Technology-Enhanced Student Success Program Short Report

Abstract

This paper presents an LLM-generated “Short Report'' on designing technology-enhanced first-year success programs for first-generation college students at large, public universities. We describe the three-phase process used to generate the report, and the process evolution as we came to understand the LLM’s tendencies, while also highlighting the opportunities and challenges of integrating LLMs in the scholarly practitioner discourse. We explore whether Generative AI can serve as a valuable tool for educational and scholarly contexts. The current iteration of Large Language Models (LLMs) exhibits several critical shortcomings for reliably producing an article fit for publication, including imprecise information, overt hallucinations, problematic oversights, and vague language that would typically render it unsuitable for rigorous academic discussion. However, Generative AI can present us with viable, impactful ideas for improving learning. It is incumbent on us as educators to recognize, evaluate, and develop those ideas for our campuses and communities.