A Tutorial on Teaching Data Analytics with Generative AI

Abstract

This tutorial addresses the challenge of incorporating large language models (LLMs), such as ChatGPT, in a data analytics class. It details several new in-class and out-of-class teaching techniques enabled by AI. For example, instructors can parallelize instruction by having students interact with different custom-made GPTs to learn different parts of an analysis and then teach each other what they learned from their AIs. For another example, instructors can turn problem sets into AI tutoring sessions, whereby a custom-made GPT guides a student through the problems, and the student uploads the chatlog for their homework submission. For a third example, you can assign different labs to each section of your class and have each section create AI assistants to help the other sections work through their labs. This tutorial advocates the programming in the English paradigm, in which students express the desired data transformations in prose and then use AI to generate the corresponding code. Students can wrangle data more effectively by programming in English than by manipulating in Excel. However, some students will program in English better than others, so you will still derive a robust grade distribution (at least with current LLMs).