Creating a High-Performance Computing Virtual Environment for Artificial Intelligence Education

This project aims to serve the national interest by developing a virtual environment for learning Artificial Intelligence (AI). Advances in AI are transforming research and development across industries, government, and academia. A key challenge in learning AI is that its solutions are less transparent than traditional algorithmic solutions that mimic high-level human reasoning.

AI solutions are often a black box, lacking interpretability, which makes it hard to gain insights, understand, and ultimately develop knowledge in AI. This project will develop a real-time interactive tool that will promote the development of AI knowledge and practices of learners from all disciplinary backgrounds. Such a tool will inform the design of learning interventions that will make abstract and complex topics more accessible.

The outcomes of this project will support learning in AI with the scaffolding of interactive visualization, according to tenets of embodied cognition theories. The envisioned tool will be designed from the ground up to best support AI education through interactive visualization that is either immersive, using virtual reality headsets, or non-immersive, on conventional displays.

The project will focus on foundational topics in AI and will contribute to discipline-based education research in AI education. The findings will generate new knowledge about students’ conceptual understanding of AI constructs and procedural understanding of AI practices. The visualization environment’s learning effectiveness will be validated iteratively first in a pilot and then in an experimental stage, with a diverse group of 250 learners from an interdisciplinary community at Purdue University.

The NSF IUSE: EHR 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.