Empowering Older Adults in Digital Technology Use with Foundation Models

2026-01-15

AI Summary

This study addresses the communication barriers older adults face when seeking digital technology support, stemming from unfamiliarity with technical terminology and age-related cognitive changes that impede their ability to articulate needs accurately. To mitigate these challenges, the authors propose a prompt-chain-based restatement mechanism leveraging GPT-4o to identify and alleviate four canonical types of communication breakdowns through contextual enrichment and semantic restructuring of user requests. The work introduces OATS, the first high-fidelity synthetic dataset of older adults’ technology help-seeking utterances, and validates the approach via controlled experiments. Results demonstrate that AI-mediated restatements significantly improve support accuracy—increasing solution correctness from 46% to 69% and search result relevance from 35% to 69%. Moreover, older adults exhibited high acceptance rates, with 89.8% endorsing AI-guided questioning and 94.7% approving the generated solutions.

Technology Category

• Humans and AI: AI for Accessibility
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Application Domains: Humanities & Computational Social Science

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• older adults
• digital technology
• communication barriers
• technology support
• foundation models

Innovation

• foundation models
• prompt chaining
• older adults
• OATS dataset
• AI-mediated communication