Generative AI becoming a co-pilot in lecture halls and labs

While we continue to focus on monitoring the impact of generative AI on student work, it is equally important to explore how educators can incorporate AI into teaching practices to enhance learning outcomes.

A soon-to-be-published paper by US-based colleagues in the Journal of Statistics and Data Science Education titled Generative AI for Data Science 101: Coding without Learning to Code illustrates how AI can be a companion tool in delivering a business statistics course for MBA students.

Teaching introductory statistics at the undergraduate or graduate level poses unique challenges. Many students enrolled in these courses do not aspire to become statisticians or econometricians; for them, completing the course is a stepping stone for careers in psychology, social sciences, or business.

However, some instructors (yours truly included) insist on teaching coding or scripting skills to enable students to analyze data, produce tables, estimate statistical models, and create visualizations. As a result, these courses often focus more on mastering the intricacies of programming languages (such as Python, R, or Stata) than on the statistical concepts that students need to apply in their professional lives.

The referenced paper demonstrates how generative AI enables students to communicate with computers using natural language. Students describe their intended data analysis tasks in everyday language, and the AI generates and executes the necessary scripts to deliver the desired results.

Abstract Should one teach coding in a required introductory statistics and data science class for non-major students? Many professors advise against it, considering it a distraction from the important and challenging statistical topics that need to be covered. By contrast, other professors argue that the ability to interact flexibly with data will inspire students with a lasting love of the subject and a continued commitment to the material beyond the introductory course. With the release of large language models that write code, we saw an opportunity for a middle ground, which we tried in Fall 2023 in a required introductory data science course in our school’s full-time MBA program. We taught students how to write English prompts to the artificial intelligence tool GitHub Copilot that could be turned into R code and executed. In this short article, we report on our experience using this new approach.

This approach shifts the course focus from teaching programming mechanics to emphasizing the interpretation and application of analytical results. Importantly, this does not diminish the need for coding skills in fields where they are essential, such as engineering or computer science. Instead, it advocates for tailoring educational objectives to the needs of the students—for those who must learn coding, it remains critical; for others, the focus should be on interpreting and leveraging analytical outputs rather than struggling with programming syntax.

The referenced paper is particularly relevant to a book on business statistics that I have been working on for the past five years. Progress has been slow, partly due to rapid technological changes that have necessitated frequent revisions. Generative AI, in particular, has rendered most of my earlier writings almost obsolete.

That said, I am optimistic about AI's potential in training young scholars in analytics. Generative AI allows us to bypass the steep learning curve of coding and instead focus on critical thinking and inference. Additionally, traditional statistics and econometrics textbooks often rely on sanitized datasets—complete, well-documented, and free of real-world messiness. These datasets do not reflect the challenges researchers face in practice, where data are often messy, incomplete, poorly coded, and expensive to acquire.

We can bring real-world messy data into the labs with generative AI, teaching students and non-student learners to navigate its complexities and derive actionable insights. This shift has the potential to better prepare them for the realities of professional work in analytics.