Integrating Somatic Empathy, AI Analysis, and Co-Design for Blind and Low-Vision Users

Designing effective assistive technology (AT) for blind and low-vision (BLV) users requires methodologies that move beyond visual-centric paradigms to deeply understand emotional and embodied experiences. While co-design is essential, traditional feedback mechanisms often fail to capture the full spectrum of non-verbal and somatic user needs. This case study investigates the benefits of integrating AI-driven sentiment analysis of qualitative user data, somatic workshops to build embodied empathy in designers, and participatory co-design with the BLV community. Key findings reveal that sentiment analysis is powerful for triangulating individual emotional pain points but insufficient as a standalone tool. Somatic practices were transformative in disrupting designers' sensory biases and fostering a non-visual, empathetic foundation. However, the integration of these methods also revealed critical challenges, including language barriers and the need for highly structured prototyping schedules. The study highlights a necessity in shifting design methodology toward multisensory, tech-supported, and inclusive practices to not only address the needs of BLV users but also reduce visual fatigue and improve usability for a broader audience.