Fostering Generative AI Literacy in Economics: A Hands-on Approach

A five-step, active-learning framework for building AI literacy in economics courses. Beck & Brodersen (2025) present a hands-on approach in which students analyze real-world scenarios (song lyrics, news articles), generate ChatGPT responses to the same questions, and then critically evaluate the AI output against their own answers. The approach deepens economic concept understanding while fostering engagement, critical thinking, and AI Literacy.

Key Findings

This paper addresses the reality that students use Generative AI tools like ChatGPT "with or without educator guidance," arguing that Higher Ed institutions should therefore integrate AI literacy into the curriculum rather than restrict it. The authors present a structured five-step activity that pairs theoretical knowledge with practical application and critical evaluation of AI outputs, rooted in established Active Learning strategies such as Think-Pair-Share.

The five-step framework. Each activity opens with a real-world scenario — a news article, song lyrics, or personal experience — that illustrates economic concepts like opportunity cost, elasticity, or decision-making under constraints. Students first analyze the scenario and answer guided questions independently (Step 1). They then use ChatGPT to generate responses to the same questions using a standardized prompt, ensuring comparability (Step 2). In Step 3, students critically evaluate the AI-generated responses for accuracy, clarity, and depth, comparing them to their own work. Step 4 involves critical reflection and answer refinement, assessing how AI can enhance or hinder economic analysis. The activity closes with a collaborative class discussion on broader implications (Step 5). This structure exemplifies an Learning Design that sequences students through analysis, generation, evaluation, and reflection.

Learning objectives. The activities aim to deepen understanding of economic concepts, foster Critical Thinking, and enhance AI literacy. By completion, students should be able to apply economic concepts to real-world scenarios, evaluate AI outputs, refine analytical skills, develop AI literacy, and collaborate and reflect. These objectives align with a broader competency-based vision of preparing students for an AI-driven workforce.

Sample activity: price elasticity of demand. The paper's illustrative activity uses Tim McGraw's song "Just to See You Smile" to teach price elasticity of demand. Students analyze the lyrics and answer questions about opportunity cost and willingness to pay, then prompt ChatGPT with a standardized question. Notably, ChatGPT frequently describes the depicted behavior as "perfectly elastic" demand — which is incorrect; the correct answer is (perfectly) inelastic demand. This discrepancy drives an engaging class discussion about the importance of critically evaluating AI-generated content and validating outputs, directly supporting Reducing AI Misuse through skepticism rather than prohibition.

Engagement and feedback. Integrating GenAI into introductory and graduate-level economics courses received overwhelmingly positive feedback. Students found ChatGPT helpful in grasping economic concepts while recognizing its limitations, such as vague or occasionally inaccurate responses. Concerns about cheating were minimal. The hands-on approach significantly increased Student Engagement, creating a collaborative, low-pressure learning environment in which students could experiment with GenAI without fear of being accused of cheating.

Benefits and challenges. Benefits include transforming passive learning into active, student-centered classrooms, fostering higher-order thinking (analysis, synthesis, evaluation), and building both GenAI and digital literacy. Challenges include the risk of over-reliance on AI undermining independent critical thinking and creativity, the significant educator effort required to redesign curricula, and ethical concerns about bias, academic integrity, data privacy, and equitable access to AI resources, particularly in resource-constrained settings.

Implications

The framework offers a concrete, adaptable model for integrating Generative AI into discipline-specific instruction without requiring extensive course redesign. Its adaptability across topics, proficiency levels, and course formats (in-person, online, hybrid) makes it a practical resource for instructors seeking to build AI Literacy within authentic disciplinary contexts.

By foregrounding the critical evaluation of AI outputs — including exposing their variability in quality and accuracy — the approach reframes GenAI as a tool to be scrutinized rather than trusted wholesale. This directly addresses Reducing AI Misuse concerns: rather than banning tools, educators design activities that demonstrate AI is not foolproof, thereby mitigating over-reliance while equipping students with validation skills. The minimal cheating concerns reported suggest that embedding AI use into transparent, active tasks can reduce the incentive for misuse that arises when AI use is covert or prohibited.

The findings also speak to Learning Design practice in Higher Ed. The five-step sequence — independent analysis, AI generation, critical evaluation, reflection, and collaboration — provides a transferable template for any instructor wanting to develop Critical Thinking and AI literacy. The emphasis on collaborative, low-pressure discussion links GenAI integration to broader Student Engagement and Active Learning agendas in the economics classroom and beyond.

Connected Concepts

• AI Literacy
• Generative AI
• Critical Thinking
• Higher Ed
• Student Engagement
• Learning Design
• Active Learning
• Reducing AI Misuse

Connected Articles

• Hingle Collaborative AI Literacy 2025 — Systematic Review of Collaborative Learning Activities for Promoting AI Literacy
• AI Literacy Continuum Higher Education — A Practical Five-Stage Developmental Continuum for AI Literacy in Higher Education
• Scaffolding Critical Engagement GenAI Minority Students — Scaffolding Critical Engagement with GenAI for Underrepresented Students
• Student Regulatory Awareness GenAI — Student Regulatory Awareness of Generative AI Use

Citation

Beck, S., & Brodersen, D. (2025). Fostering Generative AI Literacy in Economics: A Hands-on Approach. Journal of Economics Teaching, 10(4), 285–295. DOI: 10.58311/jeconteach.