Exploring AI Agents for Reminiscence Therapy in Long-Term Care

Older and younger adults in long-term care, particularly those with dementia or chronic physical conditions, often experience social isolation and cognitive under-stimulation due to limited opportunities for meaningful engagement. Reminiscence therapy is a highly effective approach to providing this stimulation, enhancing emotional well-being, memory recall, and social interaction. However, its implementation in care settings often depends on staff and family availability and resources, making individualised engagement inconsistent. AI-driven agents offer a potential solution by providing adaptive, interactive reminiscence experiences that encourage engagement and conversation. This study explores the potential of AI-driven agents for reminiscence therapy in long-term care facilities, focusing on residents with dementia and individuals in somatic care units. Our methodology was as follows: 1) We defined four hypotheses about the interaction between the AI-agent and the users. 2) We developed multiple variants of a functional app prototype to address these hypotheses: A web app integrating foundational models for conversational interactions, transcription, and text-to-speech. And an accompanying hardware configuration. 3) We conducted exploratory user testing with nine participants across different cognitive and physical conditions, including elderly individuals with dementia, younger individuals with dementia, and individuals in somatic care.To create personalised conversation experiences, we obtained background information about each resident from caregivers, including name, former residence, profession, and hobbies. This data was used to design customised conversation prompts and flows tailored to the residents’ individual life experiences. The system also featured wake-word and button activation and alternative avatar designs (human-like, abstract, and cartoon). Conversation flows were specifically designed to accommodate the needs of the user groups, incorporating simplified question structures to avoid overwhelming the residents, personalised prompts, and multimodal interaction options.To evaluate user interaction and accessibility, we made four different prototype versions, implementing variations in screen size, button placement, and interaction modalities. These physical prototypes allowed us to explore how hardware design influences usability and engagement for older adults with varying abilities. All participants engaged in basic conversational interactions with the AI companion, but individual comprehension levels varied due to speech issues, cognitive abilities, and other factors. Participants expressed a strong preference for simple voice-based interfaces, although a simple button-based activation method showed better usability than wake-word initiation.