Preparing Students for Artificial Intelligence and Emerging Tech Careers through Industry Partnerships and Experiential Learning
Bowie State University, Bowie MD
Investigators
Abstract
Artificial intelligence (AI) is changing many industries and creating a strong need for workers with the right skills. Many college programs teach theory, but students also need enough real-world practice or guidance to help them move smoothly into good jobs. This project aims to create and test innovative pathways to prepare students for AI careers. It includes the combination of hands-on training, career mentoring, and direct work with industry partners. The goal is to help more students gain the experience and support they need to enter high-demand AI jobs. This work is positioned to strengthen the national AI workforce, improve access to technology careers, and support U.S. growth and innovation. Project plans include a three-part approach: (1) short certification courses with applied projects, (2) career development that teaches job skills and builds industry connections, and (3) paid internships with technology companies. Plans also involve the creation of short courses in AI ethics, machine learning, research skills, and how to use AI tools in real settings. Graduate mentors and industry professionals will be trained to guide students closely. With the implementation of the education and training activities, the project includes plans to study how hands-on learning, mentorship, and industry work improve skills, job readiness, and hiring outcomes. The results are expected to provide proven strategies and tools that other schools can use to grow the AI workforce nationwide. The ExLENT Program, supported by the NSF TIP and EDU Directorates, seeks to support experiential learning opportunities for individuals to increase their interest in and access to career pathways in emerging technology fields. 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.
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