Generative Artificial Intelligence Driving More Efficient and Effective Course Optimizations

The purpose of this study was to explore the efficacy of Generative Artificial Intelligence (GenAI) in optimizing online degree courses to enhance student retention and performance. Within the context of online learning platforms, there is a challenge in efficiently and effectively updating course content, which can impact the quality and relevance of the experience. The aim of this pilot was to utilize GenAI to create and augment educational resources, thus improving the clarity, scaffolding, and engagement of the online degree coursework. The methods employed in this pilot study included a quasi-experimental design where a Performance-Based Admissions (PBA) degree course was selected, its content was optimized using GenAI, and the impact was measured using a Difference-in-Differences (DID) analysis comparing optimized and non-optimized courses. The results indicate a 6% increase in course pass rates and significant improvements in midterm pass rates, average final grades, and the percentage of assignments submitted on time within the optimized course. Given these results, we estimate a 3% to 13% positive impact on second term persistence if all first-term courses received a similar GenAI optimization. The impact of this study is significant, demonstrating the potential of GenAI to revolutionize educational practices by providing quickly-produced, high-quality, low-cost content that enhances student learning outcomes. It supports the shift towards student-centered learning approaches and can be particularly beneficial in resource-constrained environments. In conclusion, the study underscores the transformative role of GenAI in education and emphasizes the need for ethical and responsible use to empower students and improve learning outcomes.