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

NSF小企业创新研究（SBIR）第一阶段项目的广泛/商业影响始于中学生在线自我评估，以确定她们在创业、科学、技术或工程领域的内在兴趣。美国目前的趋势显示，从中学阶段开始，对这些领域感兴趣的女孩就开始大量流失。在随后的学年里，这一比例有所下降，这导致少数女性在成年后的职业生涯中担任这类角色。评估分析和个性化路线图将有助于明确、支持和培养个人的成长和发展之旅，以实现他们的职业选择，包括STEM职业和创业。评估工具的不断完善和加强将有助于为教育课程的必要改革和（或）社会思想的转变提供信息，以帮助缩小妇女担任高技能角色方面的持续差距。评估和后续资源的潜在商业和社会经济影响定义了具有相关劳动力的可销售产品，这些劳动力跨越了家庭、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，评估学生对创业、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。我们的目标是证实，利用深度学习为这些女孩提供一个动态的职业探索路线图，可以成功地对抗对她们追求天生兴趣和发展企业家、科学家、技术专家和工程师职业所需技能产生负面影响的常见社会力量。据推测，这些先天兴趣的早期识别可以预防身份刻板印象。为了消除“女孩不擅长这些职业所需的基本技能”的确认偏见，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培养相关工作技能的资源联系起来，为用户提供适合的机会和挑战，并提供与成功榜样联系的机会，以解决这些领域缺乏女性代表的问题。该项目的最初范围将以中学女生及其父母/监护人为对象，然后扩大到教师、导师、教练和整个社会的更广泛受众。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 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.