---
title: "Personalized Mathematical Word Problem Generation"
authors:
  - "Oleksandr Polozov"
  - "Eleanor O'Rourke"
  - "Adam M. Smith"
  - "Luke Zettlemoyer"
  - "Sumit Gulwani"
  - "Zoran Popovic"
doi: ""
url: "https://ijcai.org/proceedings/2015/60"
pdf: "https://ijcai.org/proceedings/2015/0060.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "381-387"
track: "Main Track"
keywords:
---

# Personalized Mathematical Word Problem Generation

Oleksandr Polozov, Eleanor O'Rourke, Adam M. Smith, Luke Zettlemoyer, Sumit Gulwani, Zoran Popovic

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 381-387.

## Abstract

Word problems are an established technique for teaching mathematical modeling skills in K-12 education. However, many students find word problems unconnected to their lives, artificial, and uninteresting. Most students find them much more difficult than the corresponding symbolic representations. To account for this phenomenon, an ideal pedagogy might involve an individually crafted progression of unique word problems that form a personalized plot. We propose a novel technique for automatic generation of personalized word problems. In our system, word problems are generated from general specifications using answer-set programming (ASP). The specifications include tutor requirements (properties of a mathematical model), and student requirements (personalization, characters, setting). Our system takes a logical encoding of the specification, synthesizes a word problem narrative and its mathematical model as a labeled logical plot graph, and realizes the problem in natural language. Human judges found our problems as solvable as the textbook problems, with a slightly more artificial language.