Optimizing Design Thinking Strategy for AI-Generated Image Models: Using Logo Design as a Case Study
Yuxin Chen · SHS Web of Conferences · 2025
In recent years, the rapid development of artificial intelligence (AI) has greatly improved design efficiency and visual effects. Nevertheless, the image design aspect remains largely rudimentary in current AI design platforms. Looking at examples of logo design, we see that most logos are merely replicas of current logos. To fully exploit the potential of AI in logo design, we need to optimize design thinking. This study investigates the advantages and challenges of existing AI image models in logo design applications. It proposes a training program to improve design knowledge, thinking, skills, and visual materials in the AI design platform database. Deepening training in specific domain knowledge improves the AIGC model’s ability to understand the context of logo design, combining the designer’s creative thinking with the AI’s processing power to achieve more creative results. Additionally, this study creates a new model with user participation in design, using AIGC technology to collect user feedback and dynamically adjust the design scheme. The goal is to enhance the use of AIGC in logo design, offer a fresh design approach, aid AI in creating image technology to enhance design thought and expression, make AI logo generation technology more intelligent and varied, boost design creativity, and improve brand image construction efficiency and quality.