Convergence Accelerator Phase I (RAISE): AI-Enabled Personalized Training for Displaced Workers in Materials Supply Chain

NSF融合加速器支持以团队为基础的多学科努力，以应对国家重要性的挑战，并在不久的将来展示可交付成果的潜力。 这个融合加速器第一阶段项目的更广泛影响是创建支持人工智能（AI）的工具，旨在遏制第四次工业革命造成的迫在眉睫的劳动力流失危机。开发的人工智能技术将允许个性化的培训技术移植到新兴的工作中。具体而言，个性化培训和评估生成可以在具有各种背景和领域的工人中使用，只关注工人需要学习的内容，跳过他们已经掌握的内容，这可以大大缩短培训时间，同时提高知识和技能的获取，建立工人的自我意识和自信，并确保工人的公平待遇。虽然直接的试验台是在采矿，金属加工和制造业，利用现有的核心教育和研究优势以及我们的工业财团和利益相关者行业，但工具的影响可以为工程的其他领域量身定制。通过学术界，工业界和教育领域的多学科合作，该项目将加强对未来工人培训的科学理解，并形成人工智能技术，教育科学和传统工程科学的教学融合，以满足未来跨学科的工作要求。这个融合加速器第一阶段项目将复制个性化的辅导方法，采用人工智能方法，自动评估失业工人的技能和差距，并进行快速、公平和具有成本效益的大规模培训，使他们能够找到新的工作。我们将专注于三个相互依赖的研究目标，以实现我们的研究目标。首先，学术界、工业界和教育领域的多学科合作将增强我们对未来工人培训的科学理解，并将为人工智能工具定义有意义的模型结构、特征化输入和评估指标。第二，新的学习方法将从现有的工人-工作组合中模块化地学习和转移知识，以便为培训期间没有看到的给定的新工人-工作组合生成定制的培训方案。最后，该团队将制定一个坚实的计划，将人工智能工具与现有的大学和工业培训计划相结合，并为人工智能工具在工业中的实际部署铺平道路。该项目的交付成果将满足美国整个材料供应链部门的需求，并培养一支多元化、具有全球竞争力的STEM员工队伍。该奖项反映了NSF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 of this Convergence Accelerator Phase I project is to create artificial intelligence (AI) -enabled tools aimed at curbing the looming workforce displacement crisis due to the 4th industrial revolution. The developed AI technology will allow personalized training techniques to be ported to the emerging jobs. Specifically, the personalized training and assessment generation can be used across workers with various backgrounds and areas, focusing only on what workers need to learn, skipping what they have already mastered, which can cut training time dramatically while boosting knowledge and skill acquisition, build workers self-awareness and self-confidence, and ensure the fair treatment of workers. Whilst the immediate test beds are in mining, metal processing and manufacturing, which take advantage of existing core education and research strengths and our industrial consortia and stakeholder industries, the impact of the tools can be tailored for other fields in engineering. Through the multidisciplinary cooperation across academia, industry, and education fields, this project will enhance the scientific understanding of future worker training and forms a pedagogical convergence of AI techniques, educational sciences, and traditional engineering & sciences, which meet the future interdisciplinary job requirements. This Convergence Accelerator Phase I project will replicate personalized tutoring methods with an AI-enabled approach that will allow for automatic assessment of the skills and gaps for displaced workers and for fast, fair, and cost-effective training at scale to place them in new jobs. We will focus on three research objectives that are mutually dependent for achieving our research goal. First, the multidisciplinary cooperation across academia, industry, and education fields will enhance our scientific understanding of future worker training and will define meaningful model structure, featurized inputs, and assessment metrics for the AI tool. Second, a new learning approach will modularly learn and transfer knowledge from available worker-job combinations to generate the customized training program for a given new worker-job combination that was not seen during training. Lastly, the team will develop a solid plan which will integrate the AI-enabled tool with the existing university and industrial training programs and pave the way for practical deployment of the AI tool in industry. The deliverables of this project will serve the US needs across the entire materials supply chain sector and develop a diverse, globally competitive STEM workforce.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.