STTR Phase I: Simplification AI for Workforce Upskilling

STTR 第一阶段：简化人工智能以提高劳动力技能

• 批准号: 1914104
• 负责人: Eleni Miltsakaki
• 金额: $ 22.47万
• 依托单位: Choosito!
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1914104&HistoricalAwards=false
• 关键词: STTR; Phase; Simplification; AI; Workforce

This STTR Phase I project addresses the challenge of upskilling the workforce by developing novel personalized content simplification technology. It will build a novel digital binder tool with an AI empowered content selection and simplification capability. The magnitude and urgency of the workforce upskilling problem require an immediate and robust solution. Corporations are desperate to find and nurture appropriately skilled workers to fill emerging roles. Beyond big high-tech corporations, the need for retraining is expanding in the trades and manufacturing space. An estimated 300-600 million people will need to be retrained between now and 2030. The proposed breakthrough solution utilizes state-of-the-art deep learning and natural language processing algorithms to automatically generate training materials appropriate for the level of familiarity that trainees have with the content and skills they need to acquire. This is facilitated by three core technologies: a content simplification solution with the capability of searching and identifying collections of digital resources by identifying key concepts. The applied algorithms consider the current background of the learner and include documents needed to comprehend those key concepts. The second technology addresses the open problem of text simplification by proposing a hybrid approach of traditional text simplification techniques like word substitution and a novel information retrieval approach that identifies concepts critical for the comprehensibility of advanced documents. The third technology is a personalized content simplification engine tailored to the needs and capacities of each trainee who can continue to use the technology for continuous retraining, upskilling and lifelong learning.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.

这个STTR第一阶段项目通过开发新颖的个性化内容简化技术来解决提高劳动力技能的挑战。它将构建一个新的数字活页工具，具有人工智能授权的内容选择和简化功能。劳动力技能提升问题的严重性和紧迫性需要立即采取强有力的解决方案。企业迫切希望找到并培养合适的技术工人来填补新兴的角色。除了大型高科技公司，贸易和制造业领域对再培训的需求也在扩大。从现在到2030年，估计有3亿至6亿人需要接受再培训。拟议的突破性解决方案利用最先进的深度学习和自然语言处理算法，自动生成适合受训者对所需内容和技能的熟悉程度的培训材料。这得益于三项核心技术：内容简化解决方案，能够通过识别关键概念来搜索和识别数字资源集合。所应用的算法考虑了学习者的当前背景，并包括理解这些关键概念所需的文档。第二种技术解决了文本简化的开放性问题，提出了一种混合的方法，传统的文本简化技术，如单词替换和一种新的信息检索方法，识别概念的高级文档的可理解性的关键。第三项技术是个性化的内容简化引擎，针对每个受训者的需求和能力量身定制，他们可以继续使用该技术进行持续的再培训，提高技能和终身学习。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。