AI Technicians: Developing Rapid Occupational Training Methods for a Competitive AI Workforce
Jaromir Savelka, Can Kultur, Arav Agarwal, Christopher Bogart, Heather, Burte, Adam Zhang, Majd Sakr

TL;DR
This paper reports on a multi-year collaboration to develop and evaluate rapid training methods for AI technicians, addressing workforce gaps caused by the fast-paced evolution of AI technology and societal integration.
Contribution
It introduces a novel, collaborative approach to rapidly train AI technicians through iterative updates and stakeholder engagement, filling a critical workforce gap.
Findings
Trained 59 AI technicians over four years
Frequent training updates are essential for relevance
Stakeholder collaboration is key to success
Abstract
The accelerating pace of developments in Artificial Intelligence~(AI) and the increasing role that technology plays in society necessitates substantial changes in the structure of the workforce. Besides scientists and engineers, there is a need for a very large workforce of competent AI technicians (i.e., maintainers, integrators) and users~(i.e., operators). As traditional 4-year and 2-year degree-based education cannot fill this quickly opening gap, alternative training methods have to be developed. We present the results of the first four years of the AI Technicians program which is a unique collaboration between the U.S. Army's Artificial Intelligence Integration Center (AI2C) and Carnegie Mellon University to design, implement and evaluate novel rapid occupational training methods to create a competitive AI workforce at the technicians level. Through this multi-year effort we have…
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