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

NSF小型企业创新研究(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，以确定她们在创业、科学、技术或工程领域的天生兴趣。美国目前的趋势显示，从中学开始，对这些领域感兴趣的女孩的自然流失率很高。在接下来的几个学年里，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些类型的角色。评估分析和个性化路线图将有助于澄清、支持和培育个人在其职业选择(包括STEM和创业)的成长和发展过程中的旅程。评估工具的不断改进和加强将有助于为教育课程提供必要的变化和/或社会思维的转变，以帮助缩小妇女担任高技能职位的持续差距。评估和后续资源的潜在商业和社会经济影响定义了一种适销对路的产品，以及跨越家庭、学术、政府和社会机构的相关劳动力。该项目的技术创新是一个独特的框架，评估了创业、科学、技术或工程领域的固有兴趣，并利用这些数据创建了个性化的人工智能(AI)驱动的职业探索、技能发展和就业能力课程。其目标是确认，利用深度学习为这些女孩提供充满活力的职业探索路线图，可以成功地对抗对她们追求与生俱来的兴趣和发展作为企业家、科学家、技术专家和工程师的职业所需技能产生负面影响的共同社会力量。人们假设，及早识别这些与生俱来的利益会先发制人，从而避免身份刻板印象。为了消除女孩不擅长这些职业所需基本技能的确认偏见，机器学习和人工智能数据聚合被用来将这些与生俱来的特征与培养相关工作技能的资源关联起来，提供适合用户的机会和挑战，并提供机会与成功的榜样建立联系，以解决女性在这些领域缺乏代表性的问题。该项目的初始范围将以中学女生及其父母/监护人为目标，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，并已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 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.