## 2 Literature Review
### Research Gap and Eye-Tracking Technology  
AIGC has exhibited robust data processing and prediction capabilities in practical applications, with its algorithms playing a vital role in facilitating the solution of research problems through deep learning-based data analysis. These studies effectively illuminate the \"instrumental nature\" [33] of AIGC. Although the results generated by AIGC serve as inspiration for designers, limited research has investigated whether the product generated by AIGC meets the preferences of its intended users and whether it can enhance users' attention to the product and thus promote product sales to a certain degree. This study will research this aspect by utilizing appropriate methodologies.  
Eye-tracking technology is employed to study user attention towards AI-generated products. Eye-tracking technology is typically used to observe what attracts a user's attention and examine the user's behavior [34]. Compared with questionnaires, interviews, scales, and other conventional survey methods, data obtained from eye-tracking techniques are less susceptible to the study population's subjective biases, competence levels, and environmental factors [35; 36]. Eye movements can identify the original feelings of users since user visual perception is closely linked with emotional response, and users are often unable to control their physiological responses voluntarily [37; 38; 36]. Eye-tracking technologies capture signals from the movements and activities of the pupil, cornea, sclera, iris, retina, and other eye components using various methods, including shape-based, appearance-based, feature-based, and hybrid methods [39; 40]. The eye indicators of an eye tracker include time to first fixation, first fixation duration, fixation count, fixation duration, visit count, and visit duration, among others [41]. The number of usability studies incorporating eye-tracking methodology has increased in the past decade [42]. [PERSON] et al. (2023) revealed differences in people's visual perceptions and preferences towards waterfront parks via eye movement experiments, which informed more judicious landscape element selections and park space arrangements [43]. [PERSON] et al. (2021) conducted a study on users' visual search efficiency and their experience with application icons varying in color and border shape through the use of eye-tracking techniques [44]. [PERSON] and [PERSON] (2019) investigated the correlation between eye movements and users' emotional responses to product features using images of various SUVs and accounting for gender differences [45]. [PERSON] et al. (2019) employed eye-tracking technology to examine children'svisual attention towards texts and images of equivalent areas within storybooks, offering a scientific basis for the layout design of electronic storybooks [46].  
The preceding analyses demonstrate AIGC's broad applicability in various fields and its vital role in facilitating the solution of research problems. As an emerging AI productivity engine, AIGC has also triggered in-depth thinking in the design field. This study aims to explore the potential of AIGC in design by analyzing male and female attention towards AI-generated products, thereby verifying its assistance in the design process. Additionally, this study accounts for gender differences. The entry point for the study is wireless Bluetooth in-ear earphones. We used GPT4.0 for image generation and combined it with eye-tracking technology to analyze male and female attention to the corresponding images. The research outcomes will, to a certain degree, reflect AIGC's capability for creation and comprehension. These findings will be valuable in accelerating product design output, enhancing product attractiveness, and facilitating product sales.