  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
