RAISE: C-Accel Pilot-Track B1:DIRECT: A Framework for Diagnosis, Recommendation, and Training in Continuous Workforce Development

NSF融合加速器支持基于团队的多学科努力，以应对国家重要性的挑战，并在不久的将来显示出交付成果的潜力。这一融合加速器第一阶段项目的更广泛影响/潜在好处是提供一个软件工具，指导美国制造业工人在其整个职业生涯中完成工作选择和技能提升的过程。由于工作场所技术的快速发展，如机器人和计算机与机器的接口，未来的工作需要学校或标准培训计划没有教授的技能。因此，作为终身学习过程的一部分，工人再技能和再培训对美国经济至关重要，也是一个具有国家重要性的话题。调查人员将通过收集和分析制造业大型合作伙伴公司的数据并采访真实的工人和利益相关者来研究这一问题；建议的方法将通过合作伙伴公司雇用的工人和当地合作伙伴市政府招聘的工人进行测试。调查人员将结合他们在计算机科学、教育技术以及对劳动力市场的社会和经济分析方面的专业知识，提出一个有效、公平和可扩展的软件解决方案，以帮助美国劳动力中的大多数人，包括制造业和其他行业。这个融合加速器第一阶段项目旨在最终开发一个框架，执行员工档案诊断、培训计划推荐和智能培训平台开发(DIRECT)，以实现劳动力的持续发展。Direct是一个集成的软件工具，可以帮助员工识别理想的未来工作，推荐培训计划，并指导员工规划未来的职业道路。它由四个连续且相互交织的组件组成：(I)使用认知模型根据工作数据评估工人技能水平的技能水平诊断和评估组件，(Ii)使用智能辅导概念帮助工人获得新技能的培训经验发展组件，(Iii)使用劳动力市场分析来识别高需求工作以及工人与其期望工作之间的技能差距的技能差距识别组件，以及(Iv)使用预测人工智能算法将工人与未来工作联系起来并选择培训计划以获得必要技能的未来工作和培训计划推荐组件。在该项目的第一阶段，调查人员将与行业和政府合作伙伴合作，制定具体的研究问题，确定数据源，开发原型，并进行试点研究，以确保直接有效和实用。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。

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 provide a software tool to guide individual workers in the US manufacturing workforce through the process of job selection and upskilling in their entire career. Due to the rapid development of workplace technology, such as robots and computer interfaces to machinery, future jobs require skills that are not taught in schools or standard training programs. Therefore, worker reskilling and retraining as part of the lifelong learning process is critical to the US economy and is a topic of national importance. The investigators will study this problem by collecting and analyzing data from a large partner corporation in the manufacturing industry and interviewing real workers and stakeholders; the proposed approaches will be tested by workers both employed by the partner corporation and recruited by a local partner city government. The investigators will integrate their expertise on computer science, educational technology, and social and economic analyses of the labor market to propose an effective, fair, and scalable software solution that can help a broad segment of workers in the US workforce, in both the manufacturing industry and beyond.This Convergence Accelerator Phase I project aims at ultimately developing a framework that performs worker profile Diagnosis, training program RECommendation, and intelligent Training platform development (DIRECT) for the purpose of continuous workforce development. DIRECT is an integrated software tool that helps workers identify desirable future jobs, recommends training programs, and guides workers through the process of planning future career paths. It consists of four consecutive and intertwined components: (i) a skill level diagnosis and assessment component that uses cognitive models to assess worker skill levels from on-job data, (ii) a training experience development component that uses intelligent tutoring concepts to help workers acquire new skills, (iii) a skill gap identification component that uses labor market analysis to identify high-demand jobs and the skill gaps between a worker and their desirable job, and (iv) a future job and training program recommendation component that uses predictive artificial intelligence algorithms to connect workers to future jobs and select training programs to acquire the necessary skills. In Phase I of the project, the investigators will work with industry and government partners to formulate concrete research problems, identify data sources, develop prototypes, and conduct pilot studies to ensure that DIRECT is effective and practical.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.