Convergence Accelerator Phase I(RAISE): Rapid Dissemination of AI Microcredentials through Hands-On Industrial Robotics Education (RD-AIM-HIRE)

NSF融合加速器支持基于团队的多学科努力，以应对国家重要性的挑战，并在不久的将来显示出交付成果的潜力。这一融合加速器第一阶段项目的更广泛的影响/潜在好处是促进社会学、人机交互、微型认证、软件工程和改进科学的文献和方法的融合，以改进协作开发和交付流程，以培训能够满足当前和即将过渡到“工业4.0”范式的机器人技术人员，在“工业4.0”范式中，工厂车间被改造成由专门的人工智能(AI)软件控制的联网网络物理系统。技术人员在安装和维护先进制造系统方面仍然至关重要，但他们必须了解这些系统，以便有效地对其进行故障排除。良好的培训既是真实的，也是始终如一的。要做到这一点，需要课程设计者、学科专家、教育从业者和许多其他利益攸关方之间的协调。从课程发展的角度来看，这一努力被认为是一个“产品设计”问题，从组织政策的角度来看，这是一个“组织协调”问题。我们的方法在跨学科的努力中将这两个角度联系起来，以产生一种技术培训工具，可以跟上机器学习和人工智能等领域的快速发展。这个融合加速器第一阶段项目采用设计研究方法，将组织内部和产品外部的方法融合到一个高效的企业中。我们的项目使用设计和改进科学方法来回答以下问题：(I)如何为几乎没有STEM背景的技术员受训人员设计有效和可交付的机器学习(ML)课程；以及(Ii)如何在考虑到广泛的利益相关者的情况下，围绕这一目标设计富有成效的协作例程。我们从“准备就绪”的课程理念和一个经验丰富的生产团队开始，他们使用以人为中心的设计(HCD)技术来设计可用的、教学上有效的活动。除了与人工智能主题专家进行作者咨询外，HCD还涉及收集关于教师、学习者及其扩展学习环境的数据，包括行政和社会方面。在此过程中，我们将生产团队与其他利益相关者的协作模式问题化为问题，确定改进的常规以加速协作的联合工作。该项目的这一阶段提供了关于如何通过增强的协作例程加速课程设计的知识，关于如何将ML/AI整合到技术人员培训中的知识，面向非四年期受众的ML/AI课程原型，以及对试点参与者的直接培训影响。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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 is to facilitate convergence of literature and methodologies from sociology, human-computer interaction, microcredentialing, software engineering, and improvement science to improve collaborative development and delivery processes for training robotics technicians who can meet the needs of American manufacturers both today and during the impending transition to an "Industry 4.0" paradigm in which factory floors are transformed into networked cyber-physical systems controlled by specialized artificial intelligence (AI) software. Technicians will remain critical in the installation and maintenance of advanced manufacturing systems, but they must understand such systems in order to troubleshoot them effectively. Good training is both authentic and consistently deliverable. Achieving this requires coordination between curriculum designers, subject matter experts, educational practitioners, and many other stakeholders. The endeavor has been approached alternately as a "product design" problem from the curriculum development perspective, or an "organizational coordination" problem from an organizational policy perspective. Our approach connects these two perspectives in a transdisciplinary effort to produce a technical training apparatus that can keep pace with rapid advancement in fields like machine learning and AI.This Convergence Accelerator Phase I project takes a design research approach to fusing the organizations-inward and product-outward approaches into a single efficient enterprise. Our project employs design and improvement science methods to answer questions about (i) How to design effective and deliverable machine learning (ML) curriculum for technician trainees with little or no STEM background; and (ii) How to design productive collaborative routines around this objective, given the wide variety of stakeholders. We start with a "shovel-ready" curriculum concept and an experienced production team that uses human-centered design (HCD) techniques to design usable, pedagogically effective activities. In addition to authorial consultation with AI subject matter experts, HCD involves data collection on instructors, learners, and their extended learning contexts including administrative and social aspects. We problematize collaboration patterns of the production team with other stakeholders during this process, identifying improved routines to accelerate the collaborative's joint work. This phase of the project produces knowledge of how to accelerate curriculum design through enhanced collaborative routines, knowledge of how ML/AI can be integrated into technician training, prototype ML/AI curriculum for non-four-year audiences, and direct training impact for pilot participants.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.