Abstract

This paper tackles a persistent challenge in R-based data analytics education: the traditional "grammar-first" teaching model that forces students to wrestle with syntax before they can actually analyze data. We have developed a pedagogical framework that brings large language models into the mix—not as a crutch, but as collaborative partners throughout the learning process. Over sixteen weeks, students work through AI-assisted assignments, tackle open-AI assessments, and learn to verify rather than blindly trust computational outputs. Our comparative study across parallel course sections will measure whether this approach improves analytical thinking. The goal is to transform students from syntax memorizers into thoughtful analysts who know how to leverage AI intelligently.

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