CISE-MSI: DP: IIS-III: ACOSUS: An AI-driven COunseling System for Underrepresented Transfer Students

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。2021年的统计数据显示，计算机职业中只有15%的工人是黑人和西班牙裔，25%是女性，而32.56%的大学生是代表性不足的少数民族。计算相关领域迫切需要高度多样化的劳动力。多样化劳动力的一个关键因素是社区学院和四年制大学之间的转学途径。事实上，这些机构中的许多机构都面临着共同建议转学生的挑战，因为从转学到转学后毕业的道路存在不确定性，特别是那些来自弱势少数民族或计算专业代表性不足的群体的学生。利用信息系统帮助这些学生提高技能，转入大学，并在就业市场取得成功，这是高等教育的一个重要但具有挑战性的目标。该项目的总体目标是（1）调查学生在转学决策过程中遇到的挑战，学校表现和就业安排，以及（2）设计一个创新的人工智能驱动的学生咨询系统，为计算机专业的少数转学生提供个性化的准备评估和建议。该项目旨在全面了解个人，学术和行为因素影响着代表性不足的学生在追求教育和职业决定方面的表现。它还探讨了环境因素，如社会规范和社交媒体，影响他们的决定和追求目标的努力。利用这些信息，该项目设计和评估了一个人工智能驱动的咨询系统，该系统集成了代表性不足的学生的个人，学术，行为和环境因素，不仅评估了他们对技能提升转移和就业市场的准备情况，还提供了改进建议和资源。该系统在设计中使用自然语言处理算法、深度神经网络沿着其他人工智能方法。该奖项反映了NSF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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Statistics in 2021 show that only 15% of workers in computing occupations are Black and Hispanic, and 25% are female, whereas 32.56% of college students are underrepresented minorities. There is a dire need for a highly diversified workforce in computing-related fields. A crucial contributor to a diverse workforce is the transfer pathways between community colleges and 4-year universities. In fact, many of these institutions have challenges co-advising transfer students due to the uncertainty surrounding the path from their decision to transfer to post-transfer graduation, particularly those who are from disadvantaged minority or underrepresented groups in computing majors. The use of information systems to assist these students in their upskilling transfer to universities and job market success is a critical but challenging objective of higher education. The overarching goal of this project is to (1) investigate the challenges experienced by students in their transfer decision-making process, school performance, and job placement, and (2) design an innovative AI-driven student advising system that provides personalized readiness assessment and suggestions to underrepresented transfer students in computing majors, among others.The project aims to gain a comprehensive understanding on how personal, academic, and behavioral factors influence underrepresented students in their pursuit of education and career decisions. It also explores environmental factors like social norms and social media that impact their decision and goal-pursuing endeavors. Using this information, the project designs and evaluates an AI-driven advising system that integrates personal, academic, behavioral, and environmental factors specific to underrepresented students to not only assess their preparedness for upskilling transfer and the job market, but provide improvement suggestions and resources as well. The system uses natural language processing algorithms, deep neural networks, along with other AI approaches in its design. It also employs data curation techniques to eliminate the racial and gender bias that is prevalent in current AI-driven systems.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.