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Explore the driving factors of designers’ AIGC usage and its impact on the graphic design industry

November 7, 2024

The Transformation of the Design Industry in the AIGC Era

Traditionally, users have perceived that only manual laborers or those in repetitive jobs would be subject to technological substitution. However, with the emergence of technologies like Midjourney, ChatGPT, and Notion AI, known as Artificial Intelligence-Generated Content (AIGC), we have come to realize that cognitive laborers, particularly creative designers, also face similar professional challenges.

Yet, there has been relatively little research analyzing the acceptance and trust of artificial intelligence from the perspective of designers. This study integrates the TAM/TPB behavioral measurement model, incorporating intrinsic characteristics of designers, to delineate their perceived risks of AIGC into functional and emotional dimensions. It explores how these perceived characteristics, risks, and trust influence designers’ behavioral intentions, employing structural equation modeling for validation.

The findings reveal the following:

Designer trust is the primary factor influencing their behavioral choices.
Different dimensions of perceived risks have varying degrees of impact on trust, with functional risks significantly positively affecting trust compared to emotional risks.
Only by enhancing the transparency and credibility of Artificial Intelligence-Generated Content (AIGC) can the perceived characteristics of designers be elevated.
Only by effectively safeguarding designers’ legitimate rights and interests can perceived risks be significantly reduced, thereby enhancing trust and subsequently prompting actual behavioral intentions.

This study not only enhances the applicability and suitability of AIGC across various industries but also provides evidence for the feasibility of intelligent design in the creative design industry, facilitating the transition of AIGC to Artificial Intelligence-Generated Design (AIGD) for industrial upgrading.

The Impact of AIGC on the Creative Design Industry

AI has promoted social progress and optimized industrial structure on a macro level, while improving individual work efficiency and reducing repetitive labor on a micro level. Particularly for the creative industry, AI is like a double-edged sword.

On one hand, creative industry practitioners hope to improve work efficiency with AI. On the other hand, they are concerned that AI may lower industry entry barriers and reduce job opportunities and salaries. Its main impacts on the creative industry are reflected in several aspects:

The advantages that AIGC brings to designers and the creative industry are as follows:

It reduces repetitive labor and improves work efficiency. AIGC technology can generate a large number of design proposals in a short time, saving designers time and energy.
It has strong learning and induction capabilities, enhancing design creativity expression and implementation. AIGC technology can explore novel points and potential inspirations that designers may overlook through different algorithms and data analysis methods, enhancing design creativity.
AI can achieve customized services based on big data background user experiences. AIGC technology can optimize user experience through analysis of user behavior data, enhancing market competitiveness.

The threats posed by AIGC to designers and the creative industry are as follows:

It cannot replace human aesthetics. Although AIGC technology can generate design proposals, it cannot replace human aesthetics and personalized demands.
Lack of design thinking and leadership. AIGC technology can generate a large number of design proposals, but it cannot understand the intent and purpose of design, and cannot fundamentally solve design problems.
Possibility of repetitive design. Due to the training of AIGC technology algorithms based on existing data, there may be similar or repetitive design proposals.

However, we can balance the advantages and disadvantages of AIGC technology through some methods. Specific measures include:

Making the generation process and creative thinking of AIGC algorithms transparent and interpretable, allowing humans to better understand the generation process and results of AIGC technology.
Guiding AI training through enhanced human–computer interaction to achieve consistency in thinking and skills.
Increasing affinity through humanized interfaces or forms. Designing more friendly and user-friendly interfaces can make AIGC technology more in line with human needs and creativity.

AIGC is rapidly transforming the creative design industry by enhancing design quality through increased efficiency, reduced costs, and personalized services, while also driving interdisciplinary integration and transforming design education. This presents unprecedented development opportunities for the creative industry.

Overcoming Barriers to AIGC Adoption in the Design Industry

After exploring the key factors influencing designers’ adoption of AIGC, from objective conditions to subjective intentions, this study concludes that the main barriers to the rapid adoption of AIGC in the creative industry are concerns regarding intellectual property and ethical challenges.

Undoubtedly, mastering AIGC has become an indispensable skill for future designers. However, transitioning from AIGC to AIGD, the application of AIGC technology in the field of design continues to expand and deepen, providing designers with more tools and possibilities, while also promoting innovation and development in design. Its impact on the creative industry can be seen in several aspects:

Providing designers with more intelligent design tools. With the continuous development of AIGC technology, more and more intelligent design tools will be developed. These tools can help designers complete design tasks more quickly and accurately, while also being able to intelligently optimize based on user feedback.
Facilitating interdisciplinary collaboration. AIGC requires expertise from multiple fields, thus promoting interdisciplinary collaboration to achieve better design outcomes.
Designers also need to bear design ethics and legal responsibilities. Designers must ensure the fairness and transparency of AIGC algorithms, avoiding bias and discrimination in AIGC technology. These are issues that future designers need to pay attention to and consider.

In the creative industry, the legal risks associated with AIGC primarily include issues related to copyright and intellectual property ownership and infringement liability, the dissemination of false information and fraud, data privacy and ethical concerns, as well as contract and licensing agreements.

First, although there have been widespread calls for the government to establish effective punitive measures against unlawful AI activities, the progress of policy implementation has been relatively slow. The main reason lies in the complexity of AIGC application scenarios and the diversity of evaluation criteria. The specific legal risks can be analyzed from the following three perspectives:

a. Copyright and intellectual property ownership: public policy must clearly protect these rights, particularly patent rights. AIGC-generated content may infringe on others’ copyrights or privacy, leading to legal disputes. Additionally, the generation of false content by AIGC could be used for fraudulent purposes.
b. Data privacy: this must be ensured by the technology platforms. Designers require vast amounts of data for AIGC model training, and the collection and use of this data might violate privacy protection laws. The inherent technical characteristics of AIGC could inadvertently lead to personal data breaches, further complicating the determination of infringement.
c. Data authenticity: ensuring the authenticity of data is a prerequisite for its commercial application. Since AIGC can generate highly realistic false content (e.g., fake news, images, videos), such content could be used for fraud, misleading the public, or infringing on others’ rights. Therefore, the authenticity and validity of AIGC content must be verified before its use.

In conclusion, to regulate the risks brought by artificial intelligence technology, designers must operate within the framework of policy regulations, ensure data security, and uphold the principles of authenticity and validity when applying AIGC commercially.

Ethical Considerations and Fairness in AIGC Adoption

Technological change brings new challenges and opportunities to societal production models. In the face of AIGC’s transformation of the creative industry, designers must first assess its feasibility from the perspectives of external legal risks, internal ethical risks, and the fairness that balances both. Only then can AIGC be broadly integrated into creative work.

The moral risks brought by AIGC in the creative industry mainly cover content authenticity, trustworthiness of works, weakening of subjective values of creators, unclear attribution of moral responsibility, and spreading of prejudice and discrimination, which can weaken the output of cultural diversity and individualized viewpoints. This can be analyzed from the following three perspectives:

a. Designers need to ensure the content’s authenticity and the work’s trustworthiness before using AIGC, and the destruction of social trust by false content must be avoided.
b. Designers must ensure that AIGC complies with social moral codes and ethical standards to avoid negative social impacts. Due to the autonomy-generating nature of AIGC, generating content may lead to unethical or harmful outcomes, such as spreading hate speech, violent content, and so on. How to pursue responsibility and attribution is a complex ethical issue.
c. Designers need to actively maintain cultural diversity and individualized creative perspectives. As AIGC is based on integrating big data and innovation, its design proposals tend to generate content that conforms to mainstream culture and aesthetics, which will lead to homogenization of cultural expression and weaken the space for the expression of cultural diversity and minority cultures.

The inequality that AIGC may bring in the creative industry is mainly reflected in the imbalance in access to resources, the application of technology, and the distribution of opportunities. In addition, AIGC may weaken the subjective status of human creators and amplify the bias in the data, leading to the restriction of cultural expression of minority groups. AIGC will enhance the level of creativity of social groups on the one hand, and exacerbate the differences in the level of individual creativity on the other hand. These phenomena and pitfalls can be discussed from the following three perspectives:

a. Inequitable access to knowledge and resources. Individuals’ acceptance of and proficiency in new technologies will magnify the differences between them, thus reinforcing the inequality of access to knowledge.
b. Inequity in access to cultural expression and creativity will be exacerbated. As AIGC models are often trained based on large-scale models, these data are more biased towards the mainstream culture, which will weaken the uniqueness of human creativity and intensify the bias of cultural expression.
c. Inequity in income and opportunity distribution. As AIGC undermines the market demand for human creators and lowers the employment threshold for different industries, this will lead to a reduction in income and employment opportunities for junior people, which in turn exacerbates economic inequality.

The above analyses the challenges brought by AIGC to the creative industry from the path of access, application, and distribution of resources, and these hidden dangers of technological development need to be taken seriously by designers and actively guided by policies in order to achieve healthy and sustainable social innovation.

In conclusion, the opportunities brought by AIGC to the creative industry are more efficient, high-quality, and high-experience work models, while the resulting legal, ethical, and unfair pitfalls will accompany the technological changes and the development of the industry.

Conclusion: Embracing the Transformation of the Design Industry in the AIGC Era

The research results show that in the creative industry, designers’ perceptions of the usefulness of AIGC, perceived ease of use, technology anxiety, self-efficacy, and convenience conditions all influence their willingness to use and behavior. Among the 16 hypotheses in the structural equation, only five hypotheses were validated: subjective norms significantly influence perceived usefulness, technology anxiety significantly influences perceived ease of use, technology anxiety significantly influences self-efficacy, self-efficacy significantly influences perceived ease of use, and self-efficacy significantly influences intention to use.

The reasons behind these findings lie in the subjective barriers to usage, technological development and compatibility, as well as objective factors such as self-learning channels, learning costs, and rapid technological advancements.

To regulate the risks brought by artificial intelligence technology, designers must operate within the framework of policy regulations, ensure data security, and uphold the principles of authenticity and validity when applying AIGC commercially.

The opportunities brought by AIGC to the creative industry are more efficient, high-quality, and high-experience work models, while the resulting legal, ethical, and unfair pitfalls will accompany the technological changes and the development of the industry. Designers must navigate these challenges to embrace the transformation of the design industry in the AIGC era.