Leveraging Custom GPT Bots to Enhance Learning in Economics

Students engage with instructor-designed GPT-powered bots (e.g., ECONOHelper, Owlnomics, Alpha (student bot)) to practice applying economic theories and solving structured case studies. Bots are programmed to withhold direct answers initially, prompting students with guiding questions to stimulate analysis before offering feedback or clarification. For example: In Microeconomics, ECONOHelper helps students interpret supply and demand graphs, calculate elasticity, and analyze pricing strategies for products such as Tesla’s Cybertruck or staple foods. In Macroeconomics, Owlnomics guides students through models such as Aggregate Expenditures (AE) and AD-AS, asking them to predict outcomes of fiscal and monetary policies in real-world-inspired scenarios like “Larissaland” or “Economia.” The bots’ role is not to provide instant answers but to serve as scaffolding tutors that simulate guided practice. Students must first articulate their reasoning, then refine it with bot prompts. Assignments often include screenshots of conversations with the bots, economic models they built (e.g., graphs in Desmos, Excel or Google Sheets), and short written reflections.