  This paper systematically investigates the generation of code explanations by
Large Language Models (LLMs) for code examples commonly encountered in
introductory programming courses. Our findings reveal significant variations in
the nature of code explanations produced by LLMs, influenced by factors such as
the wording of the prompt, the specific code examples under consideration, the
programming language involved, the temperature parameter, and the version of
the LLM. However, a consistent pattern emerges for Java and Python, where
explanations exhibit a Flesch-Kincaid readability level of approximately 7-8
grade and a consistent lexical density, indicating the proportion of meaningful
words relative to the total explanation size. Additionally, the generated
explanations consistently achieve high scores for correctness, but lower scores
on three other metrics: completeness, conciseness, and specificity.
