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

这个小企业技术转让（STTR）第一阶段项目的更广泛的影响/商业潜力是通过开发持久的，数据驱动的，随时访问的数字服务，帮助学习者在整个学习和就业的一生中确定机会途径，使个人能够在创新破坏的世界中茁壮成长。该项目的完成以及拟议中的学习者叙述分析能力的增加，可能会为个人、中等和高等教育机构、雇主和一系列非正规教育提供者创造一套强大而独特的工具。在这个项目中完成的研究及其产生的工具试图提供一种新的方法，使学习者的兴趣、技能和教育背景与工作和教育机会相匹配。这种方法让不同背景的学习者在教育的早期就参与其中，并帮助他们在许多目前缺乏多样性的领域取得成功，特别是在STEM学科中。更好地将技能和兴趣与学习轨迹和职业选择相匹配，可以广泛地促进人类发展，提高工作满意度，并提高劳动力的生产率。 这个小企业技术转让（STTR）第一阶段项目旨在开发将广泛的学习者兴趣和成就纳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机会和一般发展途径相匹配。 该项目还旨在评估机会的价值和学生成功的机会。本研究集中于书面反思的机器分析。从书面反思和潜在的其他类型的叙事工件中数字挖掘的结果与来自等级，考试成绩和其他技能评估和认证的数据相结合，以指导学习者获得有吸引力的教育和就业机会。此外，交互式可视化工具的结果支持人类对机器分类的探索性分析，以改善教育项目的交付。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 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.