Imagine a chatbot that doesn't just react to your questions, but anticipates your needs and guides you toward a solution. That's the promise of Conclusion-driven Conversational Question Generation (CCQG), a new approach to building AI systems that can proactively steer conversations towards a specific goal. Traditional chatbots often feel reactive, answering immediate questions without a broader understanding of the conversation's purpose. CCQG changes this by planning ahead, much like a human would in a conversation. Researchers have developed a framework called Proactive Conversational Question Planning with self-Refining (PCQPR), which combines a planning algorithm with the power of large language models (LLMs). PCQPR works by simulating future conversation turns, predicting how different questions might lead to the desired outcome. It then uses feedback from these simulations to refine its questioning strategy, learning from both successful and unsuccessful paths. This feedback loop allows the system to continuously improve its ability to ask the right questions at the right time. The results are impressive. In tests, PCQPR significantly outperforms existing methods in terms of both conversational coherence and its ability to reach the specified conclusion. This technology has exciting potential applications. In customer service, it could guide users through troubleshooting steps, even if they can't articulate their problem clearly. In education, it could personalize learning by asking questions tailored to a student's understanding. And in interactive entertainment, it could lead players through complex narratives. While promising, challenges remain. The effectiveness of the feedback mechanism depends heavily on the capabilities of the LLMs, and there's still room for improvement in integrating human feedback. Nevertheless, PCQPR represents a significant step towards more intelligent, goal-oriented conversational AI.