As the global population ages, there is increasing need for accessible technologies that promote cognitive health and detect early signs of cognitive decline. This research demonstrates the potential for in-residence monitoring and assessment of cognitive health using large language model (LLM)-powered socially assistive robots (SARs). We conducted a 5-week within-subjects study involving 22 older adults in retirement homes to investigate the feasibility of LLM-powered SARs for promoting and assessing cognitive health. We designed tasks that involved verbal dialogue based on clinically validated cognitive tools. Our findings reveal improved task performance after three robot-administered sessions, with significantly more detailed picture descriptions, fewer word repetitions in semantic fluency, and reduced need for hints. We found that older adults were more socially engaged in robot-administered tasks compared to those administered by a human, and they accepted and were willing to engage with SARs in this context, which had not been tested before.
https://dl.acm.org/doi/10.1145/3706598.3713582
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)