University of Massachusetts Lowell
Our current research: reviewed prior literature on AI systems AND older adults. This encompassed
examining existing studies to identify trends, strengths, and limitations, as well as to understand the
context in which AI systems have been applied in older adults with Mild Cognitive Impairments. By
analyzing the past research landscape, we aimed to synthesize the knowledge accumulated thus far and
reveal potential gaps in the field.
We assessed the type of AI systems used in prior literature as it was crucial in understanding the range of
technologies and approaches employed to support older adults. By classifying and analyzing these AI
systems, we could determine which systems have been most effective in addressing the challenges faced
by older adults in their daily lives.
We also analyzed the metrics and measures used to evaluate activities of daily living (ADLs), instrumental
activities of daily living (IADLs), and enhanced activities of daily living (EADLs) as well as other application
types and tasks found in the prior literature. To evaluate the effectiveness of AI systems in supporting
older adults with these activities, it is crucial to examine the metrics and measures used in prior
literature. Some of the metrics and measures include: - task completion, time efficiency, user
satisfaction, error rates, quality of assistance, et cetera. This analysis can help identify areas for
improvement, reveal best practices, and guide the development of future AI systems tailored to the
unique needs of older adults.
In addition, our research explored measurement spaces such as system performance, human-AI
interaction, and care outcomes as understanding these aspects is essential for evaluating the impact of
AI systems on older adults' lives.
Last but not least, we designed a database that breaks down the literature into various themes, codes,
and the Arenas identified by the AI Caring team such as Adaptive Longitudinal Understanding, Voice,
Language, and Conversational Assistance for Daily Routines, Kitchen, Cooking, & Meals, Physical
Wellness & Exercise, and Navigating Socially Complex Situations & Care Coordination. The database
serves as a valuable resource for future research and development, enabling the identification of
promising areas and the recognition of trends in AI systems for older adults.