SBIR Phase I: Solving Minority Equity in Science, Technology, Engineering, and Mathematics (STEM) with Artificial Intelligence (AI)-Driven Workforce Development

这个小型企业创新研究(SBIR)第一阶段项目的更广泛/商业影响是通过吸引少数群体的兴趣并将这些兴趣与真正的职业抱负联系起来，提高少数群体的科学、技术、工程和数学(STEM)职业意识。这项研究与STEM领域的多样性、公平性和包容性目标相一致，让学生接触到中学，并为学生可能需要更公平地获得的职业探索提供支持。在机器学习和人工智能的帮助下，这项研究旨在评估STEM领域少数族裔错位所涉及的社会影响，并使用技术纠正更大、更有准备的人才库的错位。这种做法应该有助于增加生物科学、数据科学和工程等领域的多样性。此外，在人才进入劳动力市场之前提高他们的技能有助于创造一支更强大的劳动力队伍，能够更快地推动STEM领域的边界，带来新的创新、社会经济平衡和社会增长。这个SBIR第一阶段项目将荷兰职业主题的使用与机器学习中的自然语言处理相结合，为中学生推荐和创建在预期的职业道路上获得知识和经验的途径，特别是STEM职业生涯。基于一个地区表现不佳的学校和劳动力的统计数据，该系统可以推动学生通过各种途径，帮助他们更有就业能力，以及更公平地提供学校资源。通过使用实时数据培训模型，学生能够获得职业准备技能，并立即将这些技能应用于完成中小型企业的基于项目的实习。这一过程确保模型提供的信息与行业相关，并可以快速调整，以适应行业的变化，例如招聘模式、技术、行业组织心理和其他增加人才库适用性的趋势。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 broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to enhance Science, Technology, Engineering and Mathematics (STEM) career awareness in minorities by engaging their interests and correlating those interests with real career aspirations. The research aligns with diversity, equity, and inclusion goals in STEM fields, exposing students in secondary schools and providing support for career exploration that may need to be more equitably available to students. With the help of machine learning and artificial intelligence, the research is designed to evaluate the social impact involved in the misalignment of minorities in STEM fields and use technology to correct the alignment for larger, more prepared talent pools. This practice should help increase diversity in fields like biological sciences, data science, and engineering, to name a few. Additionally, upskilling talent before they enter the workforce helps to create a more robust workforce that can push the boundaries of STEM fields much faster, leading to new innovations, socioeconomic balance, and societal growth.This SBIR Phase I project combines the use of Holland occupational themes with natural language processing in machine learning to recommend and create pathways for secondary school students to gain knowledge and experience in anticipated career paths, especially STEM careers. Based on statistical data about underperforming schools and the workforce of an area, the system can nudge students through pathways to help them be more employable as well as to provide school resources more equitably. By using real-time data to train the models, students are able to gain career readiness skills and immediately apply those skills to complete project-based internships with small to medium businesses. This process ensures that information provided by the model is industry relevant and can pivot quickly to align with changes in an industry such as hiring patterns, technology, industrial-organizational psychology, and other trends that increase the applicability of a talent pool.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.