Collaborative Research: Intelligent Immersive Environments for Learning Robotics

协作研究:学习机器人的智能沉浸式环境

基本信息

  • 批准号:
    2202610
  • 负责人:
  • 金额:
    $ 74.97万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-09-01 至 2025-08-31
  • 项目状态:
    未结题

项目摘要

The global economy is being rapidly reshaped by sophisticated robots that enhance human dexterity, visual perception, speed, and strength. This intense focus on creating and implementing new automation technologies is bringing disruptive changes to job markets. In Architecture, Engineering, and Construction (AEC) industries, robotics automation is transforming jobs at a speed and scale never experienced before, leading to new demand for skilled workers in advanced technologies and robotics. Addressing the learning needs of AEC students, future professionals, and industry workers is critical for ensuring the competitiveness of a large proportion of the US workforce. Our proposal is inspired by recent technological achievements in self-adaptive, data-driven, and autonomous systems for virtual learning. These technologies bear the promise to transform education by personalization and tailoring the learning content and sequence for differences in ability, experience, and sociocultural background. Leveraging these technologies, we will research, develop, and test a personalized learning tool for delivering an industrial robotics curriculum to prepare the next generation of the AEC workforce. We plan to achieve this goal with five educational and scientific innovations: 1) Artificial Intelligence (AI)-assisted Adaptive Intelligent Learning System 2) AI-assisted coaching, 3) Novel curriculum content and delivery in virtual reality, 4) Game-based learning user experience, and user interface and 5) AI-enabled learning analytics. The design and implementation of this project will contribute to technological advancement in AI-assisted Adaptive Intelligent Learning systems and our ability to apply state-of-the-art AI and Natural Language Processing techniques for the analysis of learning data. Advancing this frontier is critical for our ability to evaluate learner data at scale. Further, our development of AI-Assisted coaching will lead to broadly applicable advancements in intelligent tutoring systems. It includes a novel capacity to detect and identify learner failure patterns and to apply known remediation to improve learning outcomes. In addition, the design and implementation of a curriculum that dynamically changes in response to learner input, skill level, and advancement toward learning goals can bring new pedagogical approaches to curriculum development, reshaping our current practices. Finally, our project will enrich learning analytics by integrating biometric and performance data leading to a greater understanding of the learning process.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.
先进的机器人正在迅速重塑全球经济,这些机器人提高了人类的灵活性、视觉感知、速度和力量。这种对创造和实施新自动化技术的强烈关注正在给就业市场带来颠覆性的变化。在建筑、工程和建筑(AEC)行业,机器人自动化正在以前所未有的速度和规模改变工作,导致对先进技术和机器人领域熟练工人的新需求。满足AEC学生、未来的专业人士和行业工人的学习需求,对于确保大部分美国劳动力的竞争力至关重要。我们的建议是受到最近在自适应、数据驱动和自主的虚拟学习系统方面的技术成就的启发。这些技术承诺通过个性化和根据能力、经验和社会文化背景的差异定制学习内容和顺序来改变教育。利用这些技术,我们将研究、开发和测试个性化学习工具,以提供工业机器人课程,为下一代AEC劳动力做好准备。我们计划通过五项教育和科学创新来实现这一目标:1)人工智能(AI)辅助的适应性智能学习系统2)AI辅助指导,3)虚拟现实中新颖的课程内容和交付,4)基于游戏的学习用户体验和用户界面,以及5)AI使能的学习分析。这个项目的设计和实施将有助于人工智能辅助自适应智能学习系统的技术进步,以及我们应用最先进的人工智能和自然语言处理技术分析学习数据的能力。推进这一前沿对于我们评估大规模学习者数据的能力至关重要。此外,我们对人工智能辅助教学的开发将导致智能教学系统的广泛适用的进步。它包括一种新的能力,可以检测和识别学习者的失败模式,并应用已知的补救措施来改善学习结果。此外,课程的设计和实施可以根据学习者的输入、技能水平和向学习目标的进步而动态变化,可以为课程开发带来新的教学方法,重塑我们当前的实践。最后,我们的项目将通过整合生物识别和性能数据来丰富学习分析,从而更好地理解学习过程。这一奖项反映了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。

项目成果

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Shahin Vassigh其他文献

Biomimetic self-shading walls via 3D-printing for reduced heat gain: Multiscale learning using graph neural networks to predict solar radiation absorption
通过3D打印实现仿生自遮阳墙以减少热量获取:利用图神经网络进行多尺度学习以预测太阳辐射吸收
  • DOI:
    10.1016/j.buildenv.2025.113048
  • 发表时间:
    2025-07-01
  • 期刊:
  • 影响因子:
    7.600
  • 作者:
    Hanmo Wang;Zhuyin Lu;Amanda Wojtasiak;Shawn Owyong;Bo Sun;Yu Fang;Shahin Vassigh;Alexander Lin
  • 通讯作者:
    Alexander Lin

Shahin Vassigh的其他文献

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{{ truncateString('Shahin Vassigh', 18)}}的其他基金

Augmented Learning for Environmental Robotics Technologies: A Data-Driven Approach for Sustainable Built Environments
环境机器人技术的增强学习:可持续建筑环境的数据驱动方法
  • 批准号:
    2315647
  • 财政年份:
    2023
  • 资助金额:
    $ 74.97万
  • 项目类别:
    Standard Grant
RAPID: A Platform for Mitigating the Impacts of COVID-19 on the Healthcare System
RAPID:减轻 COVID-19 对医疗保健系统影响的平台
  • 批准号:
    2029557
  • 财政年份:
    2020
  • 资助金额:
    $ 74.97万
  • 项目类别:
    Standard Grant
Convergence Accelerator Phase I (RAISE): Preparing the Future Workforce of Architecture, Engineering, and Construction for Robotic Automation Processes
融合加速器第一阶段 (RAISE):为机器人自动化流程的未来架构、工程和施工人员做好准备
  • 批准号:
    1937019
  • 财政年份:
    2019
  • 资助金额:
    $ 74.97万
  • 项目类别:
    Standard Grant
Collaborative Research: Strategies for Learning: Augmented Reality and Collaborative Problem-Solving for Building Sciences
协作研究:学习策略:增强现实和协作解决建筑科学问题
  • 批准号:
    1504898
  • 财政年份:
    2015
  • 资助金额:
    $ 74.97万
  • 项目类别:
    Standard Grant

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