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

作为医院最大的劳动力队伍，注册护士对医院的整体稳定至关重要，在提供高质量的患者护理方面发挥着至关重要的作用。护理指导员负责培训和评估这支队伍。美国正面临护士严重短缺的问题，导致护理人员大量流失，对培训更多护士的需求日益增长。因此，护理指导员的供应也超过了需求。满足护士劳动力培训需求是一项重大挑战，并推动了护士劳动力培训中人类与技术伙伴关系变革机制的发展。为了应对这一挑战，该项目的总体目标是帮助护士指导员改善培训效果，同时腾出时间进行个性化指导，并提高护理人员培训计划的效率。为了实现这一目标，该项目将设计、开发和评估机器人智能教学辅助系统（RITAS）对未来护士劳动力培训的影响。通过虚拟智能和实体智能组件的共生，教学辅助系统将通过评估受训者的技能，将评估摘要报告给导师，并向受训者提供导师指导的辅导，帮助护理导师进行日常护理程序的培训。通过参与式设计机制，不同背景的护理教师和护士将从一开始就参与技术设计，塑造这些工具的使用，并告知未来在护理课程和更广泛的医疗保健中使用人工智能和机器人的努力。核心团队汇集了护理、护理教育、行为科学、机器人、人工智能、智能辅导和以人为本的计算方面的专业知识，以实现总体目标。项目顾问将贡献技术、护理、组织行为和管理以及学习科学专业知识。该项目包括三个并行和集成的轨道，以实现护士劳动力培训中人与技术伙伴关系的新机制。前两条轨道分别侧重于通过智能辅导和机器人技术的算法创新来设计和开发未来技术的虚拟和具体化组件。通过这些创新，该项目将为智能辅导机器人带来革命性的飞跃：RITAS将利用其传感器和化身来验证和提高学员的身体技能执行力，而不是仅仅依靠会话交互。这两个组成部分将共同协助护理程序的评估和培训。评估总结和辅导将使用语言和非语言机制进行人机交流。第三个轨道将衡量与未来技术部署和采用相关的培训、生产力和行为指标，例如机器人教学辅助对护理教师工作量和护士学员学习成果的影响。这些测量将通过在一家大型医院进行的护士劳动力培训活动中进行的广泛的人体受试者实验得出，该医院每年有超过1000名护士入职，并将其提炼成一个简明的护理理论，指导护理教育中采用技术。该项目还将开发基于空间的护理程序和培训环境模型。通过这些以工作者为中心的评估、理论和模型，该项目将为人类技术前沿的未来护理指导员的工作设计提供信息。该项目由人类技术前沿项目的未来工作支持，该项目支持多学科研究，以维持经济竞争力，促进工人福祉，终身和普及学习，提高生活质量，并阐明正在形成未来就业和工作的新兴社会和经济背景和创新驱动因素。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.