Grading programming assignments is a time-consuming task for instructors. Imagine if AI could take over, providing students with instant feedback while freeing up educators. New research explores using Large Language Models (LLMs) to automatically generate test suites for introductory programming courses. Researchers fed problem statements and reference solutions to GPT-4, prompting it to create test cases that could be used in an autograder. The results are impressive: LLM-generated test suites correctly identified valid student solutions in the vast majority of cases and were often even more comprehensive than those created by instructors. They even exposed ambiguities in some problem statements, offering opportunities to improve assignment design. This suggests LLMs have significant potential to automate autograding, though some instructor oversight is still necessary. For instance, some LLM-generated tests were invalid because they violated the problem's original constraints. While this approach shows tremendous promise, it raises important questions about the role of AI in education. Can AI truly replicate the nuanced understanding of a human instructor? How can we ensure fairness and prevent bias in automated grading systems? As LLMs become more sophisticated, their integration into educational tools will likely continue to evolve, shaping the future of how students learn to code.