Convergence Accelerator Phase I (RAISE): Skill-LeARn: Affordable Augmented Reality Platform for Scaling Up Manufacturing Workforce, Skilling, a

NSF融合加速器支持以团队为基础的多学科努力，解决国家重要性的挑战，并在不久的将来显示出可交付成果的潜力。“融合加速器”一期项目的广泛影响/潜在效益将立即适用于制造业，对经济和就业产生乘数效应。自动化正在将美国的劳动力分成两个世界：相对较少的受过高等教育的专业人士获得较高的工资，而受教育程度较低的工人则留在支付低工资的企业。尽管我们在技术上取得了突破，但整体生产率增长缓慢，部分原因是劳动力缺乏关键的新能力，如程序性教学学习、数字流畅性和其他基本技能。当前和未来的劳动力需要为不断变化的文化做好准备。公司一直依靠古老的一对一工人学徒模式来培训他们的新员工。但是，最近对大规模和快速培训的需要在时间和费用方面造成了瓶颈，对中小型企业来说尤其如此。这个由机械和电气工程师、心理学家、计算机科学家和教育研究人员组成的研究团队将努力实现一个目标，即为（重新）培训劳动力创造一种可扩展的低成本解决方案。为了解决这一（再）技能挑战，我们建议效仿工人学徒制，为中小企业开发一种新的低成本和灵活的方式，让他们自己在增强现实（AR）中创作。通过将数字内容覆盖到实体世界，我们将解决中小企业继续面临的工人培训问题。我们计划将工人（再）技能问题分解为三类：创作：知识转移；培训-学习：消费的系统扩展；反馈：向用户提供主动反馈。增强现实已被证明是一种可靠的教学培训模式，可以提高速度和可靠性，最大限度地减少错误，并减少用户的认知负荷，特别是对于空间定位指令。由于高昂的间接成本、未知的回报以及缺乏设计、创建和维护AR内容所需的专业知识，中小企业通常不知道、不愿意和缺乏访问权限，从而使这项技术取得成功。我们建议开发技术，使中小企业的专家能够自己创造内容，并提供知识的顺利转移。创建AR的便利性，以及对编程专家的依赖的消除，将转化为AR开发时间和成本的减少。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 team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact/ potential benefit of this Convergence Accelerator Phase I project will be immediately applicable to the manufacturing sector, which has a multiplier effect on the economy and jobs. Automation is splitting the American labor force into two worlds: a relatively small number of highly educated professionals earning good wages, and less-educated workers who are left with businesses that pay low wages. Although we have had technology breakthroughs, the overall productivity growth is slow partly due to a workforce that lacks critical new competencies, such as procedural instruction learning, digital fluency, and other essential skills. The current and future workforce needs to be geared up for a culture of constant change. Companies have been relying on the age-old way of one-on-one Worker Apprenticeship model to train their new workforce. However, the recent need for larger scale and rapid training has created a bottleneck in terms of time and cost, especially for small and medium enterprises (SMEs). This research team comprising of mechanical and electrical engineers, psychologists, computer scientists, and education researchers will work toward accomplishing a goal of creating a scalable low-cost solution for (re)skilling the workforce. In order to address this (re)skilling challenge, we propose to emulate the worker apprenticeship and develop a new low-cost and flexible way for SMEs themselves to author in Augmented Reality (AR). By overlaying digital content over the physical world we will address issues of worker training that SMEs continue to face. We plan to break down the problem of worker (re)skilling into three categories: Authoring: knowledge transfer, Training-Learning: system scaling for consumption, and Feedback: providing active feedback to users. AR has shown to be a reliable mode of instructional training, improving speed and reliability, minimizing errors, and reducing the cognitive load of the user, especially for spatially situated instructions. SMEs are often unaware, reluctant, and lack access because of the high overhead costs, unknown returns, and lack of expertise involved in designing, creating, and maintaining the AR content for the success of this technology. We propose to develop technologies which enable experts in the SMEs to create content on their own and provide a smooth transfer of knowledge. The ease of creation of AR, and elimination of dependency on programming experts, will translate to a reduction in AR development time and cost.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.