While substantial work has focused on explaining job–candidate matches in recruiting, a gap remains between these explanations and end-user goals, with visual explanations often being complex and overwhelming. Moreover, existing approaches are often static and provide limited interaction support, making it difficult for users to explore explanations in a way that matches their information needs. Guided by a human–centered design process, we present CAPTURE, a visual–conversational dashboard designed to support on-demand explanations in job–candidate matching. It integrates visual explanations with a conversational component powered by a multi-agent architecture that interprets user queries and orchestrates data retrieval and analysis workflows. By linking rich textual responses with relevant visualizations, CAPTURE enables flexible exploration and supports user-driven explanations in job–candidate matching algorithms.

Music-based reminiscence has the potential to positively impact the psychological well-being of older adults. However, the aging process and physiological changes, such as memory decline and limited verbal communication, may impede the ability of older adults to recall their memories and life experiences. Given the advanced capabilities of generative artificial intelligence (AI) systems, such as generated conversations and images, and their potential to facilitate the reminiscing process, this study aims to explore the design of generative AI to support music-based reminiscence in older adults. This study follows a user-centered design approach incorporating various stages, including detailed interviews with two social workers and two design workshops (involving ten older adults). Our work contributes to an in-depth understanding of older adults’ attitudes toward utilizing generative AI for supporting music-based reminiscence and identifies concrete design considerations for the future design of generative AI to enhance the reminiscence experience of older adults.