML and Adobe Sensei have demonstrated the ability to improve task automation, allowing designers to concentrate on more advanced creative endeavours. These tools enhance both the efficiency of design development and the overall quality of the design output.^53,54 Although AI technologies such as Stable Diffusion, Midjourney, and DALL-E democratise design, they also impose considerable access barriers due to elevated licence prices and membership fees. Smaller design studios, individual designers, and professionals from economically disadvantaged locations may have financial obstacles in obtaining these advanced technologies, constraining their competitiveness in the global market. Moreover, although several tools provide free versions or restricted capabilities, they frequently necessitate costly premium versions to access additional functions. The commercialisation of AI tools intensifies the disparity between well-funded entities and individual or small-scale designers.⁹

Regional inequities in access to AI technologies persist as a significant concern. In areas with restricted access to high-speed internet, sophisticated technology, or financial means, designers are less inclined to utilise or fully capitalise on AI solutions. This establishes an inequitable environment wherein designers from affluent nations prevail in the AI-assisted design domain, while those from economically disadvantaged areas are left at a disadvantage. As AI technologies progress, it is essential to guarantee that designers in low-income and distant regions have the possibility to utilise these tools for their creative and professional advancement.³⁹ Generative AI models are frequently trained using extensive datasets obtained from the internet, which may automatically exhibit the biases and cultural uniformity found in these datasets. AI-generated designs may prioritise Western aesthetic standards, thus omitting non-Western cultural nuances and constraining the diversity and representation of global design approaches. This prejudice influences both the creative results and cultural inclusion in AI-generated content. To address this, it is imperative to assemble diverse and inclusive datasets that represent a wider array of cultures, ethnicities, and design traditions.³⁷

The use of AI in automating design processes prompts apprehensions over labour displacement within the creative sectors. Although AI can markedly enhance efficiency and diminish repetitive work, it may also result in job displacement for entry-level designers or individuals engaged in routine activities such as image retouching and editing. Furthermore, the growing utilisation of AI technologies may compel designers to assume positions necessitating greater technical proficiency (e.g., AI trainer, data curator), so generating new job categories while simultaneously posing the risk of labour instability for designers lacking the necessary technical abilities. This transition may further marginalise non-technical designers, particularly in areas where access to specialised education in AI tools is restricted.⁵⁴

The environmental ramifications of training and operating generative AI models constitute a serious concern, as these models necessitate substantial computational resources and hence consume considerable energy. The carbon footprint of AI technologies in graphic design is frequently neglected. To promote sustainable growth, AI developers and users must prioritise energy-efficient models and renewable energy sources for operating these tools, particularly as AI integration expands inside the design sector.⁴⁷ AI tools can revolutionise the accessibility of graphic design for designers with disabilities. AI-driven speech recognition and assistive design features can enable designers with visual impairments or physical disabilities to participate more comprehensively in design processes. Nonetheless, the usability of these technologies is frequently impeded by suboptimal design and insufficient inclusive features in the software. To foster inclusive development, it is essential that AI tools are crafted with accessibility considerations, enabling all designers, irrespective of ability, to engage fully in the creative process.⁴¹

Although AI has remarkable proficiency in automating design chores, there is a broad consensus that technology lacks the emotional intelligence required to produce profoundly meaningful and contextually sensitive designs. Research indicates that although AI technologies may emulate specific design functions, they falter in comprehending the nuances of human emotions and cultural context, which are essential for creating emotionally impactful designs.¹⁴ The introduction of AI in the process of creating visual content raises questions about the quality and effectiveness of such materials. The use of AI allows quickly testing various visual content options, determining the most effective ones based on analytics and behavioural data. This helps to optimise marketing strategies and increase the return on investment in advertising.⁴⁴ It is important to consider that the success of such approaches depends on the quality of training of AI models and their ability to adapt to changes in consumer behaviour.

Discussion

The incorporation of AI in visual design presents substantial legal and ethical dilemmas, particularly with copyright. AI training necessitates extensive datasets, frequently including copyrighted materials utilised without authorisation, prompting legal enquiries. Copyright legislation in numerous nations lacks clarity on AI training, introducing ambiguity.³⁶ In the United States, the “fair use” concept may allow such usage for research or educational purposes; however, its applicability to AI training is disputed.⁴⁵ Authorship is intricate due to AI’s independent content generation, prompting queries regarding copyright ownership. A 2023 U.S. court determined that AI-generated content lacking human participation is not eligible for copyright protection, highlighting the legal significance of human creativity.⁴⁶ Conversely, several regimes, such as China, acknowledge copyright in AI-assisted creations that involve substantial human contribution.⁴⁷ Copyright matters are continually developing: a class-action lawsuit filed in 2023 by artists against AI corporations for the unauthorised utilisation of their creations was dismissed, with the court indicating that AI training may be considered fair use depending on the situation.⁴¹ AI algorithms such as GANs generate art but depend on data, which may inhibit uniqueness and promote uniform design trends.⁴⁸ The discourse on AI as a co-author continues, with research indicating that individuals are more inclined to acknowledge AI as a creator when it is regarded as human-like.⁴⁹

Artificial intelligence revolutionises graphic design, presenting advantages and constraints across various fields.⁴¹ It automates monotonous UI/UX chores like prototyping and testing, hence accelerating workflows, but it lacks the intuition and emotional comprehension essential for human-centred design. Instruments like Adobe Sensei and Figma automate repetitive tasks yet necessitate human intervention to guarantee emotional and cognitive alignment. Branding advantages arise from AI’s swift generation of logos and slogans utilising demographic data, fulfilling market speed requirements. Nonetheless, AI frequently generates formulaic outcomes devoid of the nuance and emotional depth essential for effective branding, which may jeopardise company identification and cultural significance, as examined in global logo design.⁵⁰

Generative image models, such as Stable Diffusion and Midjourney, have transformed graphic design by enabling people to produce intricate and original visual material using straightforward text cues. Nonetheless, a principal problem with these technologies is prejudice and equity. These models are trained on extensive datasets that frequently encompass societal biases intrinsic to the data from which they derive knowledge. The representations produced by these AI models can reinforce preconceptions, marginalise specific groups, and omit diverse viewpoints. AI-generated designs may exhibit gender, racial, or cultural biases, resulting in content that is primarily Western-centric or conforming to specific beauty standards, thus compromising the inclusivity and diversity anticipated in design practice.

Mitigating bias and guaranteeing equity in generative image models is a persistent challenge.²² Researchers and developers are striving to alleviate these biases by assembling more inclusive datasets, enhancing algorithmic transparency, and integrating fairness-aware algorithms into the design and training of AI models.^5,35,37 The objective is to guarantee that AI technologies are both innovative and effective, as well as socially responsible. In this context, fairness denotes that AI-generated material must encompass a diverse array of cultural identities, refrain from perpetuating detrimental stereotypes, and ensure equitable representation for all groups. As AI increasingly influences the creative sector, it is imperative for designers and developers to remain cognisant of these concerns and strive to construct AI systems that encourage ethical design principles and support inclusive creativity.

The ability of AI to produce graphics and stylise material facilitates the swift creation of variations, hence conserving time.^23,24 However, AI-generated images frequently lack uniqueness, profundity, and emotional impact, which are essential to human artistry. While platforms such as Artbreeder can generate innovative ideas, they can constrain the emotional resonance and distinctiveness of the creator’s style. Table 1 illustrates the benefits and limitations of AI within design disciplines. Artificial intelligence expedites design processes, automates monotonous jobs, and facilitates swift visual production. Nonetheless, constraints such as diminished emotional resonance in branding and standardised artistic forms highlight the persistent necessity for human creativity. Artificial intelligence is also transforming the graphic design labour market.²⁵ Automation diminishes monotonous tasks while generating positions such as AI Trainers and Data Curators, responsible for instructing algorithms with high-quality data.³⁶ These jobs emphasise that AI enhances rather than supplants designers by integrating technical and artistic competencies.

Table 1: Opportunities and limitations of using AI in various design areas.
Field of Design	AI Application	Advantages	Restrictions
UI/UX design	Automation of routine tasks, creation of prototypes, testing of interfaces, and optimisation of the location of elements	Accelerated development, simplified testing, and design optimisation	Inability to consider deep user experience and emotions, limited intuition in designing interaction
Branding	Generation of logos, slogans, and analysis of brand and target audience data	Quick creation of basic versions of logos and brand elements	Lack of sense of brand uniqueness and the possibility of template solutions
Illustration	Creation of images by parameters, stylisation, and automated addition of details	Scalability, fast creation of image variants	Lack of personal style, depth, and emotional expressiveness
Source: Sudarmanto (2025)28 and Wang et al. (2025).29

Collaboration between artificial intelligence and humans cultivates innovative solutions.^26,27 In generative design, AI rapidly determines ideal configurations in architecture and industrial design, where expediency is essential. UI/UX techniques such as Sketch-RNN facilitate generative exploration yet necessitate human supervision to maintain emotional and intuitive integrity. While AI automates numerous web design activities, human intuition is essential for comprehending user behaviour, emotions, and cognition in UX, guaranteeing that creativity yields valuable and engaging results. AI revolutionises graphic design by improving efficiency.^28,29 However, it needs human involvement owing to its constraints in emotion, culture, and creativity. As artificial intelligence advances, novel positions for designers will arise, embodying the interaction between technology and creativity.

In branding, AI technologies such as Wix Logo Maker democratise logo development for small enterprises, yet often yield identical designs devoid of originality. Human creativity is vital for a profound connection with company culture. The progression of AI requires ethical guidelines to avert a “filtered aesthetic” that constrains diversity and promotes clichés. Unregulated AI training on extensive datasets threatens to homogenise designs, undermining cultural significance.³⁰ Ethical frameworks are essential to guarantee that AI augments rather than supplants human innovation. The use of AI in graphic design is continually evolving, presenting new opportunities for customisation and creativity. Reconciling the advantages of AI automation with human creativity and emotional intelligence is essential for the future of design.²⁹ Table 2 outlines the principal applications of each AI tool in diverse design domains, clarifying their respective advantages and limits. It evaluates their capabilities in task automation and creative content generation while also emphasising the limitations that designers may face when relying on AI in their processes.

Table 2: Capabilities and limitations of leading AI tools in graphic design.
AI Tool	Field of Design	Advantages	Limitations
Adobe Sensei	Image Processing, UI/UX Design	Automates image enhancement, retouching, colour correction. Improves quality, minimises human error	Lacks creative functions. Cannot produce unique designs or concepts
DALL-E	Image Generation, Branding	Creates concepts from text descriptions, generates a wide variety of images	Limited detail in complex scenes, unable to produce entirely original content without human input
Runway ML	Video, Image Editing, UX Design	Automatic stylistic changes. Facilitates quick adaptations to various styles	High computational requirements, can be slower compared to other tools in terms of processing speed
Autodesk Generative Design	Industrial Design, Architecture	Generates design options based on parameters such as strength, weight, and production costs	More suitable for engineering design. Limited creative flexibility in artistic works
Artbreeder	Illustration, Concept Design	Generates varied design options through blending existing images using GAN	Lack of personal style, limited emotional expressiveness in designs
Canva	Branding, Marketing Design	Quick, accessible design creation without requiring deep design skills	Limited customisation, doesn’t allow for professional adaptation or high-end creative flexibility
TimeHero	Project Management, Workflow Optimisation	Automates project scheduling, task management, and time-tracking	Does not influence the design content itself. Focused on organisational rather than creative tasks
ARKit	Augmented Reality (AR)	Facilitates AI-driven AR design, real-time object recognition and interaction	Requires specialised knowledge for application, focused mainly on AR rather than traditional design

Table 2 illustrates that although AI technologies provide considerable advantages in efficiency and automation, their shortcomings, especially in domains necessitating profound creativity and emotional depth, highlight the enduring need for human participation in the design process. These findings underscore the necessity for a balanced methodology, wherein AI functions as an auxiliary instrument rather than a substitute for the designer’s creativity and intuition. An essential aspect of AI integration in graphic design is its disparate societal effects, influenced by factors such as cost, locality, accessibility, and environmental concerns. Numerous empirical studies indicate that the cost of AI tools limits their adoption. Hu et al.8 discovered that numerous designers are reluctant to invest in premium AI services because of ongoing licensing costs, whereas Mustafa⁹ noted analogous apprehensions among tiny enterprises that do not possess the resources of larger studios. The findings indicate that financial accessibility is a critical determinant of who gains from AI-enhanced design.

Geographical differences aggravate these imbalances. Sudarmanto,⁴¹ in the investigation of Indonesian designers, emphasised that bandwidth constraints and infrastructural deficiencies impede the adoption of generative tools, in contrast to environments equipped with high-speed internet and advanced technology. Escudero Fernández,³⁹ in collaboration with Spanish students, observed that local access to AI laboratories and institutional backing affected adoption rates, highlighting disparities even across comparatively affluent locations. Accessibility obstacles represent a significant concern. Jiang⁴ demonstrated that AI systems frequently lack sufficient features for inclusive use, hindering acceptance by designers with little technical proficiency, whereas Escudero Fernández³⁹ reported challenges in educational environments where interfaces were not tailored for learners with varied requirements. This prompts critical enquiries regarding the extent to which contemporary AI-driven design tools are developed with universal usability considerations.

Although direct empirical research on environmental impact is scarce, various conceptual studies indicate the energy-intensive characteristics of generative AI models.^10,38 These issues correspond with secondary evaluations of AI carbon footprints, indicating that sustainability should be integrated into the evaluation criteria for innovative technology. Table 3 delineates the principal social-impact dimensions of AI in graphic design, namely access cost, geography, accessibility, and environmental sustainability, correlating them with supporting evidence from the included studies and related secondary datasets, as well as implications for practice and policy. Collectively, these data indicate that although AI has the capacity to democratise creative potential, institutional impediments, financial, geographical, technical, and ecological, persist in influencing its advantages. Resolving these difficulties necessitates both technical enhancement and legislative interventions, including tiered licensing, infrastructural investment, and more robust sustainability criteria.

Table 3: Social impact dimensions of AI in graphic design: Evidence and implications.
Dimension	Evidence from Included Studies	Supporting Data/Secondary Sources	Implications and Recommendations
Access cost and licensing	Hu et al. (2025) and Mustafa (2023)   – both show affordability concerns for premium services and small firms.	Software as a Service pricing models; Adobe Creative Cloud cost benchmarks	Explore tiered licensing, institutional subsidies, or open-source alternatives to reduce exclusion.
Geography / bandwidth	Sudarmanto (2025) – Indonesian designers limited by infrastructure. Escudero Fernández (2024) – regional institutional support influences uptake.	International Telecommunication Union/World Bank broadband penetration data	Targeted infrastructural support and localized tool deployment needed in under-resourced regions.
Accessibility features	Jiang (2024) – limitations for less technically skilled designers. Escudero Fernández (2024) – barriers in student adoption.	World Wide Web Consortium accessibility design standards	Improve interface inclusivity, integrate multilingual and disability-access features.
Environmental impacts	Wang & Hu (2023) and Özdal (2024) – conceptual concerns over energy use and sustainability.	International Energy Agency/academic studies on carbon cost of AI training	Incorporate sustainability metrics in evaluating design AI; promote energy-efficient model training.
Source: Hu et al. (2025);8 Mustafa (2023);9 Sudarmanto (2025);28 Jiang (2024);4 Escudero Fernández (2024);17 Wang & Hu (2023);10 Özdal (2024).16

The incorporation of AI into graphic design has transformed creative methodologies, especially by automating monotonous jobs and augmenting originality.^32,33 AI solutions, including Adobe Sensei and Runway ML, automate image processing activities like retouching, colour correction, and the creation of intricate effects, enabling designers to concentrate more on creative and stylistic development. This shift corresponds with the findings of Wu et al., who observed that AI tools optimise design workflows by automating manual activities, hence boosting productivity in the first phases of design.⁵³ The extensive utilisation of AI-generated designs prompts apprehensions regarding the homogeneity of creative results. Begemann and Hutson illustrate that AI tools in game design may result in the standardisation of stylistic solutions, thereby inhibiting uniqueness.⁵⁴ Consistent with these findings, the present study revealed that numerous AI-generated designs lack depth and emotional resonance, underscoring the significance of human creativity in imparting personal style and meaning to the work.

The research conducted by Fang and Fang emphasised the efficacy of AI in product package design, utilising genetic algorithms to expedite design iterations efficiently.¹⁴ The obtained results corroborate this insight, as AI’s capacity to swiftly create several design alternatives facilitates rapid exploration and decision-making, which is essential to the creative process. The absence of emotional involvement in AI-generated designs remains a barrier, since it restricts AI’s ability to comprehend nuanced cultural and emotional signals essential for good design.³⁴ The role of AI in generative design has been thoroughly examined in studies, especially with its application in ideation and concept generation. Wang et al. emphasised the role of AI in enhancing collaboration between human designers and algorithms, hence enabling a varied array of design alternatives.¹⁵ This study verifies that AI-driven generative design tools, like Autodesk’s Generative Design, improve design flexibility by optimising parameters for weight, strength, or cost. The capacity to provide multiple design choices accelerates the brainstorming process, especially in the initial stages of design.

Liu examined the application of AI in painting creation, illustrating its capability to rapidly produce diverse compositions depending on defined input criteria.16 The present study validates previous findings, demonstrating that tools such as DALL-E and Artbreeder enable designers to rapidly investigate many visual notions, thereby establishing a basis for creative exploration. Nonetheless, the study underscores that although AI expedites idea production, human supervision is essential to guarantee that the produced ideas correspond with the designer’s vision and emotional intent. The recent study reveals that AI enhances the effectiveness of design processes while simultaneously generating new positions within the sector. Vaithilingam et al. found novel prospects for AI integration specialists in programming and system design.¹⁷ The current analysis aligns with this, since AI tools increasingly necessitate personnel proficient in both creative and technical fields, such as AI Trainers and Data Curators, to oversee the application of AI technology in design workflows.

Alongside automation, the incorporation of AI with augmented and virtual reality (AR/VR) environments is commencing to generate more tailored and dynamic user experiences. Mohamed examined the application of AI in recreating virtual settings, demonstrating its capacity to augment the development of immersive environments.¹⁸ The current study revealed that AI significantly enhances the personalisation of AR and VR interfaces by dynamically adjusting designs according to user input. Nonetheless, although AI can enhance technical elements, its comprehension of human emotions and context is still constrained, as emphasised by Agkathidis et al.¹⁹ This indicates that, despite AI’s ability to produce interactive and adaptable environments, human intuition and emotional intelligence remain essential for crafting compelling and contextually nuanced designs.

As highlighted by this study’s findings and corroborated by the legal analysis of Aronov and Idrysheva, AI-generated work presents substantial issues concerning copyright and authorship.46 Using AI to produce content from extensive datasets, including copyrighted works, has sparked discussions about intellectual property rights. Although certain jurisdictions, including China, have begun to acknowledge the potential for giving copyright to AI-generated works with significant human contribution, the overall legal framework remains ambiguous. This ambiguity has prompted demands for revised legislation to tackle these emerging concerns as AI technology advances.

This systematic analysis underscores the revolutionary potential of AI in graphic design, stressing the necessity for designers to adjust to new workflows that integrate automation and generative tools. Designers ought to adopt AI tools for enhanced efficiency while retaining creative control to safeguard the distinctiveness and emotional profundity of their work. Policy implications necessitate the establishment of more explicit copyright legislation to regulate AI-generated content and safeguard intellectual property rights. The review’s limitations encompass the restricted range of research considered and the absence of a comprehensive analysis of AI’s long-term effects on employment in the design sector. Future research should investigate the dynamic interplay between AI and human creativity, emphasising ethical frameworks, cultural biases, and the creation of inclusive AI tools.

Conclusions

This study examined the incorporation of artificial intelligence (AI) in graphic design, emphasising its influence on creative processes, automation, and the overall ramifications for the business. The findings underscore substantial changes in conventional techniques for producing visual material as a result of AI. The automation of regular tasks, concept development, workflow optimisation, and the application of neural network techniques have significantly enhanced designers’ capacities. These advances not only expedite task completion but also furnish designers with novel instruments for creative exploration.

The initial research objective, comprehending the impact of AI on graphic design methodologies, revealed that a principal use of AI in design is the automation of image processing. Instruments like Adobe Sensei enable rapid and precise image editing, minimising human mistake and enhancing the quality of the final product. The utilisation of text-based queries for image generation has created new opportunities for concept development, especially during the first phases of a project, where diverse design alternatives are essential. The research indicated that the calibre of produced photos is contingent upon the intricacy of the algorithms and the accuracy of input parameters, underscoring the significance of adept query formulation by designers. The second research objective, analysing the constraints and obstacles of integrating AI in graphic design, demonstrated that although AI automation markedly enhances efficiency, issues such as the harmonisation of design styles persist. The research revealed the peril of standardisation, as numerous designers employ identical methods, resulting in a uniformity of visual outcomes. This matter highlights apprehensions over the erosion of individuality in artistic creations and the possible suppression of emerging style movements.

The study identified substantial deficiencies in the legal framework regarding copyright issues associated with AI-generated content, particularly in the context of the ethical and legal dimensions of AI in design, which is a primary research focus. The absence of explicit restrictions concerning ownership and intellectual property rights for AI-generated works creates legal ambiguity. The findings demonstrate that whereas certain countries have progressed in establishing the legal status of AI-generated works, a consistent approach is lacking across jurisdictions. This underscores the necessity for additional research into the legal ramifications of AI in design, as well as the establishment of a more coherent legislative framework to handle these issues.

The study revealed that although AI eliminates numerous regular jobs in the design labour market, new job categories are emerging, including positions in generative design and AI integration. This transition highlights the necessity for designers to adjust and consistently enhance their competencies, especially in domains that require collaboration with AI tools. Research reveals that designers who integrate creative expertise with technical AI understanding will be more favourably positioned in the shifting work environment. The research additionally investigated the incorporation of AI into workflows and determined that technologies such as Runway ML and TimeHero enhance productivity by automating processes such as content adaptation, project management, and scheduling. These tools not only conserve time but also enable designers to concentrate on more strategic and creative dimensions of design. Nevertheless, despite the benefits, it is essential for designers to retain oversight and creative authority over the final product to safeguard the distinctiveness and emotional resonance of their creations.

It is advised that designers utilise AI solutions such as DALL-E, Runway ML, and Autodesk’s Generative Design to streamline workflows and enhance creativity while preserving their own artistic vision. Educators ought to incorporate AI-focused curricula into design programmes to prepare future professionals with the requisite skills for productive collaboration with AI technologies. It is essential for policymakers to create explicit frameworks that consider the ethical and legal ramifications of AI-generated material, ensuring that intellectual property rights are modified to suit these emerging technologies. This study is limited in breadth, notably with the practical deployment of AI tools in design studios. Future study ought to examine the particular issues encountered by designers across diverse industries, evaluate the enduring impacts of AI on design processes, and further scrutinise the developing legal frameworks pertaining to AI-generated material. Furthermore, investigating the function of AI in interdisciplinary collaborations may unveil new opportunities for augmenting creative processes.

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Appendix

Appendix 1: PRISMA 2020 checklist.
Section and Topic	Item #	Checklist item	Location where item is reported
TITLE
Title	1	Identify the report as a systematic review.	p. 1
INTRODUCTION
Rationale	3	Describe the rationale for the review in the context of existing knowledge.	p. 1–3
Objectives	4	Provide an explicit statement of the objective(s) or question(s) the review addresses.	p. 3
METHODS
Eligibility criteria	5	Specify the inclusion and exclusion criteria for the review and how studies were grouped for the syntheses.	p. 3–4
Information sources	6	Specify all databases, registers, websites, organisations, reference lists and other sources searched or consulted to identify studies. Specify the date when each source was last searched or consulted.	p. 3
Search strategy	7	Present the full search strategies for all databases, registers and websites, including any filters and limits used.	p. 3
Selection process	8	Specify the methods used to decide whether a study met the inclusion criteria of the review, including how many reviewers screened each record and each report retrieved, whether they worked independently, and if applicable, details of automation tools used in the process.	p. 3–4
Data collection process	9	Specify the methods used to collect data from reports, including how many reviewers collected data from each report, whether they worked independently, any processes for obtaining or confirming data from study investigators, and if applicable, details of automation tools used in the process.	p. 3–4
Data items	10	List and define all outcomes for which data were sought.	p. 3–4
Study risk of bias assessment	11	Specify the methods used to assess risk of bias in the included studies, including details of the tool(s) used, how many reviewers assessed each study and whether they worked independently, and if applicable, details of automation tools used in the process.	p. 6
Effect measures	12	Specify for each outcome the effect measure(s) (e.g. risk ratio, mean difference) used in the synthesis or presentation of results.	p. 4–6
Synthesis methods	13a	Describe the processes used to decide which studies were eligible for each synthesis (e.g. tabulating the study intervention characteristics and comparing against the planned groups for each synthesis (item #5)).	p. 3–6
	13b	Describe any methods used to tabulate or visually display results of individual studies and syntheses.	p. 3–6
	13c	Describe any methods used to synthesize results and provide a rationale for the choice(s). If meta-analysis was performed, describe the model(s), method(s) to identify the presence and extent of statistical heterogeneity, and software package(s) used.	p. 5, meta-analysis wasn’t performed
	13d	Describe any methods used to explore possible causes of heterogeneity among study results (e.g. subgroup analysis, meta-regression).	Not relevant, meta-analysis wasn’t performed
	13e	Describe any sensitivity analyses conducted to assess robustness of the synthesized results.	Not relevant, meta-analysis wasn’t performed
Reporting bias assessment	14	Describe any methods used to assess risk of bias due to missing results in a synthesis (arising from reporting biases).	p. 6
Certainty assessment	15	Describe any methods used to assess certainty (or confidence) in the body of evidence for an outcome.	p. 6
RESULTS
Study selection	16	Describe the results of the search and selection process, from the number of records identified in the search to the number of studies included in the review, ideally using a flow diagram.	p. 4
Study characteristics	17	Cite each included study and present its characteristics.	p. 6 (Appendix 3)
Risk of bias in studies	18	Present assessments of risk of bias for each included study.	p. 6 (Appendix 3)
Results of syntheses	19a	For each synthesis, briefly summarise the characteristics and risk of bias among contributing studies.	p. 6 (Appendix 3)
	19b	Present results of all statistical syntheses conducted. If meta-analysis was done, present for each the summary estimate and its precision (e.g. confidence/credible interval) and measures of statistical heterogeneity. If comparing groups, describe the direction of the effect.	Not relevant, meta-analysis wasn’t performed
	19c	Present results of all investigations of possible causes of heterogeneity among study results.	Not relevant, meta-analysis wasn’t performed
	19d	Present results of all sensitivity analyses conducted to assess the robustness of the synthesized results.	p. 5–10
Reporting biases	20	Present assessments of risk of bias due to missing results (arising from reporting biases) for each synthesis assessed.	p. 6 (Appendix 3), p. 6–10
Certainty of evidence	21	Present assessments of certainty (or confidence) in the body of evidence for each outcome assessed.	p. 6
DISCUSSION
Discussion	22a	Provide a general interpretation of the results in the context of other evidence.	p. 12–14
	22b	Discuss any limitations of the evidence included in the review.	p. 15–16
	22c	Discuss any limitations of the review processes used.	p. 15–16
	22d	Discuss implications of the results for practice, policy, and future research.	p. 15–16
OTHER INFORMATION
Registration and protocol	23a	Indicate where the review protocol can be accessed, or state that a protocol was not prepared.	Not registered
	23b	Describe and explain any amendments to information provided at registration or in the protocol.	Not available
Support	24	Describe sources of financial or non-financial support for the review, and the role of the funders or sponsors in the review.	p. 17
Competing interests	25	Declare any competing interests of review authors.	p. 17
Availability of data, code and other materials	26	Report which of the following are publicly available and where they can be found: template data collection forms; data extracted from included studies; data used for all analyses; analytic code; any other materials used in the review.	p. 17

Appendix 2: JBI critical appraisal table.
Study	Type	1	2	3	4	5	6	7	8	Overall
Matthews et al. 2023.1	Literature Review	Yes	Yes	Partial	N/A	Yes	Yes	Yes	Yes	Moderate
Bulut and Özdal. 2025.3	Empirical Study	Yes	Yes	Partial	Yes	Yes	Yes	Yes	Yes	Low
Jiang. 2024.4	Case Study	Yes	Yes	Partial	Yes	Yes	Yes	Yes	Yes	Low
Tomić et al. 2023.5	Literature Review	Yes	Partial	Partial	N/A	Yes	Yes	Yes	Yes	Moderate
Hwang and Wu. 2025.6	Conceptual Research	Yes	N/A	N/A	N/A	Partial	Yes	Yes	Yes	Low
Chen et al. 2024.7	Case Study	Yes	Yes	Partial	Yes	Yes	Yes	Yes	Yes	Moderate
Hu et al. 2025.8	Empirical Study	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Low
Mustafa. 2023.9	Industry Analysis	Yes	Partial	Partial	Yes	Yes	Partial	Yes	Yes	Moderate
Wang and Hu. 2023.10	Conceptual Research	Yes	N/A	N/A	N/A	Partial	Yes	Yes	Yes	Low
Mao et al. 2023.11	Empirical Study	Yes	Yes	Partial	Yes	Yes	Yes	Yes	Yes	Low
Qiu et al. 2024.14	Empirical Study	Yes	Yes	Partial	Yes	Yes	Yes	Yes	Yes	Low
Li et al. 2024.15	Literature Review	Yes	Partial	Partial	N/A	Yes	Yes	Yes	Yes	Moderate
Özdal. 2024.16	Conceptual Research	Yes	N/A	N/A	N/A	Partial	Yes	Yes	Yes	Low
Escudero Fernández. 2024.17	Empirical Study	Yes	Partial	Partial	Yes	Yes	Yes	Yes	Yes	Moderate
Ruiz-Arellano et al. 2022.18	Empirical Study	Yes	Partial	Yes	Yes	Yes	Yes	Yes	Yes	Low
Cui et al. 2022.19	Empirical Study	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Low
Zhang. 2025.20	Empirical Study	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Low
Yan et al. 2023.21	Conceptual Study	Yes	Partial	Partial	Yes	Yes	Yes	Yes	Yes	Moderate
Türker. 2025.22	Conceptual Research	Yes	N/A	N/A	N/A	Partial	Yes	Yes	Yes	Low
Tian. 2022.23	Empirical Study	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Moderate
Hamzaj. 2024.24	Conceptual research	Yes	Partial	Partial	Yes	Yes	Yes	Yes	Yes	Moderate
Liang. 2024.25	Empirical Study	Yes	Yes	Partial	Yes	Yes	Yes	Yes	Yes	Low
Wu et al. 2024.26	Literature Review	Yes	Yes	Partial	N/A	Yes	Yes	Yes	Yes	Moderate
Liu. 2023.27	Empirical Study	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Low
Sudarmanto. 2025.28	Empirical Study	Yes	Partial	Partial	Yes	Yes	Yes	Yes	Yes	Moderate
Wang et al. 2025.29	Conceptual Research	Yes	N/A	N/A	N/A	Partial	Yes	Yes	Yes	Low
Liu. 2024.30	Empirical Study	Yes	Partial	Partial	Yes	Yes	Yes	Yes	Yes	Moderate
Vaithilingam et al. 2023.31	Empirical Study	Yes	Partial	Partial	Yes	Yes	Yes	Yes	Yes	Moderate
Mohamed. 2024.32	Empirical Study	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Low
Muji et al. 2023.33	Conceptual Research	Yes	N/A	N/A	N/A	Partial	Yes	Yes	Yes	Low
Baquero and Almeida. 2025.34	Empirical Study	Yes	Partial	Partial	Yes	Yes	Yes	Yes	Yes	Moderate
Source: [1, 3–11; 14–34] Note: The JBI Checklist Questions (Corresponding to Column Numbers): 1) Is the research question or objective clearly defined? 2) Are the inclusion/exclusion or selection criteria appropriate and clearly described? 3) Is the sampling or study selection method adequate and transparent? 4) Are the data collection or evidence sources clearly described and appropriate? 5) Are confounding factors, biases, or limitations addressed? 6) Is the analysis or synthesis method appropriate and clearly reported? 7) Are the interpretations or conclusions consistent with the data or evidence? 8) Is there a critical appraisal, reflexive evaluation, or methodological rigor explicitly reported? Source: 1, 3–11; 14–34

Appendix 3: PRISMA table of included studies.
Author. Year	Type of research	Methodology	Sample	Country	Outcomes	Risk of bias
Matthews et al. 2023.1	Literature review	Qualitative synthesis of existing literature	N/A	Global	Found that automation in graphic design reduces manual tasks, leading to faster workflows and altered designer roles.	Moderate – Potential bias due to lack of detailed search strategy and unclear study selection process. No primary data, so the reliability of synthesis may be limited.
Bulut and Özdal. 2025.3	Empirical research	Case study of AI’s impact on logo design	N/A	Turkey	Found that AI can enhance innovation, originality, and functionality in logo design.	Low – Clear methodology, well-balanced data collection methods.
Jiang. 2024.4	Case study	Qualitative case study examining AI’s impact on design composition	N/A	Global	Demonstrated that AI can act as a co-author in design, improving the creative process through multimodal composition.	Moderate – Unclear sample size information and data collection transparency.
Tomić et al. 2023.5	Review paper	Review of existing studies on AI in graphic design	N/A	Global	Highlighted AI’s diverse applications in graphic design, such as automation, creativity enhancement, and industry transformation.	Moderate – The review lacks transparency regarding search strategy and study selection, but overall synthesis is clear. Potential bias due to missing details on methodological rigor.
Hwang and Wu. 2025.6	Conceptual research	Conceptual analysis of AI’s role in graphic design education	N/A	Global	Explores how AI in text-to-image generation is reshaping graphic design education and the role of content creators.	Low – Strong conceptual analysis with clear arguments, logical structure, and significant contribution to the field. No empirical data, but the theoretical contribution is clear.
Chen et al. 2024.7	Case study	Case study of co-innovation between human designers and AI	N/A	Global	Found that human-AI collaboration in design can foster innovation, enhancing creative output through shared decision-making	Moderate – Potential bias in case selection and subjective analysis, but generally solid empirical methodology.
Hu et al. 2025.8	Empirical study	Quantitative survey and qualitative interviews)	200	China	Revealed that designers’ subscriptions to AI tools are influenced by efficiency gains but hindered by concerns over control and creativity loss.	Low – Clear methodology, adequate sample size, and well-balanced data collection methods contribute to a low risk of bias.
Mustafa. 2023.9	Industry analysis	Descriptive industry analysis	N/A	Global	Identified AI’s impact on efficiency and creativity in graphic design but noted concerns about job displacement and uncreative outputs.	Moderate – The lack of primary data collection and a clearly defined methodology introduces some potential for bias, though the analysis of industry impacts is still insightful.
Wang and Hu. 2023.10	Conceptual Analysis	Conceptual analysis	N/A	Global	Analyzed how AI can transform information graphics design, particularly in data visualization and automating design elements.	Low – Strong theoretical analysis with clear arguments, logical coherence, and no empirical data to limit its theoretical contribution.
Mao et al. 2023.11	Empirical study	Experimental study of hybrid human-AI design assistants	100	Global	Demonstrated that hybrid human-AI systems improve design quality and speed, with AI assisting in generative tasks.	Low – Strong experimental design, clear methodology and empirical data.
Qiu et al. 2024.14	Empirical study	Quantitative survey and qualitative interviews)	250	Global	Found that generative AI tools increased design flexibility but designers struggled with balancing AI suggestions and personal creativity.	Low – The study uses a clear, structured methodology with an adequate sample size, and data collection is well-explained.
Li et al. 2024.15	Literature review	Critical interpretive synthesis of existing literature	N/A	Global	Analyzed the impact of AI on graphic design, emphasizing its potential to automate repetitive tasks while raising concerns about creativity loss and copyright issues.	Moderate – The lack of a clear search strategy and selection criteria introduces potential bias, but the overall synthesis provides some insight into the field.
Özdal. 2024.16	Conceptual research	Conceptual analysis	N/A	Global	Explored AI’s transformative role in graphic design, focusing on the evolving relationship between technology and creativity in design processes.	Low – The study presents a clear conceptual framework, logically structured argumentation, and significant contributions to the theoretical understanding of AI in graphic design.
Escudero Fernández. 2024.17	Empirical study	Quantitative and qualitative analysis of AI in Illustrator	N/A	Spain	Explored the perception and exploration of AI in Illustrator among graphic design students, focusing on usability and impact.	Moderate – The study lacks key details about sample size and data collection methodology, but it provides valuable insights into the perceptions of graphic design students.
Ruiz-Arellano et al. 2022.18	Empirical study	Survey and content analysis	150	Mexico	Found that AI tools enhance the creation of persuasive visual discourses, with designers reporting increased efficiency and creative possibilities.	Low – Clear empirical methods, well-defined sample, and outcomes
Cui et al. 2022.19	Empirical Study	Mixed-methods (survey and interviews)	200	South Korea	Investigated the usage and impact of GAN in graphic design, finding that GANs improve creativity but require extensive training and computational resources.	Low – The study has a clear methodology with an adequate sample size, and data collection methods are transparent and rigorous.
Zhang. 2025.20	Empirical study	Image recognition system-based design	50	China	Demonstrated the use of AI-based image recognition systems to streamline graphic design workflows, improving accuracy and speed in design tasks	Low – Clear methodology with quantitative data collection and well-defined outcomes.
Yan et al. 2023.21	Conceptual research	Conceptual analysis	N/A	Global	Discussed the innovation of visual communication design styles enabled by AI, focusing on how AI can generate novel design ideas and adapt to different visual languages.	Moderate – Strong conceptual analysis, but no empirical data involved.
Türker. 2025.22	Conceptual research	Qualitative analysis on human-AI interaction in design	N/A	Global	Explores the balance between human creativity and AI precision in graphic design, arguing that both are essential for producing meaningful designs.	Low – The study provides a well-structured conceptual framework and logical coherence, offering valuable theoretical insights.
Tian. 2022.23	Empirical Study	Experimental study on AI algorithms in design	80	China	Analyzed the application of AI in digital media art design, concluding that AI algorithms significantly enhance design element optimization but face challenges in maintaining artistic depth.	Low – Solid empirical research with clear methods and sample size.
Hamzaj. 2024.24	Conceptual research	Conceptual framework and analysis	N/A	Global	Explores how machine learning combined with variable fonts can empower UI/UX designers to create more personalized and responsive user interfaces.	Moderate – Strong theoretical work but lacks empirical validation.
Liang. 2024.25	Empirical study	Empirical study of AI applications in design	100	China	Demonstrated AI’s role in enhancing the design of cultural and creative products by optimizing design workflows and fostering innovation.	Low – Strong empirical research with clear outcomes and methodology.
Wu et al. 2024.26	Systematic review	Systematic literature review	N/A	Global	Analyzed the integration of AIGC (AI-generated content) in design, identifying trends in its application and evolution across various design disciplines.	Moderate – The review lacks clear details on the search strategy and study selection, which introduces potential bias, but the synthesis of existing literature provides some value.
Liu. 2023.27	Empirical Study	Experimental study on AI design assistant system	50	China	Found that AI-assisted design systems improved workflow efficiency in graphic design tasks, particularly in automating repetitive tasks.	Low – Clear empirical design, solid data collection methods)
Sudarmanto. 2025.28	Empirical Study	Qualitative study (interviews)	30	Indonesia	Explored the intersection of AI and creativity from the perspective of local Indonesian graphic designers, highlighting varying attitudes towards AI’s role in the creative process.	Moderate – Sample size is small, potential bias in interviewee selection.
Wang et al. 2025.29	Conceptual research	Conceptual study on AI-human collaboration in ideation	N/A	N/A	Developed a collaborative ideation system combining human creativity and AI, improving concept design efficiency and creative output.	Low – The study provides a clear, well-structured conceptual framework with strong theoretical contributions.
Liu. 2024.30	Empirical study	Empirical study on AI-assisted painting creation in art and design	70	China	Studied the use of AI in painting, finding that AI-assisted tools helped artists generate novel creative ideas but lacked emotional depth in final designs.	Low – Clear experimental methodology with solid data collection and analysis.
Vaithilingam et al. 2023.31	Empirical study	Experimental study on improving AI integration in programming environments	50	Global	Explored the effectiveness of AI-assisted programming tools like Visual Studio IntelliCode, finding improvements in the user experience and programming efficiency.	Low – Clear methodology and sufficient empirical data.
Mohamed. 2024.32	Empirical study	Experimental study on the application of AI in creating virtual scenes for media materials	80	Global	Found that AI techniques in virtual scene simulation enhance the realism and interactivity of media materials, improving design and storytelling capabilities.	Low – Strong empirical methods with clear research design.
Muji et al. 2023.33	Empirical study	Mixed-methods on AI in education	N/A	Global	Explored AI’s role in graphic design education, focusing on how AI can enhance teaching and learning outcomes.	Moderate – The study provides a clear conceptual framework and logical structure, contributing to the theoretical understanding of AI in education.
Baquero and Almeida. 2025.34	Empirical study	Empirical study on AI in graphic design in Quito, Ecuador	N/A	Ecuador	Analyzed the implementation, use frequency, and future outlook of AI in graphic design in Quito.	Low – Empirical research, clear methodology.

Appendix 4: PRISMA table of excluded studies.
Author. Year	Study Type	Reason for Exclusion	Stage of Exclusion
Kondratenko et al. 2016.2	Experimental Study	Study lacked relevant data on AI applications in design.	Full-text excluded
Baquero and Almeida. 2025.34	Case Study	Focus on AI implementation in graphic design in Ecuador, region-specific.	Full-text excluded
Mazakova et al. 2024.35	Applied Engineering Study	Not related to AI in graphic design, focused on UAV systems.	Full-text excluded
Smailov et al. 2025.36	Applied Engineering Study	Poor methodological quality and irrelevant focus.	Full-text excluded after eligibility assessment
Konurbayeva et al. 2015.37	Theoretical Study	Outside scope, not related to AI tools in graphic design.	Full-text excluded after eligibility assessment
Smailov et al. 2025.38	Literature Review	Focus on fibre optic sensors in concrete structures, unrelated to AI in graphic design.	Full-text excluded
Smailov et al. 2025.39	Applied Engineering Study	Outside scope, not related to AI tools in graphic design.	Full-text excluded after eligibility assessment
Sarinova et al. 2022.40	Conference ssssPaper	Focus on aerospace images, not related to AI in graphic design.	Full-text excluded
Mihăilescu and Chiper. 2025.41	Applied Engineering Study	Not related to AI tools in graphic design, focused on media integrity.	Full-text excluded
Knochel. 2023.42	Case Study	Focus on AI in art education, not graphic design specifically.	Full-text excluded
Serediuk. 2024.43	Research Article	Methodological flaws, focused on AI applications in legal analysis, not relevant to the design field.	Full-text excluded
Zelisko et al. 2024.44	Applied Research	Focus on AI in agriculture, not graphic design.	Full-text excluded
Khan et al. 2025.45	Empirical Study	Study focused on SDGs and technological innovation, not relevant to AI tools in graphic design.	Full-text excluded
Shepitko et al. 2024.46	Review Article	Methodologically not rigorous enough, focused on crime prevention using AI.	Full-text excluded
Hnatyshyn et al. 2025.47	Research Paper	Lacked relevance to design, focused on accounting innovations instead of AI tools in design.	Full-text excluded
Babak et al. 2021.48	Conference Paper	Focus on energy informatics, not related to AI in graphic design.	Full-text excluded
Orazbayev et al. 2023.49	Applied Engineering Study	Study lacked AI applications in graphic design, focused on reactors and optimization.	Full-text excluded
Zarifhonarvar. 2024.50	Empirical Study	Not related to AI in graphic design, focused on labor market impacts.	Full-text excluded
Prem. 2023.51	Review Article	Study did not meet inclusion criteria for AI in graphic design, focused on AI ethics.	Full-text excluded
Issayeva et al. 2024.52	Research Article	Focus on green economy and digital technologies, not graphic design.	Full-text excluded
Panchenko et al. 2020.53	Conference Paper	Focus on mechatronic systems, not AI in graphic design.	Full-text excluded
Efremov. 2025.54	Theoretical Study	Outside scope, unrelated to AI in graphic design.	Full-text excluded after eligibility assessment
Source: References [2; 34–54]

Cite this article as:
Zhou S, Joneuraratana E, Sirvesmas V and Jamuni P. The Impact of Artificial Intelligence on the Evolution of Graphic Design: Current Practices and Challenges. Premier Journal of Science 2025;14:100151

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