## 5 Discussion  
The Impact of AI-Generated and Conventional Orthotic Designs Across Device Categories on Social Acceptability  
In our first study, we conducted a mixed-method online survey to explore the impact of three Design Types and four Orthotic Categories on social acceptance. The quantitative analyses of Likert scales, as well as the SCM and WEAR models, indicated that Orthotic Categories such as _Foot Bandages/Textfiles_ achieved significantly higher acceptance ratings, followed by _Foot Drop Orthoses_ and then _Short-Leg_ and _High-Leg Orthoses_. Furthermore, the _Generative AI_ Design Type had a significant impact on perceived acceptability, as these devices were associated with stereotypes of greater competence and warmth compared to medical or developmental designs, which confirms our hypothesis. These findings were supported by the qualitative feedback, which highlighted the potential of using _Generative AI_ to consider user preferences and increase acceptance of orthopedic footwear. Overall, these results confirm the potential of integrating AI into the design process for wearable foot devices. However, the significantly lower acceptance rates for short and high-leg orthoses represent a consistent challenge that reflects the concerns reported in previous research. By identifying the challenges of user acceptance associated with different categories of orthoses, this study contributes to a deeper understanding of the factors that influence the social acceptance of orthotic devices. Our findings are valuable for future developments aimed at enhancing the aesthetic design of these devices to better meet user needs. The Impact of GPT Customization and Prompt Keywords on the Social Acceptability of AI-Generated Orthotic Designs  
The second study examined the impact of high-leg orthosis designs generated using _OrthoticFootGPT_ and _ChatGPT_, as well as the influence of four different prompt structures incorporating specific keywords on social acceptability. This extends the results of the first study to explore if a personalized GPT and specific Keywords can have a systematic positive influence on user perception. The quantitative results indicate that the customized _OrthoticFootGPT_ model can enhance acceptance ratings compared to _ChatGPT_. Additionally, the study showed that different keywords evoke different stereotypical perceptions of the wearable device. In particular, the keyword _Sporty Design_ led to significantly better ratings compared to other keyword variations, suggesting that aesthetic framing plays a crucial role in user perception. These findings are consistent with the qualitative feedback, which revealed a discrepancy between the perception of medical orthoses and more socially integrated designs, such as those inspired by the aesthetics of footwear or sports. Participants expressed contrasting preferences regarding the visual appearance of orthotic designs: while some preferred an inconspicuous design to avoid attracting attention, others emphasized that a more visible, eye-catching design could facilitate social recognition and consideration by others. This contrast illustrates the complexity of the relationship between aesthetics as well as functional and social expectations. These results highlight the potential of generative AI to enhance the design process of wearable medical devices. Furthermore, our results align with previous studies on wearable device perception, emphasizing that design plays a crucial role in acceptance [35; 36]. In addition, these results support earlier findings that highlight a discrepancy in the literature regarding the stigmatization of medical devices, where functional necessity often conflicts with social acceptability, leading to lower acceptance [36]. Similar to findings by [PERSON] et al. [50], who observed that wearables positioned on the ankle received lower social acceptance scores, our study showed that high-leg orthoses face significant acceptance challenges compared to other orthotic device categories.