  Reasoning about code and explaining its purpose are fundamental skills for
computer scientists. There has been extensive research in the field of
computing education on the relationship between a student's ability to explain
code and other skills such as writing and tracing code. In particular, the
ability to describe at a high-level of abstraction how code will behave over
all possible inputs correlates strongly with code writing skills. However,
developing the expertise to comprehend and explain code accurately and
succinctly is a challenge for many students. Existing pedagogical approaches
that scaffold the ability to explain code, such as producing exemplar code
explanations on demand, do not currently scale well to large classrooms. The
recent emergence of powerful large language models (LLMs) may offer a solution.
In this paper, we explore the potential of LLMs in generating explanations that
can serve as examples to scaffold students' ability to understand and explain
code. To evaluate LLM-created explanations, we compare them with explanations
created by students in a large course ($n \approx 1000$) with respect to
accuracy, understandability and length. We find that LLM-created explanations,
which can be produced automatically on demand, are rated as being significantly
easier to understand and more accurate summaries of code than student-created
explanations. We discuss the significance of this finding, and suggest how such
models can be incorporated into introductory programming education.
