Math Isn't Neutral: Designing Word Problems with GPT-4 for Relevance.
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| Title: | Math Isn't Neutral: Designing Word Problems with GPT-4 for Relevance. |
|---|---|
| Authors: | Trivedi, Mosum1 |
| Source: | Ohio Journal of School Mathematics. Spring2026, Vol. 102, p34-47. 14p. |
| Subject Terms: | *Word problems (Mathematics), *Educational relevance, *Mathematics education, *Teaching methods, Social context, Artificial intelligence in education, Geometry, Generative pre-trained transformers |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Ohio Journal of School Mathematics is the property of Ohio Council of Teachers of Mathematics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 194541050 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Math Isn't Neutral: Designing Word Problems with GPT-4 for Relevance. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Trivedi%2C+Mosum%22">Trivedi, Mosum</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Ohio+Journal+of+School+Mathematics%22">Ohio Journal of School Mathematics</searchLink>. Spring2026, Vol. 102, p34-47. 14p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Word+problems+%28Mathematics%29%22">Word problems (Mathematics)</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+relevance%22">Educational relevance</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematics+education%22">Mathematics education</searchLink><br />*<searchLink fieldCode="DE" term="%22Teaching+methods%22">Teaching methods</searchLink><br /><searchLink fieldCode="DE" term="%22Social+context%22">Social context</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence+in+education%22">Artificial intelligence in education</searchLink><br /><searchLink fieldCode="DE" term="%22Geometry%22">Geometry</searchLink><br /><searchLink fieldCode="DE" term="%22Generative+pre-trained+transformers%22">Generative pre-trained transformers</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Ohio Journal of School Mathematics is the property of Ohio Council of Teachers of Mathematics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=194541050 |
| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 34 Subjects: – SubjectFull: Word problems (Mathematics) Type: general – SubjectFull: Educational relevance Type: general – SubjectFull: Mathematics education Type: general – SubjectFull: Teaching methods Type: general – SubjectFull: Social context Type: general – SubjectFull: Artificial intelligence in education Type: general – SubjectFull: Geometry Type: general – SubjectFull: Generative pre-trained transformers Type: general Titles: – TitleFull: Math Isn't Neutral: Designing Word Problems with GPT-4 for Relevance. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Trivedi, Mosum IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Spring2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 24725986 Numbering: – Type: volume Value: 102 Titles: – TitleFull: Ohio Journal of School Mathematics Type: main |
| ResultId | 1 |
