Against the backdrop of population aging in China, the elderly group with mild cognitive impairment (MCI) faces dual challenges of cognitive decline and social isolation and the existing cognitive intervention products mostly adopt standardized game-based models, lacking personalized adaptability and natural interaction features. Therefore, the work aims to explore the application of large language models in the field of cognitive companionship for the elderly, and design a conversational AI-powered companion robot system to provide personalized and emotion-oriented cognitive stimulation services for the elderly with MCI. Based on the mapping relationship between cognitive stimulation theory and the capabilities of large language models, design principles and functional indicators were extracted through a comprehensive analysis of existing research on elderly users' demands and relevant literature. The Analytic Hierarchy Process (AHP) was adopted to construct a functional evaluation system, calculate weight values, and complete consistency checks to determine design priorities. The design practice was finalized through a series of processes including product positioning, system architecture design, functional module design, and interaction scheme design. A cognitive stimulation companion robot named "Echo" was established, which adopted a multi-turn dialogue memory mechanism and cognitive algorithms to achieve contextualized interaction and dynamic content generation. Randomized output was incorporated to maintain the diversity and challenge of stimulation content. The companion robot based on large language models breaks through the limitations of traditional cognitive training products, such as a single mode of stimulation, insufficient personalization and suboptimal interaction experience. It also achieves the deep integration of cognitive stimulation and emotional companionship, thereby providing more natural and humanized digital health services for the elderly with MCI, which serves as a valuable reference for the design of aging-friendly intelligent products.
Key words
large language models (LLMs) /
companion robots /
cognitive stimulation /
mild cognitive impairment (MCI) /
AHP
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