CAP: Expanding AI Curriculum and Infrastructure at Texas State University to Advance Interdisciplinary Research and Grow a Diverse AI Workforce

CAP：扩展德克萨斯州立大学的人工智能课程和基础设施，以推进跨学科研究并培养多元化的人工智能劳动力

• 批准号: 2334268
• 负责人: Tahir Ekin
• 金额: $ 40万
• 依托单位: Texas State University - San Marcos
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334268&HistoricalAwards=false
• 关键词: CAP; Expanding; AI; Curriculum; Infrastructure

This project is an ExpandAI Capacity building pilot (CAP), which focuses on establishing and growing AI related activities at Texas State University - San Marcos. This project aims to significantly expand access to AI education, infrastructure, and research opportunities through widespread integration of AI technology into non-AI disciplines such as criminal justice, psychology, firefighting and agriculture. By leveraging the collective expertise and resources of the team, the project will promote interdisciplinary research and education to grow a diverse pool of AI practitioners that will drive sustainable expansion of AI technology across the Texas State University San Marcos campus and in the nearby community. Led by a diverse team of researchers from various academic departments, the project will catalyze institutional change at Texas State University San Marcos by providing comprehensive training to underrepresented individuals, empowering them to pursue AI-related careers thereby enriching the field with diverse perspectives.The proposal describes a four-pronged approach to AI-related institutional transformation at Texas State University San Marcos: (i) enhancing instructional and curricular capacity by developing innovative models for teaching and learning using AI; (ii) improving the efficiency of AI research by providing high-performance computing training that will speed up data pre-processing and neural network training; (iii) investing in foundational and use-inspired AI research and (iv) workforce development initiatives for individuals traditionally underserved in AI. Curriculum development initiatives include the establishment of an Applied AI Summer School and the development of several new AI-related course modules in areas such as Data Analytics, Python programming, Deep Learning, Natural Language Processing and Computer Vision. Evidence-based pedagogical approaches - such as inclusive pedagogy, reflective practices and contextualization – will be employed to support student learning. In collaboration with the Texas State University San Marcos Center for Analytics and Data Science, this project will establish an inclusive AI ecosystem and promote interdisciplinary collaboration, driving groundbreaking research in use-inspired AI. The ExpandAI Program supports AI-powered education and workforce development, infrastructure and research at Minority Serving Institutions to strengthen and diversify U.S. research and education pathways and provide historically marginalized communities with new opportunities in STEM careers.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.

该项目是一个ExpandAI能力建设试点项目（CAP），重点是在德克萨斯州州立大学-圣马科斯建立和发展AI相关活动。该项目旨在通过将人工智能技术广泛整合到刑事司法、心理学、消防和农业等非人工智能学科中，来显著扩大人工智能教育、基础设施和研究机会。通过利用团队的集体专业知识和资源，该项目将促进跨学科的研究和教育，以培养多样化的人工智能从业者，这将推动人工智能技术在德克萨斯州州立大学圣马科斯校区和附近社区的可持续发展。该项目由来自不同学术部门的多元化研究人员团队领导，将通过为代表性不足的个人提供全面培训，促进德克萨斯州立大学圣马科斯的制度变革，使他们能够从事人工智能相关的职业，从而以多样化的视角丰富该领域。该提案描述了德克萨斯州立大学圣马科斯人工智能相关制度转型的四管齐下的方法：（i）通过开发使用人工智能进行教学和学习的创新模型，提高教学和课程能力;（ii）通过提供高性能计算培训，加快数据预处理和神经网络培训，提高人工智能研究的效率;（iii）投资于基础和使用启发的人工智能研究，以及（iv）为传统上在人工智能方面服务不足的个人提供劳动力发展计划。课程开发计划包括建立应用人工智能暑期学校，并在数据分析、Python编程、深度学习、自然语言处理和计算机视觉等领域开发几个新的人工智能相关课程模块。将采用基于证据的教学方法，如包容性教学法、反思性做法和情境化，以支持学生学习。该项目与德克萨斯州立大学圣马科斯分析和数据科学中心合作，将建立一个包容性的人工智能生态系统，促进跨学科合作，推动使用启发式人工智能的开创性研究。ExpandAI计划支持少数民族服务机构的人工智能驱动的教育和劳动力发展，基础设施和研究，以加强和多样化美国的研究和教育途径，并为历史上被边缘化的社区提供STEM职业的新机会。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。