CAREER: With Age Comes Wisdom: Leveraging Older Adults' Crystallized Decision-Making Abilities to Develop Adaptive Human-Automation Interfaces for Dynamic Environments

Purdue University, West Lafayette IN

Investigators

Abstract

Adults 65 years and older are now the fastest-growing age group worldwide. At the same time, artificial intelligence (AI) and automation continue to penetrate every aspect of human life. Yet despite decades of advances, current technologies in complex transportation, work, and healthcare environments fail to account for the wealth of knowledge that older adults accumulate through their life experiences, which often shapes how they interact with AI and automation. This research aims to overcome this challenge by employing a novel perspective that focuses on the cognitive abilities that improve with age as opposed to those that decline. Specifically, the project will build a framework to: (1) evaluate how older adults apply their crystallized knowledge to decide whether and when to use automation in safety-critical, dynamic settings; and, (2) develop customized decision support techniques that intelligently tailor information to this population's specific needs. Project outcomes will help to change the narrative on older populations from incapable to empowered, inform the design and capabilities of AI to better align with older adults’ mental models, promote increased technology usage, extend older individuals’ ability to safely and independently collaborate with sophisticated technologies in work, transportation, and leisure settings, and reduce overall technology training needs. A tightly integrated research and education program will engage and empower older adults as well as undergraduate, graduate, and underrepresented students through the development of an interactive online learning community and technology platform, community-based ‘design for aging’ research projects, and annual ‘digital design for aging’ competitions. Over the next several decades, the presence of autonomous systems will become even more widespread across many environments. This research will provide a fundamental understanding of how older adults’ prior knowledge and experiences shape their (intended) interactions with automation and incorporate this understanding into the design of emerging intelligent systems. The project has two major research thrusts: (1) identify demographic, behavioral, and performance indicators of application of crystallized knowledge that can predict automation use/disuse decisions in uncertain situations, and (2) develop an adaptive multimodal decision support system that provides older adults with real-time guidance on when to use automation to maximize safety and performance. A series of human-subject experiments will be conducted in driving, a complex task that older adults have years of experience performing and for which AI and automation promise to extend their ability to perform. Participatory design methods will be used, and data will be collected from younger and older age groups to examine how use of prior knowledge and decision-making strategies change as a function of age. Computational models will be constructed to predict automation use decisions and trigger customized feedback. This work will contribute to advancing theories, frameworks, and methods in several areas including aging, human-automation interaction, multimodal information presentation, and decision support systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.