Improving Programming Skills of Engineering Students at Historically Black Colleges and Universities Using AI-enhanced Personalized Adaptive Learning Tools

Prairie View A & M University, Prairie View TX

Investigators

Abstract

This project aims to serve the national interest by building online Artificial Intelligence (AI) enhanced personalized adaptive learning (PAL) tools to improve engineering students' programming skills at Historically Black Colleges and Universities (HBCUs). Programming skills are imperative for various simulations in engineering education. As the COVID-19 pandemic has fundamentally changed course delivery methods, online teaching tools have become indispensable. The project team plans to develop a series of online learning modules for programming teaching and learning, recommend PAL paths via deep reinforcement learning, and build a smart programming assistant (SPA) using AI techniques to improve student learning. The proposed online AI enhanced PAL tools have the potential to integrate a PAL pedagogy with AI techniques such as reinforcement learning to explore how to recommend PAL learning paths and produce appropriate reference source codes for engineering students. The project team intends to work on three primary tasks. First is to develop basic online tools with open application programming interfaces that are able to connect with novel AI algorithms to improve student learning. Second is to recommend PAL paths using deep reinforcement learning to maximize learners' engagement in programming learning at proper difficulty levels. Third, and finally, is to design an SPA via deep learning-based language models to generate reference codes to help learners with programming activities. The proposed tools have the potential to help to teach other engineering courses. The team intends to disseminate their project results at various webinars, open-source repositories, and education conferences. The proposed tools would be shared with other HBCUs and institutions. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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.