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。这个“融合加速器”第一阶段项目的更广泛影响/潜在利益是促进社会学、人机交互、微认证、软件工程、改进科学，以改善协作开发和交付流程，培训机器人技术人员，使其能够满足当今和即将向“工业4.0”范式过渡的美国制造商的需求，在“工业4.0”范式中，工厂车间将转变为由专门的人工智能（AI）软件控制的网络化网络物理系统。技术人员在先进制造系统的安装和维护中仍然至关重要，但他们必须了解这些系统，以便有效地排除故障。好的培训是真实的，并且可以持续交付。实现这一目标需要课程设计者、主题专家、教育从业者和许多其他利益相关者之间的协调。从课程开发的角度来看，这是一个“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。我们的方法将这两种观点结合起来，通过跨学科的努力，生产出一种技术训练设备，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，将组织内向和产品外向的方法融合到一个单一的高效企业中。我们的项目采用设计和改进科学的方法来回答以下问题：(i)如何为很少或没有STEM背景的技术培训生设计有效和可交付的机器学习（ML）课程；（ii）考虑到利益相关者的多样性，如何围绕这一目标设计富有成效的协作程序。我们从“准备就绪”的课程概念和经验丰富的制作团队开始，使用以人为本的设计（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书面咨询外，HCD还涉及教师、学习者及其扩展学习环境（包括行政和社会方面）的数据收集。在此过程中，我们对生产团队与其他利益相关者的协作模式提出问题，确定改进的例程以加速协作的联合工作。该项目的这一阶段产生了如何通过增强协作程序来加速课程设计的知识，了解了如何将ML/AI集成到技术人员培训中，为非四年制受众提供了ML/AI原型课程，并为试点参与者提供了直接的培训影响。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 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.