This study investigates how LLMs, specifically GPT-3.5 and GPT-4, can develop tailored questions for Grade 9 math, aligning with active learning principles. By utilizing an iterative method, these models adjust questions based on difficulty and content, responding to feedback from a simulated 'student' model. A novel aspect of the research involved using GPT-4 as a 'teacher' to create complex questions, with GPT-3.5 as the 'student' responding to these challenges. This setup mirrors active learning, promoting deeper engagement. The findings demonstrate GPT-4's superior ability to generate precise, challenging questions and notable improvements in GPT-3.5's ability to handle more complex problems after receiving instruction from GPT-4. These results underscore the potential of LLMs to mimic and enhance active learning scenarios, offering a promising path for AI in customized education. This research contributes to understanding how AI can support personalized learning experiences, highlighting the need for further exploration in various educational contexts

研究了LLMs，特别是GPT-3.5和GPT-4，如何根据主动学习原则为九年级数学提供量身定制的问题。结果显示，GPT-4能够生成准确、具有挑战性的问题，并且GPT-3.5在从GPT-4接受指导后在处理更复杂问题方面有了明显改善，从而突显了LLMs模拟和增强主动学习场景的潜力，为个性化教育中的人工智能提供了有前景的途径，需要在不同的教育环境中进一步探索。

提升教育中主动学习的生成式人工智能：基于GPT-3.5和GPT-4的个性化测试题比较研究