Abstract

Textbook word problems often miss students’ lives; AI can fix that only when teachers stay in the driver’s seat. This article shows a practical, repeatable way to use GPT-4 to design mathematically rigorous tasks that feel relevant to students. I present an iterative prompting approach that pairs content goals with two added parameters: [SC] Social Context and [PA] Pedagogical Approach, alongside the familiar task elements (object, shape, properties, target, position). Drawing on examples from a Detroit high school, the paper traces how simple geometry items were reshaped into modeling tasks, civic case studies, and a systems-based investigation of environmental data. For immediate classroom use, the article includes: (1) a step-by-step “try it next week” recipe, (2) a revision checklist for catching quantity/wording issues, and (3) a rubric for analyzing the results. A brief classroom pilot (a trigonometry quiz with local contexts) illustrates gains in student talk, diagramming, and flexible reasoning, as well as common pitfalls and how to revise AI drafts. The goal is not to automate curriculum, but to amplify teacher judgment and help answer the daily question, “Why are we doing this?”, with tasks that connect mathematics to the world students inhabit.

Keywords

Word problems, Pedagogy, Post-Constructivism, Relevant mathematics

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