FW-HTF-RM: Addressing and Amplifying the Skills of the Future Hispanic and Latino Construction Workforce Using BIM and Augmented Reality

我国目前面临着熟练行业工人的严重短缺，因为越来越多的人退休，而不是进入这些至关重要的建筑职业领域。此外，不断扩大的外语差距迫使非英语母语的新建筑工人面临就业市场缺乏必要的技能缺口，这造成了人员配备、安全和技能问题。该项目通过利用增强现实(AR)和建筑信息建模(BIM)技术，推动培训目前被边缘化的拉美裔和拉丁裔工人所需的理论和知识，解决了劳动力构成变化造成的这一差距。这一新的基础知识将支持创造一个所有人都能充分参与的环境。我们的跨学科团队正在通过研究AR和实时语音翻译如何实现公平来进行创新，以及BIM技术如何通过为建筑劳动力中“失踪的数百万人”开辟更多的职业道路来促进可转让的翻新技能的学习，以扩大个人的理想目标。通过扩大工人为翻新工作带来的好处，增加这些工人获得更好职位的机会。我们的目标是通过利用AR访问母语(西班牙语)的BIM数据来增强非英语母语员工的能力，从而帮助他们学习按需改装技能。该项目将利用一种新的跨学科方法来促进对学习科学的基本理解，支持AR+BIM人类技术前沿的按需改装技能和设计。我们将进行语言需求分析，了解几种西班牙语方言的微妙和复杂性，以确定未来AR+BIM实时翻译接口的优势和劣势。我们将开发经过验证的流程，为非英语母语工人利用这些翻译能力，同时开发新的迷你实验室，通过测试工人的第一和第二语言理解对复杂建筑任务完成的影响，促进更多可移植的改装技能。这项研究项目将研究母语和肢体动作如何在复杂的建筑环境中体现和延伸认知过程，并获得对未来多样化劳动力的语言和文化能力的理解。我们的方法直接适用于提供对制造业、护理和公共安全等替代问题领域的见解，所有这些领域都面临着熟练劳动力短缺和公平准入的挑战。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。

Our nation currently faces a critical shortage of skilled trades workers as more are retiring than joining these critically important construction occupation fields. Additionally, a widening foreign language gap forces new construction workers, who are not native English speakers, to face a job market lacking the requisite skills gap creating staffing, and safety, and skill issues. This project addresses this gap created by the changing composition of the labor force by advancing the theory and knowledge required to train currently marginalized Hispanic and Latino workers utilizing augmented reality (AR) and building information modeling (BIM) technology. This new foundational knowledge will support the creation of an environment where all individuals can fully participate. Our interdisciplinary team is innovating by investigating how AR, with real-time voice translation can enable equity, and BIM technology can facilitate the learning of transferable retrofitting skills to expand individuals’ aspirational goals, by opening additional career paths to the ‘Missing Millions’ in the construction workforce. Increasing access to better positions for these workers by amplifying workers with what they bring to retrofitting jobs. Our goal is to empower non-native English-speaking workers by utilizing AR to access BIM data in their native language (Spanish), thereby assisting them to learn in-demand retrofitting skills. This project will utilize a novel interdisciplinary approach to advance the fundamental understanding of the learning science supporting in-demand retrofitting skills and designs at the edge of the AR+BIM human-technology frontier. We will conduct a language needs analysis to understand the subtleties and complexities of several Spanish dialects to identify strengths and weaknesses in future AR+BIM real-time translation interfaces. We will develop validated processes to leverage these translation capabilities for non-native English-speaking workers, while developing novel mini-labs to promote greater transferable retrofitting skills by testing impacts of workers’ first and second language comprehension on complex construction task completion. This research project will study how native language and physical actions embody and extend cognitive processes in complex construction environments and gaining understanding of the linguistic and cultural competencies of a diverse future workforce. Our approach is directly applicable towards providing insights into the alternative problem domains of manufacturing, nursing, and public safety, all of which are facing the challenges of skilled labor shortages and equitable access.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.