Imagine being able to predict a student’s grasp of algebra or calculus just by analyzing their past quiz answers. This isn’t science fiction; it's the exciting reality of Knowledge Tracing (KT). But traditional KT methods have limitations. They rely on manually tagging questions with specific knowledge concepts, which is time-consuming and error-prone. They also often treat questions as mere identifiers, ignoring the rich meaning embedded within them. New research introduces KCQRL, a framework that revolutionizes how we track and understand student learning. It uses the power of Large Language Models (LLMs) to automatically annotate questions with relevant knowledge components, going beyond simple keyword matching by actually solving the problems step-by-step. This approach not only eliminates manual tagging, it also digs into the underlying logic of each question, creating a far more nuanced understanding of the knowledge involved. KCQRL then takes things a step further with a clever trick called contrastive learning. This method helps the AI learn detailed, context-rich representations of questions and concepts. By connecting the meaning behind the question to the concepts it tests, KCQRL supercharges the accuracy of learning predictions. This isn’t just about improving test scores. KCQRL empowers educators to create genuinely personalized learning paths. By understanding precisely where a student is struggling, they can target interventions and customize resources with greater accuracy. This personalized approach can lead to significant improvements in educational outcomes, fostering deeper understanding and a love of learning. While promising, KCQRL highlights the challenges of developing robust AI models for education. As educational datasets grow and AI models become more sophisticated, we can expect even more personalized and effective learning experiences in the future. The ability to tap into the intricate workings of student understanding is a game-changer for education. This research brings us one step closer to unlocking the full potential of every learner.