Imagine an AI teaching assistant that doesn't just grade your code, but actually helps you understand *how* you code. That's the promise of new research exploring the use of Large Language Models (LLMs) to analyze programming process data. Traditionally, computer science education has focused on the final product: does the code work? But what about the journey? How do students approach a problem, how do they debug, and how do their coding strategies evolve? These questions are crucial for understanding and improving the learning process, particularly for beginners. This research utilizes LLMs to analyze the step-by-step evolution of student code, looking for patterns and potential areas for improvement. Think of it like having an AI coach that reviews your coding game tape, offering personalized advice on your strategy, not just your execution. In a case study, researchers fed code snapshots from student assignments to different LLMs, asking them to summarize the coding process and provide feedback. The results are fascinating. The LLMs were able to identify common issues like jumping into coding without planning, leaving behind commented-out code, and struggling with debugging. They even offered encouraging feedback, praising students for adding comments and experimenting with new concepts. However, the research also uncovered a key challenge: differentiating between feedback on the *process* of coding and the code itself. LLMs, like humans, tend to focus on the code's quality rather than the coder's approach. This highlights the need for more sophisticated prompts and potentially new training methods to help LLMs understand the nuances of human coding behavior. Despite this challenge, the potential for AI-driven coding coaches is immense. As LLMs improve, they could become invaluable tools for educators, offering personalized feedback that helps students develop more effective coding strategies, ultimately leading to greater success and less frustration in the world of programming.