Situated Understanding of Errors in Older Adults’ Interactions with Voice Assistants: A Month-Long, In-Home Study

Our work addresses the challenges older adults face with commercial Voice Assistants (VAs), notably in conversation breakdowns and error handling. Traditional methods of collecting user experiences—usage logs and post-hoc interviews—do not fully capture the intricacies of older adults’ interactions with VAs, particularly regarding their reactions to errors. To bridge this gap, we equipped 15 older adults’ homes with smart speakers integrated with custom audio recorders to collect “in-the-wild” audio interaction data for detailed error analysis. Recognizing the growing use of Large Language Models (LLMs) to enhance capabilities of voice assistants, our study also explored how this integration of LLMs changes older adults’ interaction dynamics, specifically during errors. Midway through our study, we deployed ChatGPT-powered VA to investigate its efficacy for older adults. Our research suggests that while technical improvements—such as leveraging vocal and verbal responses combined with LLMs’ contextual capabilities—can enhance error prevention and management in VAs, interaction-level challenges still remain, particularly those unique to older adults. We propose design considerations to better align future VAs with older adults’ expectations and lived experiences.