Using GenAI to Support Applied Learning in

Principles of Economics Courses

Abstract

Introductory economics instructors often seek ways to connect core concepts to current data. This paper presents a practical framework for using Generative AI (GenAI) tools, especially ChatGPT, for activity design and NotebookLM for source-grounded student work, to support applied learning in principles courses in economics. We situate the approach within backward design and illustrate it with two classroom-ready examples: inflation (macroeconomics) and minimum wage (microeconomics). In each example, students use GenAI to extract information from public reports and interpret the economic data and its relevance. We also discuss ethical guardrails, instructor oversight, and preliminary implementation feedback. The paper's contribution is practical: it offers adaptable instructional designs that help instructors integrate current economic data while supporting applied learning and economic reasoning through the use of GenAI tools. 

Keywords: Generative AI, Pedagogy, Digital tools in education, Principles of economics JEL classification: A20

JEL Classification: A20, A22, I21, O33