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With the fast development of Industry 4.0, the ways in which manufacturing workers handle machines, materials, and products also change drastically. Such changes post several demanding challenges to the training of future workforce. First, personalized manufacturing will lead to small batch and fast changing tasks. The training procedure must demonstrate agility. Second, new interfaces to interact with human or robots will change the training procedure. Last but not least, in addition to handling the physical objects, a worker also needs to be trained to digest and respond to rich data generated at the manufacturing site. To respond to these challenges, in this paper we describe the design of an AI-assisted training platform for manufacturing workforce. The platform will collect rich data from both machines and workers. It will capture and analyze both macro and micro movement of trainees with the help of AI algorithms. At the same time, training for interaction with robot/cobot will also be covered. Mixed reality will be used to create in-situ experiences for the trainee.
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This content will become publicly available on April 22, 2026
Interactive AI Tutors for Training the Workforce of the Future
The need to train new workers effectively and upskill the existing workforce is a challenge faced by almost every industry across the globe. The healthcare industry, in particular, is confronting a crisis. The World Health Organization (WHO) projects a shortage of 10 million healthcare workers by 2030. However, according to the Future of Jobs Report by the World Economic Forum, only half of the workers have access to training and learning opportunities. To sustain a resilient workforce and to protect the health of the world’s population, my thesis looks at using AI and robots to accelerate human learners’ acquisition of workforce skills. Specifically, I develop novel Explainable AI (XAI) algorithms to automate training to enable workers to collaborate with autonomous robots - a trend that is fast-growing. I also use statistical models to model human learner cognitive processes to create Human-Robot Interaction (HRI) systems to generate effective instructions tailored to individual learners. In addition to driving technical advances, my research is having a positive societal impact. I collaborate with Houston Methodist Hospital to create a first-of-its-kind robotic tutor for clinical nursing education to reduce healthcare-associated infections.
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- Award ID(s):
- 2326390
- PAR ID:
- 10638064
- Editor(s):
- Unhelkar, Vaibhav
- Publisher / Repository:
- Rice University
- Date Published:
- Subject(s) / Keyword(s):
- Artificial Intelligence Robotics Intelligent Tutoring Explainable AI Human-Robot Interaction
- Format(s):
- Medium: X
- Institution:
- Rice University
- Sponsoring Org:
- National Science Foundation
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