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. In addition to boosting the students’ engagement, such a technology also enables individual instructional scaffolding of problem progressions and data-driven problem complexity research in a wide spectrum of educational domains, such as algebra, language comprehension, or programming.

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 synthesizes the problem narrative and its mathematical model from the logical encoding of the specification as a labeled logical plot graph. Human judges found our problems as solvable as the textbook problems, with a slightly more artificial language.