Convergence Accelerator Phase 1 (RAISE): Fostering a Diverse Artificial Intelligence (AI) Workforce

NSF融合加速器支持基于团队的多学科努力，以应对国家重要性的挑战，并在不久的将来显示出交付成果的潜力。这一融合加速器第一阶段项目的更广泛影响/潜在好处是揭示了让未被充分代表的高中生参与人工智能(AI)主题的关键因素，这些主题导致了对职业的长期追求，最终目标是增加人工智能的多样性，更好地满足日益增长的劳动力需求。该项目将采用“人工智能优先”模式，强调从数据中自动生成代码的概念--这与只强调编码的传统CS教育有所不同。这种范式转变需要一种多学科的方法。这项建议汇集了社会工作、计算机科学和STEM教育推广领域的专家，并利用了多所大学和一个非营利性教育组织之间的现有关系。通过将揭示学生与人工智能接触的最佳实践的研究，以及将作为研究结果试验床的补充外展努力，这项提议将为如何在K-12级别建立管道，以产生一支多样化和具有竞争力的人工智能劳动力奠定基础。最终目标是这种多样化的劳动力将创造出使用人工智能的新产品，而不会有太大的偏见。这一融合加速器第一阶段项目旨在解决人工智能劳动力部门缺乏合格和多样化的候选者的问题。它还解决了人们日益担忧的问题，即人工智能劳动力中缺乏代表性正在导致技术的创造，这种技术更有可能加剧社会偏见，包括性别歧视和种族主义。这里采用的融合研究方法包括：1)提炼关于K-12参与人工智能课程的主要研究问题，并开发配套的评估工具来评估人工智能外展计划；2)确定社会工作研究的最佳实践，以创建一个强大的招聘模型，有意针对人工智能中代表性不足的群体提供教育外展机会；以及3)利用社会工作框架为试点实习计划开发一个模型，将学生与雇主联系起来，并使他们在通往人工智能职业生涯的管道中更进一步。这项研究将导致开发一个工具包，该工具包将被K-12从业者广泛使用，以吸引更多的学生，从而创建甚至更强大的人才管道，以在未来进入人工智能劳动力。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 NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact/potential benefit of this Convergence Accelerator Phase I project is to uncover key factors in engaging underrepresented high school students in Artificial Intelligence (AI) topics that lead to long-term pursuit of careers with an ultimate goal of increasing diversity in AI and better meeting the growing workforce demands. This project will employ an "AI First" model that emphasizes the concept of code that produces itself from data - a departure from traditional CS education that emphasizes coding alone. Such a paradigm shift requires a multi-disciplinary approach. This proposal brings together experts in the field of Social Work, Computer Science, and STEM education outreach and leverages existing relationships between multiple universities and a nonprofit education organization. Through research studies that will uncover best practices in engaging students with AI and through complementary outreach efforts that will serve as test beds for research findings, this proposal will set the foundation for how to build a pipeline at the K-12 level to lead to a diverse and competitive AI workforce. The end goal is that this diverse workforce will create new products using AI with less potential for bias.This Convergence Accelerator Phase I project aims to address the lack of qualified and diverse candidate pool for the AI workforce sector. It also addresses the growing concern that lack of representation among the AI workforce is resulting in creation of technology that is more likely to exacerbate societal biases, including sexism and racism. The convergent research approach employed here involves: 1) refining primary research questions regarding K-12 engagement with AI curriculum and develop accompanying assessment tools to evaluate AI outreach programs; 2) identifying best practices in social work research to create a robust recruitment model that intentionally targets underrepresented groups in AI to education outreach opportunities; and 3) utilizing a social work framework to develop a model for a pilot internship program that connects students to employers and brings them further along in the pipeline to a career in AI. This research will result in the development of a Toolkit that will be used widely by K-12 practitioners to engage more students and thereby create and even stronger pipeline of talent to enter the AI workforce in the future.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.