Unremarkable to Remarkable AI Agent: Exploring Boundaries of Agent Intervention for Adults With and Without Cognitive Impairment

TL;DR

This study explores social boundaries and design considerations for AI agents supporting older adults and caregivers, highlighting acceptance concerns and the potential for agents to transition from unremarkable to remarkable in their support roles.

Contribution

It provides insights into social barriers and proposes design opportunities for AI agents to effectively support aging populations with cognitive decline.

Findings

Older adults worry about dementia risk from agents

Early-stage cognitive decline individuals want agents to ease caregiver burden

Agents that know users well could advocate for their needs

Abstract

As the population of older adults increases, there is a growing need for support for them to age in place. This is exacerbated by the growing number of individuals struggling with cognitive decline and shrinking number of youth who provide care for them. Artificially intelligent agents could provide cognitive support to older adults experiencing memory problems, and they could help informal caregivers with coordination tasks. To better understand this possible future, we conducted a speed dating with storyboards study to reveal invisible social boundaries that might keep older adults and their caregivers from accepting and using agents. We found that healthy older adults worry that accepting agents into their homes might increase their chances of developing dementia. At the same time, they want immediate access to agents that know them well if they should experience cognitive decline. Older adults in the early stages of cognitive decline expressed a desire for agents that can ease the burden they saw themselves becoming for their caregivers. They also speculated that an agent who really knew them well might be an effective advocate for their needs when they were less able to advocate for themselves. That is, the agent may need to transition from being unremarkable to remarkable. Based on these findings, we present design opportunities and considerations for agents and articulate directions of future research.