Targeted Infusion Project: Infuse Data Science into the Mechanical Engineering Undergraduate Curriculum in Materials, Manufacturing, and Engineering Design

Data has become central to how engineers design products, operate factories, and keep systems safe, and employers increasingly seek graduates who can combine traditional engineering skills with the ability to learn from data. Yet many undergraduate engineering curricula have little room to add new courses, and many students are unable to hone these essential skills prior to graduation. To address this urgent need, this project transforms mechanical engineering undergraduate education at Prairie View A&M University, a Historically Black College and University, by weaving data science and artificial intelligence (AI) into existing courses focused on materials, manufacturing, and engineering design. Curricular enhancements will be made by embedding short, hands-on data science projects into required mechanical engineering courses, training faculty in pedagogical practices to effectively integrate data science and AI and establishing a laboratory where students can work with authentic data drawn from research and industry collaborators. Incoming freshman students will learn computer programming through a summer bridge activity and later apply these skills to real engineering problems through carefully curated curricular design and experiential learning opportunities. Ultimately, the project aims to prepare a workforce ready students, positioned to compete in a data-driven economy. The research team integrates a progressive sequence of data science and machine learning modules across mechanical engineering courses in materials science, manufacturing processes, and machine design, to ensure that students receive repeated, reinforcing exposure to data-driven methods in authentic engineering contexts. Module content advances from regression and surrogate modeling of process, followed by structure to property relationships, through feature extraction and interpretable methods that explain model predictions, to generative design and multimodal approaches for tailored material and thermal properties. Instruction draws on datasets generated in the research laboratories of STEM faculty, including additive manufacturing process data, topology-optimized design data, and physics-based simulation data, which keeps the curriculum aligned with active research. Faculty development is delivered through certified workshops, self-paced tutorials, and faculty learning communities that build lasting instructional capacity. A data science laboratory equipped with high-performance computing supports project-based learning, annual competitions, and undergraduate research, while collaborations with industry supply authentic datasets, mentorship, and career pathways. A mixed-methods evaluation with independent external review measures gains in student learning, engagement, and readiness, and isolates the contribution of each activity. The project generates open teaching modules, datasets, and coding examples for use by other institutions, contributing a scalable model for integrating artificial intelligence and data science into engineering education. This award is funded by the Historically Black Colleges and Universities Undergraduate Program (HBCU-UP), through Targeted Infusion Projects, which supports the development, implementation, and study of evidence-based, innovative models and approaches for improving the preparation and success of HBCU undergraduate students to pursue science, technology, engineering, or mathematics (STEM) graduate programs and/or careers. 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.