STTR Phase I: Exploring Artificial Intelligence (AI)-Enabled Skills Data For Education-to-Employment Transitions and Career Support

STTR 第一阶段：探索人工智能 (AI) 支持的技能数据，以实现从教育到就业的过渡和职业支持

• 批准号: 2112276
• 负责人: Nicholas Hathaway
• 金额: $ 25.6万
• 依托单位: BRIO DATA GROUP, LLC
• 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: 2022-06-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112276&HistoricalAwards=false
• 关键词: STTR; Phase; Exploring; Artificial; Intelligence

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to empower individuals to thrive in a world of innovative disruption through the development of an enduring, data-driven, ready-access digital service that will assist learners to identify opportunity pathways throughout a lifetime of learning and employment. Completion of this project and the addition of the proposed analytical capabilities applied to learner narratives may create a powerful and unique set of tools for individuals, secondary and higher educational institutions, employers, and a range of informal education providers. The research completed in this project and tools produced by it seek to inform a new approach to matching the interests, skills and educational backgrounds of learners to jobs and educational opportunities. This approach engages learners of diverse backgrounds early in their education and assists them in succeeding in many fields that currently suffer from lack of diversity, especially in the STEM disciplines. Better matching of skills and interests to learning trajectories and career options may enhance human development broadly, increase job satisfaction, and lead to a more productive workforce. This Small Business Technology Transfer (STTR) Phase I project seeks to develop methods for bringing a wide range of learner interests and achievements into a common framework that will be used to match a learner’s current state to specific opportunities and general development pathways. The project also seeks to assess the value of opportunities and chance of success for the student. This research centers on machine analysis of written reflections. Findings digitally mined from written reflections and potentially from other kinds of narrative artifacts are combined with data from grades, test scores, and other skills assessments and certifications to guide learners toward attractive educational and employment opportunities. In addition, findings from interactive visualization tools support human exploratory analyses of machine categorizations in order to improve the delivery of educational programs.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.

这项小型企业技术转移（STTR）I阶段项目的更广泛的影响/商业潜力是，通过开发持久，数据驱动的，现成的数字服务，使个人能够在创新破坏的世界中蓬勃发展，这将帮助学习者在整个学习和就业的整个学习期间中识别机会途径。该项目的完成以及对学习者叙述的提议分析能力的增加可能会为个人，二级和高等教育机构，雇主以及一系列非正式教育提供者创建强大而独特的工具。该项目完成的研究和IT生产的工具旨在为一种新的方法提供匹配学习者的兴趣，技能和教育背景与工作和教育机会。这种方法吸引了潜水员的学业早期的学习者，并在许多领域的多样性中，尤其是在STEM学科中的许多领域，协助他们在许多领域的继承。更好地匹配技能和兴趣与学习轨迹和职业选择可以广泛增强人类发展，提高工作满意度并导致更有生产力的劳动力。这个小型企业技术转移（STTR）I阶段项目旨在开发将各种学习者利益和成就带入一个共同框架的方法，该框架将用于将学习者的当前状态与特定的机会和一般发展途径相匹配。该项目还旨在评估学生的机会和成功机会的价值。这项研究以书面反射的机器分析为中心。从书面思考和其他类型的叙事文物中进行了数字挖掘的发现，与成绩，考试成绩以及其他技能评估和证书的数据结合在一起，以指导学习者带来有吸引力的教育和就业机会。此外，交互式可视化工具的发现支持人类对机器类别的勘探分析，以改善教育计划的交付。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子和更广泛的影响评估标准来评估，被认为是珍贵的支持。