  This paper focuses on procedurally generating rules and communicating them to
players to adjust the difficulty. This is part of a larger project to collect
and adapt games in educational games for young children using a digital puzzle
game designed for kindergarten. A genetic algorithm is used together with a
difficulty measure to find a target number of solution sets and a large
language model is used to communicate the rules in a narrative context. During
testing the approach was able to find rules that approximate any given target
difficulty within two dozen generations on average. The approach was combined
with a large language model to create a narrative puzzle game where players
have to host a dinner for animals that can't get along. Future experiments will
try to improve evaluation, specialize the language model on children's
literature, and collect multi-modal data from players to guide adaptation.
