AI policy for an intro economics course, with examples

Sample AI syllabus policies for introductory economics at three levels of use, with clauses for problem sets, graphs and showing work.

Short answer: in introductory economics, most of the learning happens on problem sets, and current AI tools answer many problem-set questions well. Your policy should say whether problem sets are practice (where AI help can be fine if students still do the reasoning) or assessment (where it usually is not), and it should require students to show their work in a way you can check. Pick one of the three policies below, then add the clauses that fit your course.

What makes economics problem sets different

  • Many question types are easy for AI. A 2026 study rated GPT-4o’s answers to 260 introductory economics questions and found it did well on text discussion questions and purely numerical questions (Liu et al., 2026).
  • Graphs are harder for AI, but not a fix. The same study found GPT-4o did poorly on graphical questions, especially ones that need numerical reasoning from a graph. The authors caution against treating graph questions as an “AI-proof” solution and recommend mixed-format assessments aligned with course goals.
  • Showing work is the evidence. Setting up the model, drawing the shift and explaining the mechanism are the skills an intro course builds. A correct final number says little about whether a student can do them.

Three sample policies

Copy the one closest to your course and edit the bracketed parts.

1. No generative AI on graded work

Generative AI tools (for example ChatGPT, Claude or Gemini) may not be used on problem sets, quizzes or exams in this course. Problem sets are where you practice setting up and solving economic models, and that practice is what prepares you for the exams. You may work with classmates [if allowed], use the textbook and lecture notes, and come to office hours. If you are unsure whether something is allowed, ask before you submit.

2. AI for studying, not for answers

You may use generative AI tools to study: to explain a concept from lecture, generate practice questions, or check your understanding of a topic after you have attempted the problem set. You may not use them to produce answers, graphs or explanations that you submit. Your submitted work should show each step: the setup, the graph and the reasoning. If you used an AI tool while studying, add one sentence at the end saying how.

3. AI allowed with disclosure on problem sets

Problem sets in this course are practice and are graded for [completion / effort]. You may use generative AI tools on them if you disclose which tool you used and for what. You still need to show your work, and you are responsible for checking anything an AI tool tells you, since these tools make mistakes, especially with graphs. AI tools may not be used on quizzes or exams, which are where your understanding is assessed.

Clauses worth adding for economics

  • Show the setup. “Answers without the setup (equations, labeled graph, or the reasoning step) will not receive full credit, whether or not AI was used.”
  • Explain the mechanism in your own words. Ask for one or two sentences on why a curve shifts or why an equilibrium changes. This is short to grade and hard to outsource well.
  • What to disclose. Name the tool, what it was used for and the prompts that shaped the answer (KU CTE).
  • Match the policy to the weight. If problem sets are low stakes and exams are high stakes, a permissive problem-set policy with in-class exams is a coherent design. Say so in the syllabus so students understand why.

Write yours in a few minutes

The free AI policy generator asks a few questions about your course and assessments and produces a policy you can paste into your syllabus.

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