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

Abstract: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.

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)

arXiv:2601.10018 [cs.HC]

(or arXiv:2601.10018v2 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2601.10018 Focus to learn more arXiv-issued DOI via DataCite

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