Pivots: Enabling Access for Historically Underserved and Underrepresented Groups to Experiential Learning and Credentials in Artificial Intelligence

STEM劳动力的多样性仍然是一个挑战，在STEM教育项目以及数据科学和人工智能(AI)等STEM职业生涯中，低收入、种族、民族、性别代表和第一代个人的历史代表性不足就是明证。ExLENT-AI Externship项目将为来自数据科学和人工智能新兴技术领域历史上服务不足和代表性不足的群体的人提供体验式学习机会，以拓宽不同个人了解这些领域并可能在这些领域工作的机会。北卡罗来纳州立大学将与达美航空、利盟、Charity Navigator、Randstad和其他行业组织合作，设计和实施为期40周的外部奖学金计划。外部培训结合了每周一次的现场研讨会和真实世界的行业指导、工作跟踪，以及与合作伙伴一起完成真正的任务。ExLENT-AI的目标是：1)在体验式学习中利用循证的最佳实践，吸引不同的学习者投身新兴技术职业；2)招募来自历史上服务不足和代表性不足的群体的个人参与ExLENT-AI基于证据的外部培训计划；3)加强与适当利益攸关方的合作伙伴关系，以开发一个综合的、协作的网络，最好地支持参与者；4)通过导师指导和其他社区建设活动，为队列建立一个学习者社区；5)准备参与者获得相关的人工智能能力、知识和技能；以及6)通过求职过程和安置，支持参与者进入人工智能领域的新职业。ExLENT-AI Externship项目将利用十种基于证据的最佳做法，这些做法被发现在提供体验式学习和吸引/留住来自历史上服务不足和代表性不足的背景的参与者方面有效。这十种主要做法包括：1)建立STEM生态系统，2)使用集体影响模式；3)有条理的课程活动的课程作业；4)课程交付的反向课堂模式；5)团队合作和小组/队列参与知识的话语和应用；6)持续参与外部培训，在个人参与真实行业背景的真实任务的情况下；7)接触导师；8)明确阐述和评估学习目标；9)使用激励和奖励(例如，津贴和证书)；以及10)通过职业指导和求职支持进行指导。该项目将广泛促进实现具有社会意义的成果，包括在人工智能领域发展一支多样化、具有全球竞争力的新兴技术劳动力队伍。这一计划模式将得到充分发展，并将培养33名在新兴技术职业中历来服务不足和代表性不足的新个人，包括女性、第一代大学生、退伍军人、残疾人和种族/少数民族。此外，通过与业界的密切合作，项目团队将拥有一种机制，继续为不同的群体提供机会、机会和赋权，以继续保持持续的人才管道并与职业直接联系。该项目非常适合由NSF TIP和EDU董事支持的NSF ExLENT计划，因为它寻求支持来自不同专业和教育背景的个人的体验式学习机会，以增加他们对新兴技术领域的兴趣和获得职业道路的机会。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。

Diversity in the STEM workforce remains a challenge as evidenced by the historical underrepresentation of low-income, racial, ethnic, gender representation, and first-generation individuals in STEM education programs and in STEM careers such at data science and artificial intelligence (AI). The ExLENT-AI Externship project will provide experiential learning opportunities to people from historically underserved and underrepresented groups in the emerging technology fields of data science and AI to broaden access for diverse individuals to learn about and potentially work in careers in these areas. North Carolina State University will partner with Delta Air Lines, Lexmark, Charity Navigator, Randstad, and other industry organizations to collaborate on the design and implementation of the 40-week externship program. The externship is a combination of live, weekly workshop sessions and real-world, industry mentoring, job shadowing, and working on authentic tasks with partners. The objectives of ExLENT-AI are to: 1) leverage evidence-based best practices in experiential learning to attract diverse learners to emerging technology careers; 2) recruit individuals with from historically underserved and underrepresented groups to participate in the ExLENT-AI evidence-based externship program; 3) strengthen partnerships with appropriate stakeholders to develop an integrated, collaborative network to best support participants; 4) establish a community of learners for the cohorts through mentorship and other community-building activities; 5) prepare participants to gain relevant artificial intelligence competencies, knowledge, and skills; and 6) support participants through the job search process and placement into their new careers in artificial intelligence. The ExLENT-AI Externship project will leverage the use of ten evidence-based best-practices found to be effective in delivery of experiential learning and attracting/retaining participants from historically underserved and underrepresented backgrounds. These ten key practices include: 1) establishment of a STEM ecosystem, 2) use of the Collective Impact Model; 3) coursework that has structured curricular activities; 4) inverted classroom models for coursework delivery; 5) teamwork and group/cohort engagement in discourse and application of knowledge; 6) sustained involvement in externships where individuals are engaged in authentic tasks with real-world industry context; 7) engagement of mentors; 8) clear articulation and assessment of learning objectives; 9) use of incentives and rewards (e.g., stipends and certificates); and 10) guidance through career coaching and job search support. This project will contribute broadly to the achievement of societally relevant outcomes including the development of a diverse, globally competitive emerging technology workforce in artificial intelligence. This program model will be fully developed and will prepare 33 new individuals historically underserved and underrepresented in emerging technology careers, including women, first-generation college students, veterans, persons with disabilities, and racial/ethnic minorities. Additionally, through strong partnerships with industry, the project team will have a mechanism to continue to provide access, opportunity, and empowerment to diverse groups to continue a sustained talent pipeline and direct connections to careers. This project fits well within the NSF ExLENT program, supported by the NSF TIP and EDU Directorates, as it seeks to support experiential learning opportunities for individuals from diverse professional and educational backgrounds to increase their interest in, and their access to, career pathways in emerging technology fields.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.