Targeted Infusion Project: Exposing Students to the Social Relevance and Trustworthiness of Artificial Intelligence

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The Historically Black Colleges and Universities Undergraduate Program (HBCU-UP) through Targeted Infusion Projects supports the development, implementation, and study of evidence-based innovative models and approaches for improving the preparation and success of HBCU undergraduate students so that they may pursue science, technology, engineering, or mathematics (STEM) graduate programs and/or careers. The project at Fayetteville State University will address the impact of Artificial Intelligence (AI) approaches on society and its potential benefits. Almost every area of human knowledge from the sciences and engineering, humanities and the arts, can be potentially enhanced by AI. Computing applications are being developed and deployed to provide AI support for data-driven decisions that have real effects on the lives of people. AI algorithms have been adopted to solve different problems in healthcare, law enforcement, military, and government, to name only a few major areas. While it will take more time and effort to realize its full potential in these areas, the risks of adopting AI is an urgent and important problem because it can potentially cause harm when it is prematurely deployed before being tested for trustworthiness. This project is based on AI research and educational research that suggest computing and non-computing curricula can benefit from customized modules that expose students to the knowledge of AI and the trustworthiness of AI models. The trustworthiness and social relevance of AI is a profoundly multi-disciplinary effort, and the project is leveraging the knowledge of researchers and educators from various disciplines. The Process Oriented Guided Inquiry Learning (POGIL) will be used for creating inquiry-based activities that will be completed by students in teams. Most activities involve hands-on, experimental work with AI models that are executed within a sandbox software environment. Through immersion in this process students will understand how AI models work and how to assess trust, which is essential when taking actions based on the predictions of models, or when deploying a new model. This guided process also provides insights into the inner working of AI models, which students can use to transform an untrustworthy model or prediction into a trustworthy one. A novel repository of educational materials will be created to support the adoption of AI in non-computing disciplines and to strengthen the computing curricula with content that has traditionally not received adequate coverage. This project adds to the growing body of knowledge on how to motivate the study of AI, how to prepare students for the modern-day workplace where both AI skills and domain knowledge are important, and how to train informed and socially responsible creators and users of AI.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.