An official website of the United States government
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.
more »
« less
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.
more »
« less
- 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
More Like this
-
-
The rapid evolution of modern manufacturing systems is driven by the integration of emerging metaverse technologies such as artificial intelligence (AI), digital twin (DT), and different forms of extended reality (XR) like virtual reality (VR), augmented reality (AR), and mixed reality (MR). These advances confront manufacturing workers with complex and evolving environments that demand digital literacy for problem solving in the future workplace. However, manufacturing industry faces a critical shortage of skilled workforce with digital literacy in the world. Further, global pandemic has significantly changed how people work and collaborate digitally and remotely. There is an urgent need to rethink digital platformization and leverage emerging technologies to propel industrial evolution toward human-centered manufacturing metaverse (MfgVerse). This paper presents a forward-looking perspective on the development of MfgVerse, highlighting current efforts in learning factory, cognitive digital twinning, and the new sharing economy of manufacturing-as-a-service (MaaS). MfgVerse is converging into multiplex networks, including a social network of human stakeholders, an interconnected network of manufacturing things or agents (e.g., machines, robots, facilities, material handling systems), a network of digital twins of physical things, as well as auxiliary networks of sales, supply chain, logistics, and remanufacturing systems. We also showcase the design and development of a virtual learning factory for workforce training. Finally, future directions, challenges, and opportunities are discussed for human-centered manufacturing metaverse. We hope this work helps stimulate more comprehensive studies and in-depth research efforts to advance MfgVerse technologies.more » « less
-
This study aimed to investigate the key technical and psychological factors that impact the architecture, engineering, and construction (AEC) professionals’ trust in collaborative robots (cobots) powered by artificial intelligence (AI). This study seeks to address the critical knowledge gaps surrounding the establishment and reinforcement of trust among AEC professionals in their collaboration with AI-powered cobots. In the context of the construction industry, where the complexities of tasks often necessitate human–robot teamwork, understanding the technical and psychological factors influencing trust is paramount. Such trust dynamics play a pivotal role in determining the effectiveness of human–robot collaboration on construction sites. This research employed a nationwide survey of 600 AEC industry practitioners to shed light on these influential factors, providing valuable insights to calibrate trust levels and facilitate the seamless integration of AI-powered cobots into the AEC industry. Additionally, it aimed to gather insights into opportunities for promoting the adoption, cultivation, and training of a skilled workforce to effectively leverage this technology. A structural equation modeling (SEM) analysis revealed that safety and reliability are significant factors for the adoption of AI-powered cobots in construction. Fear of being replaced resulting from the use of cobots can have a substantial effect on the mental health of the affected workers. A lower error rate in jobs involving cobots, safety measurements, and security of data collected by cobots from jobsites significantly impact reliability, and the transparency of cobots’ inner workings can benefit accuracy, robustness, security, privacy, and communication and result in higher levels of automation, all of which demonstrated as contributors to trust. The study’s findings provide critical insights into the perceptions and experiences of AEC professionals toward adoption of cobots in construction and help project teams determine the adoption approach that aligns with the company’s goals workers’ welfare.more » « less
-
Online education is on the rise in the US and abroad and provides a convenient form of knowledge transfer to people who cannot be full- and or even part-time students at community colleges or universities. This factor impacts industry representatives, displaced workers, and low-income learners. Usually, online education consists of online lectures and/or tutorials designed so users can comprehend the studied subject. The missing piece of online education is the lack of hands-on activities. To address this issue, Michigan Tech and West Shore Community College collaborate on researching, developing, and implementing a State-of-the-Art Teleoperated Robotic Workcell (TRW) to enable enhanced remote training for industrial robots. The system is designed to provide training opportunities to college students, industry representatives, and displaced workers wishing to retool their skills and become more competitive in the job market.more » « less
-
With the construction sector primed to incorporate such advanced technologies as artificial intelligence (AI), robots, and machines, these advanced tools will require a deep understanding of human–robot trust dynamics to support safety and productivity. Although other disciplines have broadly investigated human trust-building with robots, the discussion within the construction domain is still nascent, raising concerns because construction workers are increasingly expected to work alongside robots or cobots, and to communicate and interact with drones. Without a better understanding of how construction workers can appropriately develop and calibrate their trust in their robotic counterparts, the implementation of advanced technologies may raise safety and productivity issues within these already-hazardous jobsites. Consequently, this study conducted a systematic review of the human–robot trust literature to (1) understand human–robot trust-building in construction and other domains; and (2) establish a roadmap for investigating and fostering worker–robot trust in the construction industry. The proposed worker–robot trust-building roadmap includes three phases: static trust based on the factors related to workers, robots, and construction sites; dynamic trust understood via measuring, modeling, and interpreting real-time trust behaviors; and adaptive trust, wherein adaptive calibration strategies and adaptive training facilitate appropriate trust-building. This roadmap sheds light on a progressive procedure to uncover the appropriate trust-building between workers and robots in the construction industry.more » « less
