Imagine a classroom where teachers can instantly understand how each student learns, predict their future performance, and provide personalized feedback. This isn't science fiction, but the promise of a new AI technique called Explainable Few-Shot Knowledge Tracing. Traditional methods for tracking student knowledge often rely on vast amounts of data, making them impractical in real-world classrooms. This new approach leverages the power of large language models (LLMs), the same technology behind ChatGPT, to analyze student performance with just a few examples of their work. Think of it like a teacher who can quickly grasp a student's understanding after seeing them solve a handful of problems. This method not only predicts whether a student will answer a question correctly but also explains *why*, offering valuable insights into their learning process. By understanding the 'how' and 'why' behind student learning, teachers can tailor their instruction to individual needs, identify areas where students struggle, and provide more effective support. This personalized approach has the potential to revolutionize education, making learning more efficient and engaging for every student. While this technology is still in its early stages, the initial results are promising. Researchers have found that LLMs can perform as well as, or even better than, traditional methods that require much more data. However, challenges remain, such as the cost of running these powerful models and the need for better ways to select the most informative examples of student work. As researchers continue to refine these techniques, Explainable Few-Shot Knowledge Tracing holds the potential to unlock a deeper understanding of student learning and transform the future of education.