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

我们的国家目前面临着技术工人的严重短缺，因为更多的人退休而不是加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缺乏必要技能差距的就业市场，从而造成人员配备、安全和技能问题。该项目通过推进利用增强现实（AR）和建筑信息建模（BIM）技术培训目前被边缘化的西班牙裔和拉丁裔工人所需的理论和知识，解决了劳动力组成变化所造成的这一差距。这种新的基础知识将有助于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进行创新，研究AR如何通过实时语音翻译实现公平，BIM技术可以通过为建筑劳动力中的“失踪的数百万人”开辟额外的职业道路，促进可转移的改造技能的学习，以扩大个人的理想目标。通过扩大工人对改造工作的贡献，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来访问母语（西班牙语）的BIM数据，从而帮助非英语母语的工人学习所需的改造技能。该项目将利用一种新的跨学科方法来推进对学习科学的基本理解，以支持AR+BIM人类技术前沿边缘的按需改造技能和设计。我们将进行语言需求分析，以了解几种西班牙语方言的微妙之处和复杂性，从而确定未来AR+BIM实时翻译界面的优势和劣势。我们将开发经过验证的流程，以利用非英语母语工人的这些翻译能力，同时开发新型迷你实验室，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，促进更大的可转移改造技能。该研究项目将研究母语和身体动作如何体现和扩展复杂建筑环境中的认知过程，并了解未来多元化劳动力的语言和文化能力。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价值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.