用于增强教育中主动学习的生成式AI：GPT-3.5与GPT-4在定制化试题命制中的比较研究
Generative AI for Enhancing Active Learning in Education: A Comparative Study of GPT-3.5 and GPT-4 in Crafting Customized Test Questions
AI总结：
本研究对比GPT-3.5与GPT-4在九年级数学定制试题命制中的表现，通过迭代调整、GPT-4当教师GPT-3.5当学生的设置模拟主动学习，发现GPT-4出题能力更优，且能提升GPT-3.5的解题能力，为AI赋能个性化教育提供参考。
摘要：
本研究探究大语言模型（LLM），具体为GPT-3.5和GPT-4，如何依据主动学习原则，为九年级数学开发定制化试题。研究采用迭代方法，让这些模型根据模拟“学生”模型的反馈，围绕难度和内容调整试题。该研究的一个创新点是将GPT-4作为“教师”命制复杂试题，GPT-3.5作为“学生”应对这些挑战，这一设置复刻了主动学习模式，能促进更深层次的参与。研究结果表明，GPT-4在生成精准、有挑战性的试题方面能力更优，且GPT-3.5在接受GPT-4的指导后，处理更复杂问题的能力有显著提升。这些结果凸显了LLM模拟并增强主动学习场景的潜力，为AI在定制化教育中的应用提供了可行方向。本研究有助于理解AI如何支持个性化学习体验，同时表明有必要在各类教育场景中开展进一步探索。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
