  Parsons problems provide useful scaffolding for introductory programming
students learning to write code. However, generating large numbers of
high-quality Parsons problems that appeal to the diverse range of interests in
a typical introductory course is a significant challenge for educators. Large
language models (LLMs) may offer a solution, by allowing students to produce
on-demand Parsons problems for topics covering the breadth of the introductory
programming curriculum, and targeting thematic contexts that align with their
personal interests. In this paper, we introduce PuzzleMakerPy, an educational
tool that uses an LLM to generate unlimited contextualized drag-and-drop
programming exercises in the form of Parsons Problems, which introductory
programmers can use as a supplemental learning resource. We evaluated
PuzzleMakerPy by deploying it in a large introductory programming course, and
found that the ability to personalize the contextual framing used in problem
descriptions was highly engaging for students, and being able to customize the
programming topics was reported as being useful for their learning.
