Imagine a world where learning to code is easier, where complex concepts are broken down into digestible steps, and where helpful comments guide you through every twist and turn of a program. Large Language Models (LLMs) like those powering ChatGPT and other AI tools are showing promise in making this a reality. A recent research paper explored how well these LLMs can generate code comments that are actually *helpful* for novice programmers, comparing AI-generated comments to those written by experienced humans. The researchers used a dataset of relatively simple Java problems from LeetCode, a popular platform for practicing coding skills. They prompted several LLMs—GPT-4, GPT-3.5-Turbo, and Llama 2—to generate comments explaining the solutions. Then, they brought in expert programmers to evaluate the quality of these comments based on several factors: clarity, beginner-friendliness, explanation of key concepts, and step-by-step guidance. The results? GPT-4 performed remarkably well, often producing comments comparable in quality to those written by the human experts. It excelled at explaining tricky concepts in a way that beginners could grasp and at breaking down the code into manageable steps. Llama 2, on the other hand, struggled to keep its explanations simple and often lacked the detail needed for a novice to fully understand the code. While GPT-3.5 fell somewhere in between, it still showed a good understanding of how to explain code clearly. This research highlights the exciting potential of LLMs to personalize the learning experience for new coders. Imagine AI tutors that can provide tailored explanations, point out common mistakes, and offer support just like a human teacher. However, the study also reveals that not all LLMs are created equal. More research is needed to improve these models further, ensuring they provide truly helpful and supportive guidance for those just starting their coding journey.