The Differential Effects of Agreeableness and Extraversion on Older Adults' Perceptions of Conversational AI Explanations in Assistive Settings

TL;DR

This study investigates how agreeableness and extraversion in a conversational AI affect older adults' perceptions, revealing personality influences empathy and likeability but not perceived intelligence, with environmental explanations being more effective in emergencies.

Contribution

It provides empirical evidence on how agent personality impacts user perceptions and highlights the importance of context-sensitive, personality-aware AI design for older adults.

Findings

High agreeableness increased empathy perceptions.

Low agreeableness reduced likeability.

Environmental explanations outperformed conversational history explanations in emergencies.

Abstract

Large Language Model-based Voice Assistants (LLM-VAs) are increasingly deployed in assistive settings for older adults, yet little is known about how an agent's personality shapes user perceptions of its explanations. This paper presents a mixed factorial experiment (N=140) examining how agreeableness and extraversion in an LLM-VA ("Robin") influence older adults' perceptions across seven measures: empathy, likeability, trust, reliance, satisfaction, intention to adopt, and perceived intelligence. Results reveal that high agreeableness drove stronger empathy perceptions, while low agreeableness consistently penalized likeability. Importantly, perceived intelligence remained unaffected by personality, suggesting that personality shapes sociability without altering competence perceptions. Real-time environmental explanations outperformed conversational history explanations on five measures, with advantages concentrated in emergency contexts. Notably, highly agreeable participants were especially critical of low-agreeableness agents, revealing a user-agent personality congruence effect. These findings offer design implications for personality-aware, context-sensitive LLM-VAs in assistive settings.