探索大语言模型在对话中预测辅导策略与学生成果的能力
Exploring LLMs for Predicting Tutor Strategy and Student Outcomes in Dialogues
AI总结：
本研究利用两个数学辅导对话数据集，评估Llama 3和GPT-4o预测辅导策略与学生成果的能力，发现策略预测困难但策略高度预示成果，需更强方法。
摘要：
近年来，鉴于在线学习的普及以及由大语言模型（LLMs）驱动的人工智能（AI）代理新兴的辅导能力，辅导对话受到了广泛关注。近期研究表明，辅导者采用的策略能显著影响学生成果，因此需要方法来预测辅导者将如何行动及其行动如何影响学生。然而，很少有研究探讨在对话中预测辅导策略。因此，在本工作中，我们利用两个数学辅导对话数据集，研究了现代LLMs（特别是Llama 3和GPT-4o）预测对话中未来辅导者行动和学生成果的能力。我们发现，即使是最先进的LLMs也难以预测未来的辅导策略，而辅导策略高度预示学生成果，这凸显了需要更强大的方法来解决这一任务。
英文摘要：
Tutoring dialogues have gained significant attention in recent years, given the prominence of online learning and the emerging tutoring abilities of artificial intelligence (AI) agents powered by large language models (LLMs). Recent studies have shown that the strategies used by tutors can have significant effects on student outcomes, necessitating methods to predict how tutors will behave and how their actions impact students. However, few works have studied predicting tutor strategy in dialogues. Therefore, in this work we investigate the ability of modern LLMs, particularly Llama 3 and GPT-4o, to predict both future tutor moves and student outcomes in dialogues, using two math tutoring dialogue datasets. We find that even state-of-the-art LLMs struggle to predict future tutor strategy while tutor strategy is highly indicative of student outcomes, outlining a need for more powerful methods to approach this task.
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。
