# Human-AI Co-Drawing: Studying Creative Efficacy and Eye Tracking in Observation and Cooperation
## 1 Introduction  
AI technologies have been landing in the art field over the last decade. A series of text-to-image AI systems, such as Disco Diffusion, Midjourney, Stable Diffusion, Open AI's DALL-E 2, and Google's Imagen, are making great strides [1; 2; 3; 4]. It has triggered a heated debate about human-AI cooperation and creativity in AI art. AI is based on image generation from natural language descriptions, which largely enhances the efficiency and effectiveness of the translation from creativity to vision [1]. Human-AI cooperative drawing refers to the process of drawing creation in which humans and AI technology participate together. In this cooperation, humans can provide creativity, conceptualization, and aesthetic guidance for decision-making and selection, while the AI uses its powerful computational capabilities and algorithms to generate unique images or create paintings [2; 3]. In the past, authors of both traditional and digital painting creations needed to be skilled in the use of tools and have extensive technical experience to accurately map their brain's imagery to the visual layer. However, in co-creation with text-to-image AI generators, artists and non-artists alike can input textual descriptions to generate many high-quality images [5]. Most of the current AI drawings use text to generate images with some limitations on the semantics that text can describe. There are learning costs for novices to learn operations such as multiple parameters and prompt commands. But there is currently the possibility of using sketches plus text to generate images, which has increased levels of human intervention and freedom, and in the AI real-time drawing generation application, KREA.AI, artists can interact with the AI through textual descriptions coupled with simple brushstroke drawings and colours, with the AI interpreting and elaborating on the user's inputs in real-time and transforming them into complex, detailed images. This real-time feedback makes the drawing process full of unpredictability and allows the artist to see the AI's feedback on the artist's picture during the drawing process, allowing for AI-based adjustments to the picture during the drawing process, which also allows the user to have a variety of strategies for looking at the picture and forming new ways of cooperating on the drawing. The generative mechanisms use a verbal-visual model to understand the user's \"cue + hand-drawn\" input, and then guide the generator to produce a high-quality image. They are capable of composing images of any style and content based on cues and hand drawings. It is worth discussing what new variations in the new HCI paradigm are possible. Existing research on the impact of human-AI collaborative mapping on visual and creative perception is scarce and lacks empirical studies [5; 6]. The use of eye-tracking technology can record and analyse multiple metrics such as users' attention distribution and concentration, as well as visual perception and cognitive load in cooperating with the AI, along with its unique value and rare application in such studies. Therefore, this study aims to explore how different observation strategies and cooperation modes affect human visual and creative perception. The study will test these effects through eye-tracking experimental design and creative efficacy scale design, provide thoughts on different modes of cooperation for future design practitioners and AI design drawing enthusiasts, and promote the idea that more professionals or non-design and art professionals can also experiment with new ways of cooperation. People can choose more efficient human-AI collaborative drawing modes based on the theories in this article and improve cooperation and drawing efficiency and focus visual attention. The study improves visual search efficiency and provides new research perspectives and ideas for later researchers to choose and think about efficient and high-quality cooperation modes and interaction forms.