Multi-organizational, interdisciplinary work advances workforce development and retraining
ADAPT faculty Sam Spiegel, Xiaoli Zhang and Craig Brice are leading teams recently awarded National Science Foundation grants to
- develop online learning opportunities that empower the workforce of today and tomorrow to better harness the power of data in advanced manufacturing
- develop artificial intelligence (AI)-based tools to help train and retrain workers preparing for or displaced by the proliferation of automation in mining, metallurgy and manufacturing
Workforce Development
Mines is one of five U.S. universities to receive NSF PEER (Production Engineering Education and Research) awards, designed to advance the workforce for future needs through a convergent science approach that integrates knowledge, methodology and expertise from different disciplines.
The learning platforms developed by PEER awardees will be online courseware, an increasingly prevalent educational tool that has received comparatively little study due to its recent emergence. In addition to developing new platforms, PEER awardees will study the effectiveness of online courseware, finding out what connects best with learners at various levels of skills in several different environments.
NSF’s PEER program is made possible in part by a $10 million gift from The Boeing Company. The Mines team is led by Sam Spiegel, director of the Trefny Innovative Instruction Center, and includes Brice, director of the Advanced Manufacturing program, and other faculty from Mines, Red Rocks Community College and Colorado Community College Online. The team will focus its attention on data science in advanced manufacturing by developing adaptive learning progressions that assess and advance current workers as well as students at community colleges and universities. This program builds from the Trefny–ADAPT partnership established for additive manufacturing workforce development, outreach and training through the Department of Defense Office of Economic Adjustment (DOD-OEA) Mountain West Advanced Manufacturing Network program, which began in January 2017 and is now in phase II.
Displaced Worker Retraining
Another Mines team led by Xiaoli Zhang, associate professor of mechanical engineering, was one of 43 awardees in NSF’s pilot Convergence Accelerator program. The awards, totaling $39 million, will support projects across the country that will find new ways to apply Big Data to science and engineering and create technologies that can enhance the lives of American workers.
The Mines team will focus on retraining workers displaced by changes in industry attributed to the proliferation of advanced manufacturing. “The fourth industrial revolution is here,” said Zhang, “and while AI is assisting people in many ways, it and other advanced technologies may replace humans in some job scenarios.”
This “fourth industrial revolution” is expected to impact tens of thousands of jobs—mostly engineers—between 2020 and 2060. The Mines team will create proactive AI-enabled tools aimed at curbing this looming workforce displacement crisis by developing algorithms that will automatically assess skills and gaps, then generate individualized retraining plans to quickly prepare workers for new jobs.
Traditional retraining programs can be time-consuming and costly to develop. They also tend to treat all trainees the same, without regard to their age, experience or specialized knowledge.
The work in process by Zhang as principal investigator, supported by Brice, Spiegel, Sridhar Seetharaman (ADAPT Board Member), Aaron Stebner (ADAPT Executive Director), and others, aims to create an AI-enabled approach to training that enables fast, fair, cost-effective and customized training at scale. Each worker will benefit from a tailored training approach based on their skills, interests, experience and best fit for a new role.
For more information on the PEER project, see the Mines and NSF announcements.
For more information on the Convergence Accelerator award, see the NSF announcement and the Mines proposal abstract.