AI Technicians: Developing Rapid Occupational Training Methods for a Competitive AI Workforce
This program is tentative and subject to change.
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 (maintainers, integrators) and users (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 project which is a unique collaboration between a large governmental organization and an R1 university in the United States 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 trained 60 AI technicians, and we found that regular frequent updates to the training are necessary since the adoption of AI is evolving rapidly. Hence, a tight collaboration among the stakeholders is essential for successful development and maintenance of the training for the evolving role. Our findings can be leveraged by large organizations that face the challenge of developing a competent AI workforce as well as educators and researchers engaged in solving the challenge.
This program is tentative and subject to change.
Thu 27 FebDisplayed time zone: Eastern Time (US & Canada) change
13:45 - 15:00 | |||
13:45 18mTalk | AI Technicians: Developing Rapid Occupational Training Methods for a Competitive AI Workforce Papers Jaromir Savelka Carnegie Mellon University, Can Kultur Carnegie Mellon University, Arav Agarwal Carnegie Mellon University, Christopher Bogart Carnegie Mellon University, Heather Burte Carnegie Mellon University, Adam Zhang Carnegie Mellon University, Majd Sakr Carnegie Mellon University | ||
14:03 18mTalk | Analysis of Generative AI Policies in Computing Course Syllabi Papers Areej Ali George Mason University, Aayushi Hingle George Mason University, Umama Dewan George Mason University, Nora McDonald George Mason University, Aditya Johri George Mason University, USA | ||
14:22 18mTalk | Does Reducing Curricular Complexity Impact Student Success in Computer Science? Papers Sumukhi Ganesan Khoury College of Computer Sciences, Albert Lionelle Khoury College of Computer Sciences, Northeastern University, Catherine Gill Northeastern University, Carla Brodley Northeastern University, Center for Inclusive Computing | ||
14:41 18mTalk | Moving What's in the CS Curriculum Forward: A Proposition to Address Ten Wicked Curricular Issues Papers |