• Debugging. LLMs, as generative models, may not be completely error-free when generating code, leading to bugs in execution. However, this shortcoming can be viewed as a feature, rather than a bug, in learning and education . The capability of the LLM to correct itself by interacting with error messages provides a natural way of debugging, which helps students learn coding gradually with chains of reasoning steps. The requirement of user interaction with LLMs prior to executing code also lowers the risk of over-reliance on LLMs when chemistry students learn to understand the setup of quantum chemistry calculations and basic rules of coding.