Generative AI For Enhancing Active Learning In Education: A Comparative Study Of GPT-3.5 And GPT-4 In Crafting Customized Test Questions

Rouzegar Hamdireza, Makrehchi Masoud. Proceedings of The 2024

[Paper]    
Fine Tuning GPT Model Architecture Reinforcement Learning

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

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