AI-assisted methodologies captured lived experiences and enhanced innovation practices, supporting practitioners, policymakers, and researchers in designing ageing technology. This study examined AI-assisted methods, leveraging open conversations with 30 seniors to address the complexities of ageing and technology in Singapore. Using prompt engineering, we analysed coded data with role-based, context-providing, and information-seeking prompts, generating Python code for clustering analysis. The focus was on seniors’ perceptions of technology and health concerns, revealing 25 indicators across six health dimensions. Of these, 12 social-emotional determinants influenced perceptions through emotional support and social interaction on technology adoption. Our analysis produced a four-cluster typology, providing a systematic framework to categorise perception patterns and address seniors’ diverse needs.
