Imagine a classroom where lessons morph and adapt to each student's unique learning gaps. That's the promise of in-context teaching, a fascinating area of AI research explored in the paper "Toward In-Context Teaching: Adapting Examples to Students' Misconceptions." Traditional teaching often relies on a one-size-fits-all approach, but what if AI could tailor educational examples to address individual misconceptions? This research delves into precisely that, examining how AI models can identify and adapt to a student's learning struggles. The researchers introduce ADAPT, a suite of tools designed to evaluate how effectively AI models can personalize teaching. ADAPT uses simulated student models with specific misconceptions, allowing researchers to test different teaching strategies. They also developed ATOM, a new model that actively infers a student's prior knowledge and selects examples to directly address their misconceptions. The results are promising. ATOM consistently outperforms other models in simulated tests, demonstrating the power of personalized learning. In experiments with real students learning mathematical functions, both ATOM and large language models like GPT-4 showed significant improvements over random example selection. This research highlights the potential of AI to revolutionize education. By understanding how students learn and where they struggle, AI can provide targeted support, leading to more effective and engaging learning experiences. While challenges remain, such as modeling more complex student behaviors and incorporating natural language explanations, the future of personalized learning looks bright. As AI models become more sophisticated, they could unlock a new era of education where every student receives the individualized support they need to succeed.