  Worked examples, which present an explained code for solving typical
programming problems are among the most popular types of learning content in
programming classes. Most approaches and tools for presenting these examples to
students are based on line-by-line explanations of the example code. However,
instructors rarely have time to provide explanations for many examples
typically used in a programming class. In this paper, we assess the feasibility
of using LLMs to generate code explanations for passive and active example
exploration systems. To achieve this goal, we compare the code explanations
generated by chatGPT with the explanations generated by both experts and
students.
