NSF Award
2320951
Democratization of artificial intelligence (AI) technologies, such as large language models and chatbots, has accelerated the need for an AI ready workforce. To meet this challenge, the project aims to instill AI readiness in a broad spectrum of users and researchers of advanced cyberinfrastructure (CI), so they can productively and harmoniously use AI for secure big data analysis. The project directly involves eighteen science and engineering faculty members, ten industry experts, and over a thousand students across partner universities and colleges, including several minority serving institutions. By promoting AI readiness across diverse populations with accessible and relatable learning materials and holistic professional development, the project aims to level the playing field for those who feel left behind via bottom-up empowerment. Collective impact guides the team's concerted effort to broaden participation from underrepresented groups such as women, minorities, and veterans. All training materials have reproducibility built in by design to facilitate adoption by a broad range of learners, including those not currently attending college or otherwise having limited access to computational or traditional educational resources.<br/><br/>Advanced cyberinfrastructure (CI) enabled artificial intelligence (AI) with big data analysis is an important enabler for science and engineering (S&E) research. However, as recent mishaps involving chatbots show, AI can produce convincing but incorrect or even harmful results. AI cannot yet be trusted with full autonomy, especially in mission critical applications. There is an emphasis on machine assisted secure data analysis, with human in the loop to enhance safety, security, and reliability. Responding to repeated calls for a multi-faceted approach to AI training, the project studies AI readiness along three dimensions: technical, psychological, and behavioral. Expectation confirmation theory is extended with decision quality and coupled with pervasive technology strategies. This development both advances knowledge and provides a theoretical basis for precise and measurable outcomes. A comprehensive suite of experiential learning modules forms the core of a set of customizable training materials. Critical knowledge and skills are distilled into bitesize units known as flexible micro modules (FMMs) to rapidly upskill learners, who in turn create their own personalized toolkits. An additional layer is built on top to give learners multimodal immersive experiences using extended reality (XR) technologies. These immersive experiences help learners better comprehend difficult concepts and internalize complex sequences of tasks. <br/><br/>This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Division of Graduate Education (DGE) within the NSF Directorate for STEM Education (EDU) and Information and Intelligent Systems (IIS) division within the Computer and Information Science and Engineering (CISE) directorate.<br/><br/>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.
