ChatGPT is a state-of-the-art language model that facilitates natural language interaction, enabling users to acquire textual responses to their inquiries. The model's ability to generate answers with a human-like quality has been reported. While the model's natural language responses can be evaluated by human experts in the field through thorough reading, assessing its structured responses, such as lists, can prove challenging even for experts. This study compares an openly accessible, manually validated list of "course concepts," or knowledge concepts taught in courses, to the concept lists generated by ChatGPT. Course concepts assist learners in deciding which courses to take by distinguishing what is taught in courses from what is considered prerequisites. Our experimental results indicate that only 22\% to 33\% of the concept lists produced by ChatGPT were included in the manually validated list of 4,096 concepts in computer science courses, suggesting that these concept lists require manual adjustments for practical use. Notably, when ChatGPT generates a concept list for non-native English speakers, the overlap increases, whereas the language used for querying the model has a minimal impact. Additionally, we conducted a qualitative analysis of the concepts generated but not present in the manual list.