Targeted Infusion Project: Shaping the Workforce for Industry 4.0 through AI and Data Science in Architecture, Engineering, Construction, and Digital Media Arts Education

Prairie View A & M University, Prairie View TX

Investigators

Abstract

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 undergraduate students enrolled at HBCUs so that they may pursue science, technology, engineering, or mathematics (STEM) graduate programs and/or careers. This project seeks to enhance student preparedness for the rapidly evolving job market by integrating data science and artificial intelligence into existing and newly developed courses across architecture, engineering, construction science, and digital media. By embedding these emerging technologies into the core curriculum, the initiative helps students gain practical and transferable skills that are increasingly essential in today’s economy. The project supports national efforts to strengthen science and technology education by ensuring that learners across design and built environment fields are equipped with tools that align with the future of the workforce. This activity promotes progress in science, fosters economic advancement, and enhances educational equity through improved access to technological literacy. The project introduces a curricular framework designed to incorporate AI and data science principles into undergraduate coursework in architecture and allied disciplines. It involves developing three new interdisciplinary courses and enhancing four existing courses with technical modules focused on predictive analytics, machine learning, and data visualization using open-source tools. Faculty across participating programs will be trained in the application of these tools and pedagogical strategies. Assessment will be conducted using pre- and post-course surveys, reflective assignments, and learning analytics to measure skill acquisition and engagement. The project’s outcomes include scalable curricular models, accessible teaching resources, and a replicable strategy for AI integration in design-based education that contributes to continued advancement in technology-driven 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.