## 3 Materials and Methods
#### 3.2.2 Stimuli  
The model GPT-4 from ChatGPT was used to create the stimuli of high-leg orthoses, which allows the creation of a custom GPT that can be configured according to the desired response and outputs. DALL-E 3 image generation was activated in the configuration interface of the customized GPT, and specific knowledge and instructions were provided as input. However, OpenAI does not disclose specific details about its training configuration or model architecture. The customized GPT named _OrthoticFootGPT_ was described as a \"generative design assistant to support the creation of socially acceptable foot orthoses\". To generate a user-specific output, the knowledge base for the _OrthoticFootGPT_ was customized with a comprehensive set of data tailored to high-leg orthoses. This dataset included  
* Product specifications: Detailed information on the aesthetic and functional aspects of high-leg orthoses, such as material types, sizes and weight from data sheets and guidelines.
* Usage instructions: Documents with instructions and user manuals for the use and maintenance of these devices.
* Product images: Sample images of high-leg orthoses from various suppliers as a visual reference for a realistic design.
* Research articles: Related articles providing insights into product design rules, the concepts of accessibility/usability, and orthotic development were searched on Google Scholar.
* Keyword definitions: Documents based on international standards and dictionary definitions provided explanations about the three keywords (usability, social acceptability, and sport design) to guide the AI in generating contextually relevant content.  
For both _ChatGPT_ and _OrthoticFootGPT_, the following prompt was used as the foundation for image generation: _\"Create a realistic image of a white-grey foot orthosis with a high shaft. The orthosis should have stabilizing properties so that it can be worn for longer periods during rehabilitation. The background should be white and only show the leg with the orthosis\"_. In addition, a sentence was added to the basic query for each keyword to tailor the images to the specific definition. For example, for social acceptability: _\"The orthosis should ensure a high level of social user acceptance by offering an attractive aesthetic design and high wearing comfort\"_.  
To ensure realistic product images and avoid misinterpretation or misleading details, the DALL-E editor interface was applied manually to ensure consistency across all images (e.g., by removing the second leg or unrealistic features). Similar to the first study, each image was manually adjusted using Adobe Photoshop to create transparent backgrounds and ensure consistent foot rotation angles. All generated images are shown in Figure 2 and the workflow for generating the visual stimuli is illustrated in Figure 3.  
Figure 2: Overview of the 16 stimuli used in the online survey, categorized by the two independent variables, GPT and Keyword. The stimuli are arranged horizontally by the four keywords, _No Keyword_, _Usability_, _Social Acceptability_, and _Sporty Design_, and vertically by the two GPTs: _ChatGPT_ and _OrthoticFootGPT_.