SBIR Phase I: Artificial Intelligence (AI)-enabled Personalized Employability Curriculum (APEC)

SBIR 第一阶段：人工智能 (AI) 支持的个性化就业能力课程 (APEC)

• 批准号: 2230864
• 负责人: Terrisa Duenas
• 金额: $ 27.43万
• 依托单位: ESTE(TM) LEVERAGE, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230864&HistoricalAwards=false
• 关键词: SBIR; Phase; Artificial; Intelligence; AI

The broader/commercial impact of this NSF Small Business Innovation Research (SBIR) Phase I project begins with an online self-assessment by middle-school girls to identify their innate interests within the fields of entrepreneurship, science, technology, or engineering. Current U.S. trends show a high attrition of girls with interests in these fields, beginning at the middle school level. There is a subsequent drop-off over the ensuing academic years, and this results in small numbers of women occupying these types of roles in their adult careers. The assessment analysis and personalized roadmap will help clarify, support, and nurture the individual’s journey in their growth and development towards their career choices including careers in STEM and entrepreneurship. Ongoing refinement and enhancement of the assessment tool will help inform needed changes to the educational curriculum and/or shifts in societal thinking to help close the ongoing gap in women occupying highly skilled roles. The potential commercial and socioeconomic impact of the assessment and follow-on resources defines a marketable product with associated workforce that spans across the family, academic, governmental, and societal institutions. The technical innovation in this project is a unique framework assessing innate interest in the fields of entrepreneurship, science, technology, or engineering and leveraging these data to create a personalized artificial intelligence (AI)-driven career exploration, skills development, and employability curriculum. The goal is to confirm that the use of deep learning to provide these girls with a dynamic career exploration roadmap can successfully counter the common societal forces that negatively impact their pursuit of innate interests and development of the skills necessary for careers as entrepreneurs, scientists, technologists, and engineers. It is hypothesized that early identification of these innate interests preempts identity stereotypes. To combat confirmation bias that girls aren’t good at the fundamental skills needed for these careers, machine learning and AI-enabled data aggregation is used to correlate these innate traits with resources that foster associated job skills, offer opportunities and challenges that are suitable to the user, and provide opportunities to connect with successful role models to address the lack of representation of women in these areas. The initial scope of the project will target middle school girls and their parents/guardians with expansion to the broader audiences of teachers, mentors, coaches, and society in general.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NSF小企业创新研究（SBIR）第一阶段项目的更广泛/商业影响始于中学女生的在线自我评估，以确定他们在创业，科学，技术或工程领域的先天兴趣。目前美国的趋势表明，从中学开始，对这些领域感兴趣的女孩的流失率很高。 在接下来的几年里，妇女的数量有所下降，这导致在成年后担任这类角色的妇女人数很少。评估分析和个性化的路线图将有助于澄清，支持和培养个人的成长和发展之旅，走向他们的职业选择，包括在STEM和创业的职业生涯。不断完善和加强评估工具将有助于为教育课程所需的变革和/或社会思维的转变提供信息，以帮助缩小妇女在担任高技能角色方面的现有差距。评估和后续资源的潜在商业和社会经济影响定义了一个可销售的产品，其相关劳动力跨越家庭，学术，政府和社会机构。该项目的技术创新是一个独特的框架，评估创业、科学、技术或工程领域的内在兴趣，并利用这些数据创建个性化的人工智能（AI）驱动的职业探索、技能发展和就业能力课程。我们的目标是确认，使用深度学习为这些女孩提供动态的职业探索路线图，可以成功地对抗常见的社会力量，这些力量对她们追求与生俱来的兴趣和企业家、科学家、技术人员等职业所需技能的发展产生负面影响。和工程师。据推测，这些先天利益的早期识别先发制人的身份刻板印象。为了消除确认偏见，即女孩不擅长这些职业所需的基本技能，机器学习和人工智能支持的数据聚合被用来将这些先天特征与促进相关工作技能的资源相关联，提供适合用户的机会和挑战，并提供与成功榜样联系的机会，以解决这些领域女性代表性不足的问题。该项目的最初范围将针对中学女生及其家长/监护人，并扩大到更广泛的教师，导师，教练和社会大众。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。