While high-quality technology support can assist older adults in using digital applications, many struggle to articulate their issues due to unfamiliarity with technical terminology and age-related cognitive changes. This study examines these communication challenges and explores AI-based approaches to mitigate them. We conducted a diary study with English-speaking, community-dwelling older adults to collect asynchronous, technology-related queries and used reflexive thematic analysis to identify communication barriers. To address these barriers, we evaluated how foundation models can paraphrase older adults' queries to improve solution accuracy. Two controlled experiments followed: one with younger adults evaluating AI-rephrased queries and another with older adults evaluating AI-generated solutions. We also developed a pipeline using large language models to generate the first synthetic dataset of how older adults request tech support (OATS). We identified four key communication challenges: verbosity, incompleteness, over-specification, and under-specification. Our prompt-chaining approach using the large language model, GPT-4o, elicited contextual details, paraphrased the original query, and generated a solution. AI-rephrased queries significantly improved solution accuracy (69% vs. 46%) and Google search results (69% vs. 35%). Younger adults better understood AI-rephrased queries (93.7% vs. 65.8%) and reported greater confidence and ease. Older adults reported high perceived ability to answer contextual questions (89.8%) and follow solutions (94.7%), with high confidence and ease. OATS demonstrated strong fidelity and face validity. This work shows how foundation models can enhance technology support for older adults by addressing age-related communication barriers. The OATS dataset offers a scalable resource for developing equitable AI systems that better serve aging populations.

翻译：尽管高质量的技术支持能够协助老年人使用数字应用程序，但许多老年人因不熟悉技术术语和与年龄相关的认知变化而难以清晰表述问题。本研究探讨了这些沟通挑战，并探索了基于人工智能的缓解方法。我们通过日记研究收集了英语社区居住老年人的异步技术相关查询，并采用反思性主题分析法识别沟通障碍。为应对这些障碍，我们评估了基础模型如何通过转述老年人查询来提高解决方案的准确性。随后进行了两项对照实验：一项由年轻成年人评估人工智能转述的查询，另一项由老年人评估人工智能生成的解决方案。我们还开发了基于大语言模型的流程，构建了首个关于老年人技术求助方式的合成数据集（OATS）。我们识别出四大关键沟通挑战：冗长性、不完整性、过度具体化和欠具体化。采用大语言模型GPT-4o的提示链方法能够提取上下文细节、转述原始查询并生成解决方案。人工智能转述的查询显著提高了解决方案准确率（69% vs. 46%）和谷歌搜索结果质量（69% vs. 35%）。年轻成年人能更好地理解人工智能转述的查询（93.7% vs. 65.8%），并表现出更高的信心和操作便利性。老年人对回答上下文问题的感知能力（89.8%）和执行解决方案的能力（94.7%）均表现出较高水平，同时具备高信心度和易用性。OATS数据集展现出良好的保真度和表面效度。本研究表明基础模型可通过解决年龄相关沟通障碍来增强老年人的技术支持。OATS数据集为开发更公平、更适应老龄化人口需求的人工智能系统提供了可扩展资源。