FW-HTF-P: Optimizing Long-term Human Performance in Future Work

可穿戴系统，例如智能手表和智能眼镜，是未来工作的重要新兴技术。当这些系统与支持和指导绩效的自动化相结合时，它们为美国公司提供了巨大的机会，但也可能给他们的工人带来挑战。智能眼镜或头戴式显示器等可穿戴设备的光学透明显示器可以投射图像，同时仍然允许用户透过它们看到。由此产生的数字元素与人对现实世界的看法的混合是增强现实的一种形式。这种将新形式的信息与我们的环境进行动态集成，为实现工作场所的重大创新提供了机会。然而，使用这些设备在人们执行工作时不断无缝地提供信息和指导的能力将影响这些人的学习。一个关键的问题是，人们如何才能以一种让他们学习到提高绩效和成果、丰富工作生活所必需的方式，继续参与到任务中来。人们需要避免的是成为被动的、毫无疑问的外部支持接受者。这项研究解决了如何优化支持和援助的问题，以提高人的能力和改善直接的结果，同时仍然允许持久的，强大的学习，最终产生熟练的，有经验的工人。该奖项的核心目标是通过结合学习科学的原则，促进自动化支持在未来工作中的使用，以产生卓越的长期成果。这种方法将为确定外部援助何时以及如何向人类提供最佳援助提供基础。这项工作旨在建立一个新的理论框架，以及为从业者和开发人员提供指导方针。在一个典型家具组装任务的初步研究中，将使用增强现实技术来检查当前正在进行的性能和长期学习之间的相互作用。研究人员还将组织一个广泛的多学科研讨会，以促进未来对该领域的兴趣并发展新的合作。该计划项目的最终目标是发展必要的研究人员、研究基础设施和基础工作，以扩大研究未来技术、未来工人和未来工作的机会，达到FW-HTF全面研究计划的水平。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。

Wearable systems, for example smart watches and smart glasses, are important emerging technologies for the future of work. When such systems are coupled with automation that supports and guides performance, they offer tremendous opportunities for American companies, but also might create challenges for their workers. Optical, see-through displays from wearable devices like smart glasses or head-mounted displays can project images while still allowing the user to see through them. The resulting blend of digital elements with the person's view of the real world is a form of augmented reality. This type of dynamic integration of new forms of information with our environment provides an opportunity to enable major innovations in the workplace. However, the ability to use these devices to constantly and seamlessly inform and guide people as they perform their jobs will impact what these individuals are learning. A key issue is how people can remain engaged in the tasks in ways that allow them to learn what is necessary for them to enhance performance and outcomes and also enrich their working lives. What people need to avoid is becoming passive, unquestioning recipients of external support. This research tackles the issue of how to optimize support and assistance to increase human capacities and improve immediate outcomes, while still allowing the durable, robust learning that ultimately produces skilled, experienced workers.The core objective of this award is to advance the use of automation support for performance in future work by incorporating principles from the science of learning to produce superior long-term outcomes. This approach will provide a foundation for determining when and how external assistance would provide the best aid to humans. The work aims towards a new theoretical framework, as well as for guidelines for practitioners and developers. An initial study of performance in an exemplar furniture assembly task will use augmented reality to examine the interaction between current ongoing performance and long-term learning. The investigators will also organize a broad, multidisciplinary workshop to foster future interest in the area and develop new collaborations. The ultimate goal of this planning project is to develop the necessary research personnel, research infrastructure, and foundational work to expand the opportunities for studying future technology, future workers, and future work at the level of a FW-HTF full research proposal.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.