FW-HTF-P: Training an Agile, Adaptive Workforce for the Future of Manufacturing with Intelligent Augmented Reality

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The future of the American manufacturing workforce faces a perfect storm of challenges: (1) a shortage of workers due to the retirement of the Baby Boom generation, (2) a shifting skillset due to the introduction of advanced technologies, and (3) a lack of understanding and appeal of manufacturing jobs among younger cohorts. Consequently, over 2.4 million U.S. manufacturing jobs are anticipated to be left unfilled by 2030 with a projected cost of $2.5 trillion on the U.S. manufacturing GDP. Augmented reality (AR) has been recently adopted for experiential training and upskilling of manufacturing workers. AR is proven to reduce new-hire training time by 50% through spatiotemporal alignment of instructions with worker experience. However, evidence suggests that overreliance of workers on AR scaffolds can cause brittleness of knowledge and deteriorate performance in adapting to novel situations. This project will investigate if and how AR can help manufacturing workers develop agility and adaptability on the shop floor while avoiding the risks associated with dependence on technology and stifled innovation. A new intelligent AR system will enable dynamic adjustment of AR instructions to worker task performance and enhance their ability to master complex tasks such as assembly and maintenance. This research will serve the national priority for rapid and lifelong upskilling of manufacturing workforce, especially underrepresented and under-served minority groups.A convergent team of learning scientists, labor economists, cognitive psychologists, computer scientists, and manufacturing engineers will investigate three fundamental research thrusts: (1) Future work: Labor market analyses of changes in employer skill requirements will be conducted to understand the degree to which AR technologies have been introduced in the U.S. and the skillsets workers will need in future factories. (2) Future technology: An intelligent AR system will be devised to understand, predict, and guide the behavior of AR-supported workers through adaptive scaffolding of instructions to their performance and level of expertise. (3) Future worker: Hypothesis-driven human-subjects research will be conducted to understand the impacts of adaptive AR scaffolds on worker performance, cognitive load, and learning. The overarching goal of this research is to balance the efficiency and innovation of future manufacturing workers by improving their ability to transfer the acquired knowledge and skills to new situations on the shop floor. Experts from industry, government, and academia will be convened in a multidisciplinary workshop to illuminate the potentials and risks of AR technology for training future workforce and bridging the skills gap in manufacturing.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.