Equipment: HSI: ELPSE: EI: A Guided Pathway to Enhancing HSI Student Experience and Success in Generative AI with the Planting of Education-Oriented GPU Cluster

With support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this Educational Instrumentation (EI) track aims to enhance the experience and success of HSI students in the evolving generative artificial intelligence through building an education-oriented GPU cluster and designing high-quality related courses. Generative artificial intelligence has shown impressive performance in business, healthcare, and creative industries through revolutionary technologies, which will benefit society in the future.

However, some key challenges including the lack of generative artificial intelligence-related workforce in the short term and the stable workforce pipeline in the long term are obstacles to sustainable prosperity. To solve these challenges, this project will (1) build a powerful GPU cluster to improve the computational infrastructure as the base of generative artificial intelligence, (2) enrich the existing course materials and develop new courses/initiatives to provide students with more hands-on experiences and scientific developments, and (3) cultivate students and future workforce in generative artificial intelligence from two aspects, including technology-based programs (e.g., computer science) and application-based programs (e.g., public health).

Finally, the outcome of this project will advance curricula in generative artificial intelligence-related STEM disciplines, attract more students to pursue education, and meet critical workforce development needs. As a Minority-Serving Institution and Hispanic Serving Institution, this project will help University of Nevada Las Vegas (UNLV) to significantly expand such opportunities for students from varied socio-economic and socio-demographic communities.

The project has three specific aims: (1) to build immersive learning experiences for the rapidly growing student interest in generative artificial intelligence, (2) to enable computation-intensive education and research across disciplines, and (3) to address long-term workforce development needs in generative artificial intelligence fields. By providing modern computational resources, this GPU cluster will support hands-on learning for students, allowing them to work with cutting-edge generative artificial intelligence techniques, including generative adversarial networks, diffusion models, and large language models.

The research methods include integrating the GPU cluster into coursework, utilizing state-of-the-art generative artificial intelligence algorithms and deep learning frameworks, enabling projects that involve processing high-dimensional image or text datasets, and fostering computationally intensive interdisciplinary collaborations. Expected outcomes will increase student engagement and expertise in a broad area of artificial intelligence.

Also, this project will produce new courses for both undergraduate and graduate students and stimulate growth in course enrollments as students gain access to resources that significantly enhance their learning and practical experience. Additionally, this whole project will prepare students with adequate experience as the future workforce in different domains of generative artificial intelligence careers.

Results from this project will be disseminated through academic publications, community outreach, and expanded partnerships with local and regional institutions. As the only R1 university in southern Nevada, an EPSCOR jurisdiction, establishing this infrastructure at UNLV will empower underrepresented students with competitive skills, address existing disparities, and advance educational equity.

The HSI Program aims to enhance undergraduate STEM education and build capacity at HSIs. Projects supported by the HSI Program will also generate new knowledge on how to achieve these aims.

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.