Helping the Helper: LLM-Assisted Problem Articulation for Older Adults Seeking Technology Support

Le résumé fourni par la source

Older adults often struggle to articulate technology support needs due to unfamiliar technical terminology and age-related cognitive changes. We explore how large language models (LLMs) can facilitate this problem articulation process. Through a diary study (n = 27), we identified four communication barriers in older adults' queries: verbosity, incompleteness, over-specification, and under-specification. To mitigate these barriers, we developed an LLM pipeline that clarifies context and paraphrases unstructured queries. LLM-rephrased queries significantly improved automated solution accuracy (69% vs. 35%). Furthermore, younger adults (n = 48) acting as technology helpers understood LLM-rephrased queries better (93.7% vs. 65.8%) and reported greater ease in providing support. Older adults (n = 34) also found the resulting solutions highly actionable (94.7%). Finally, we contribute the first synthetic dataset of older adults' technology assistance queries (STAQ). This work demonstrates how LLMs can improve technology support seeking for older adults by addressing age-related communication barriers.

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