HSI Pilot Project: Enhancing Engineering Statistics Experiential Learning for AI Careers through Visualization and Gamification Approach

Texas A&M International University, Laredo TX

Investigators

Abstract

With support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this Track 1 project aims to better prepare students at Texas A&M International University (TAMIU) to develop artificial intelligence (AI) algorithms. Statistics is the backbone of AI algorithms and is often overlooked in education. TAMIU engineering and math students have low retention and success rates in introductory statistics courses and often have difficulty persisting in their overall course of study. With these challenges, the project will enhance the curriculum in two statistics courses offered in our engineering and mathematics programs. Specifically, visual learning, data visualization methods, and gamification (e.g. point scoring, competition with others, etc.) will be applied to real-world industry-based AI projects. It is expected that using AI to enhance the introductory statistics courses for engineering and math students will lead to increased retention rates and prepare them for interesting AI career opportunities in a variety of industries. The specific aim of the project is to equip students with statistical literacy to excel in AI-related fields or careers by integrating AI concepts and methods into statistics curricula. The research method to be used is an in-situ longitudinal intervention that uses a randomized control trial to select the cohorts and, through comparison of the cohort with intervention and without intervention, creates a body of knowledge and an opportunity to understand the combined application of these two tools in enhancing engineering education in AI. The interdisciplinary mix of team members will allow the researchers to hybridize pedagogical principles and strategies and apply these in the research design for creating high-impact, hands-on engineering enhancement activities in the Statistics courses. Additionally, the effectiveness of gender matched mentoring on student retention and success will be studied. Expected results include the products and techniques developed through this project that can readily be scaled and exported to other STEM disciplines, such as chemistry and physics. New knowledge will be gained about improving undergraduate education in AI and statistics. 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.