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。