Debugging is a crucial skill for aspiring programmers. However, creating diverse and effective debugging exercises is time-consuming for educators. A new AI-powered tool called BugSpotter is changing that. BugSpotter leverages large language models (LLMs) to automatically generate realistic, buggy code snippets from problem descriptions. These buggy snippets then become engaging debugging exercises for students. Instead of simply fixing the code, students use BugSpotter to design failing test cases, forcing them to deeply understand the problem specifications and pinpoint the logic flaws. This approach goes beyond traditional debugging by encouraging critical thinking and a systematic approach to problem-solving. Researchers tested BugSpotter in a large introductory programming course and found that the AI-generated exercises were comparable in difficulty and effectiveness to those created by instructors. The AI even generated diverse bug types, ranging from errors that pass some tests to those causing runtime crashes. This suggests that AI could play a significant role in automating the creation of educational resources, freeing up instructors to focus on personalized guidance and more complex learning challenges. While promising, BugSpotter currently focuses on single-function problems. Future developments aim to expand its capabilities to encompass more complex programs, fine-tune the AI to mimic common student errors even more closely, and incorporate other exercise types like test-writing and code editing. This research highlights the evolving role of AI in education, showcasing its potential to not only personalize learning but also empower educators with powerful tools for content creation.