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

这项小企业创新研究（SBIR）第一阶段项目的更广泛/商业影响是通过吸引少数民族的兴趣并将这些兴趣与实际的职业抱负联系起来，提高他们的科学、技术、工程和数学（STEM）职业意识。该研究与STEM领域的多样性、公平性和包容性目标相一致，暴露了中学生，并为职业探索提供了支持，这些职业探索可能需要更公平地提供给学生。在机器学习和人工智能的帮助下，该研究旨在评估STEM领域少数民族错位所带来的社会影响，并利用技术来纠正更大、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对他们进行技能培训有助于创造一支更强大的劳动力队伍，从而更快地推动STEM领域的界限，从而实现新的创新、社会经济平衡和社会发展。SBIR第一阶段项目将荷兰职业主题与机器学习中的自然语言处理相结合，为中学生推荐和创造途径，以获得预期职业道路，特别是STEM职业的知识和经验。根据有关表现不佳的学校和一个地区劳动力的统计数据，该系统可以推动学生通过各种途径，帮助他们更容易就业，并更公平地提供学校资源。通过使用实时数据来训练模型，学生能够获得职业准备技能，并立即将这些技能应用到中小型企业的项目实习中。这个过程确保了模型提供的信息与行业相关，并且可以快速调整，以适应行业的变化，如招聘模式、技术、行业组织心理学和其他增加人才库适用性的趋势。该奖项反映了美国国家科学基金会的法定使命，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/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.