Collaborative Research [FW-HTF-RM]: The Future of Nurse Training: Robotic Teaching Assistant Systems for Nursing Instructors

William Marsh Rice University, Houston TX

Investigators

Abstract

As the largest hospital workforce, registered nurses are essential to the overall stability of hospitals and play a vital role in delivering quality patient care. Nursing instructors are responsible for training and assessments of this workforce. The U.S. is experiencing a dire shortage of nurses, leading to significant turnover of nursing staff and an ever-growing need to train more nurses. As a result, the supply for nursing instructors too has outstripped demand. Meeting the nurse workforce training needs is a significant challenge and motivates development of transformative mechanisms for human-technology partnership in nurse workforce training. To address this challenge, the project's overall goal is to help nurse instructors improve training outcomes while freeing up their time for personalized instruction and enhance efficiency of nursing workforce training programs. To achieve this goal the project will design, develop, and evaluate the impacts of Robotic Intelligent Teaching Assistant Systems (RITAS) on the future of nurse workforce training. Through a symbiosis of virtual and embodied intelligence components, the teaching assistance system will assist nursing instructors in training of routine nursing procedures by assessing trainees' skills, reporting the assessment summaries to instructors, and delivering instructor-guided tutoring to trainees. Through mechanisms for participatory design, nursing instructors and nurses of varying backgrounds will be involved in the technology design from the very beginning, shaping the use of such tools and informing future efforts on the use of artificial intelligence and robotics in nursing curriculum and more broadly healthcare. The core team brings together expertise in nursing, nursing education, behavioral science, robotics, artificial intelligence, intelligent tutoring, and human-centered computing to achieve the overarching goals. Project advisors will contribute technology, nursing, organizational behavior and management, and learning sciences expertise. The project includes three concurrent and integrated tracks to realize novel mechanisms for human-technology partnership in nurse workforce training. The first two tracks focus on design and development of the virtual and embodied components of the future technology, respectively, through algorithmic innovations in intelligent tutoring and robotics. Through these innovations, the project will bring a transformative leap in intelligent tutoring robots: instead of relying solely on conversational interaction, RITAS will utilize its sensors and embodiment to verify and improve trainees' physical skill execution. Together, the two components will assist with assessment and training of nursing procedures. Assessment summaries and tutoring will be delivered using both verbal and non-verbal mechanisms for human-robot communication. The third track will measure training, productivity, and behavioral metrics that are relevant to deployment and adoption of the future technology, such as the impact of robotic teaching assistance on nursing instructors' workload and nurse trainees' learning outcomes. These measurements will be derived through extensive human subject experiments conducted within ongoing nurse workforce training activities at a large hospital, which onboards over thousand nurses per year, and distilled into a concise nursing theory guiding technology adoption in nursing education. The project will also develop spatially-grounded models of nursing procedures and training environments. Through these worker-centered assessments, theories, and models, the project will inform the work design of future nursing instructors at the human-technology frontier. This project is supported by the Future of Work at the Human-Technology Frontier program which supports multi-disciplinary research to sustain economic competitiveness, promote worker well-being, lifelong and pervasive learning, and quality of life, and illuminate the emerging social and economic context and drivers of innovations that are shaping the future of jobs and work. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.