B2: Learning Environments with Augmentation and Robotics for Next-gen Emergency Responders (LEARNER)

NSF融合加速器支持以使用为灵感，以团队为基础，多学科的努力，解决国家重要性的挑战，并将在不久的将来产生对社会有价值的可交付成果。“融合加速器”第二阶段项目的更广泛影响和潜在社会效益将是产生基于技术的学习解决方案，这些解决方案可以支持和增强应急响应（ER）人员的绩效和安全。学术研究人员、核心技术开发人员、利益相关者以及由行业和政府领导人组成的咨询委员会将齐聚一堂，评估与使用人类增强技术（hat）相关的机遇和挑战，这些技术可以改变ER工作中基础的、以使用为灵感的解决方案寻找过程，并以一种可转移到其他工作环境的方式。这将涉及学习者（下一代应急响应人员的增强和机器人学习环境）的开发和评估，这是一个具有物理，增强和虚拟现实组件的混合现实学习环境，供用户学习有效地使用两个HAT类：动力外骨骼（EXO）和头戴式AR接口（AR）。我们的努力将有助于更好地概念化收敛工作，从而促进对人类与技术相互作用的理解；促进为人类及其环境量身定制、优化和持续适应的系统；以及教育和终身学习，以创造必要的劳动力。我们的努力也将成为其他研究团体的榜样，这些研究团体可以从跨越传统学科界限的工作中受益，包括工程、计算机科学、学习科学和人力资源开发。我们将与ER社区和更广泛的世界分享我们的方法、学习和发现，通过领导国家人才生态系统委员会（一个协作智库组织），支持利用先进技术进行劳动力学习的科学研究活动，并组织以技术驱动的学习科学和教育/人力资源发展为主题的Learn-X研讨会。我们将开发和评估学习者的功能原型——一个创新的、可访问的、模块化的、个性化的、可扩展的学习平台，以加速急诊室员工的技能和再培训，特别是在新兴的增强技术方面，这些技术具有巨大的潜力，可以改变工作的本质，提高效率、健康和福祉。学习者将提供一个独特的训练范例，将学习的生理、神经和行为标记结合到实时场景进化中。提出的虚拟和物理用户界面和交互技术将通过为ER仿真和与物理环境和工作工件同步的虚拟交互提供多感官方法来推进人机交互领域。此外，我们部署这些hat并开发有效学习平台的计划具有重大的变革潜力，因为exo和AR将使用户能够在个人和团队层面制定新的工作策略，从而使他们的新扩展的身体和感知能力成为可能。最后，我们的工作将通过创建一个可扩展和可复制的平台来推进学习，该平台将提高集成和采用创新和新兴HATs的速度，从而使不同工业部门的未来劳动力受益。我们的跨学科方法融合并增强了学习科学、计算机科学、虚拟和增强现实、人为因素、认知心理学和系统工程等学科的现有知识，以创建集成培训课程设计、创新和新兴技术实施以及新工作技术的学习者平台。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。

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future.The broader impact and potential societal benefits of this Convergence Accelerator Phase II project will be to generate technology-based learning solutions that can support and augment the performance and safety of emergency response (ER) personnel. Academic researchers, core-technology developers, stakeholders, and an advisory board constituted of leaders from industry and government will come together to assess opportunities and challenges related to the use of human augmentation technologies (HATs) that can transform the process of foundational, use-inspired solution-finding for ER work, and in a way that is transferable to other work contexts as well. This will involve the development and evaluation of LEARNER (Learning Environments with Augmentation and Robotics for Next-gen Emergency Responders), a mixed-reality learning environment with physical, augmented, and virtual reality components, for users to learn to work effectively with two HAT classes: powered exoskeletons (EXO) and head-worn AR interfaces (AR). Our effort will contribute to better conceptualize convergence work that can foster the understanding of reciprocal human-technology interactions; contribute to systems that are tailored, optimized, and continuously adapted for humans and their environments; and education and lifelong learning to create the requisite workforce. Our effort will also serve as a model for other research communities that can benefit from working across traditional disciplinary boundaries in engineering, computer science, learning sciences, and human resource development. We will share our methods, learnings and findings with the ER community and the wider world by leading a National Talent Ecosystem Council, a collaborative think-tank organization, to support scientific research activities on workforce learning with advanced technologies and organizing Learn-X symposiums on the topic of technology-driven advances in learning-sciences and educational/human resource development.We will develop and evaluate a functional prototype of LEARNER – an innovative accessible, modular, personalized, and scalable learning platform to accelerate skilling and reskilling of ER workers, particularly on nascent augmentation technologies that have significant potential to change the very nature of work and improve efficiency, health, and well-being. LEARNER will provide a unique training paradigm by incorporating physiological, neurological, and behavioral markers of learning into real-time scenario evolution. The proposed virtual and physical user interfaces and interaction techniques will advance the human-computer interaction field by providing a multisensory approach for ER simulation and synchronized virtual interactions with physical environments and work artifacts. Furthermore, our plan to field these HATs and develop an effective learning platform has significant transformative potential as EXOs and AR will enable users to formulate new work strategies at the individual and team levels enabled by their newly extended physical and perceptual capabilities. Finally, our work will advance learning by creating a scalable and replicable platform that will increase the speed of integration and adoption of innovative and emerging HATs that benefit the future workforce across diverse industrial sectors. Our transdisciplinary approach converges and enhances the existing knowledge from the disciplines of learning science, computer science, virtual and augmented realities, human factors, cognitive psychology, and systems engineering to create the LEARNER platform that integrates training course design, innovative and emerging technology implementation, and new techniques of 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.