The broader impact/commercial potential of this I-Corps project will be to define the requirements needed to develop technology to train the next generation workforce and narrow the skills gap in advanced manufacturing. By the year 2025, the Manufacturing Institute estimates that the US manufacturing sector will demand 2 million more jobs be filled by the labor force. Traditional schools and apprenticeships alone cannot meet the increasing demand for skilled labor. Small- to medium-sized manufacturers (SMMs) cite a “skills gap†in advanced manufacturing while receiving more orders than they can fill. The SMMs seek to hire more workers to increase output, but they cannot find enough workers with the necessary skills. Currently, the most successful solution to these problems focuses on providing SMMs with workers just out of high school. However, training costs and the time spent training workers are significant barriers. This proposed technology aims to help companies train current workers with the skills necessary to compete in the modern manufacturing economy.
This I-Corps project is based on the development of an autonomous robotic system that embodies interactive learning for workforce training. The proposed robotic technology is an instructor that delivers educational materials from a remote online learning system. The technology also is a machine that demonstrates physical actions and that physically interacts with learners. By streamlining instruction with physical demonstration and interaction, the team establishes a unique pedagogical approach that may engage a broad population, including those having no engineering training. To accomplish this, a software architecture has been developed to control the robot and facilitate robust teaching. The software is cloud-based and modularly designed so that different industrial robots may be easily exchanged with minimal reconfiguration. In addition, this methodology represents the first educational robot that customizes its curriculum in real-time based on user learning style and background by collecting data on the learner as they progress through the course and making decisions accordingly.
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.
