Dynamic Evolution of Needs and Interaction Experiences in GenAI Health Video Co-Design Workshops for Older Adults: BERTopic-Based Analysis

Amid global aging, older adults urgently need support for health self-management. Generative AI (GenAI) offers personalized health support, yet research lacks systematic analysis of human-computer interaction (HCI) and co-design in dynamic GenAI scenarios. This study used BERTopic-based dynamic topic modeling to trace the evolution of older adults’ needs across three iterative GenAI health video co-design workshops. Twenty community-dwelling older adults aged 60 to 84 participated, providing interview records, focus group transcripts, and user-GenAI prompt texts. Four core HCI themes emerged. Early rounds centered on basic operational demands such as interface clarity and guided prompts. Needs then shifted toward personalization, including adjustable voice speed and dialect support, and finally toward in-depth health value, such as tailored advice and real-time feedback. Concerns about information reliability and privacy persisted across all rounds. These findings guide phased HCI design that prioritizes simplicity, then personalization, with full-cycle privacy safeguards to support age-friendly GenAI health tools.