MathBuddy：一种用于情感数学辅导的多模态系统
MathBuddy: A Multimodal System for Affective Math Tutoring
AI总结：
MathBuddy是一个多模态情感数学辅导系统，通过融合文本和面部表情建模学生情绪，映射到教学策略，提升LLM辅导能力，实验显示胜率提升23个百分点。
摘要：
基于LLM的对话系统的快速采用已经在改变教育技术的格局。然而，当前最先进的学习模型并未考虑学生的情感状态。教育心理学的多项研究支持这样的观点：积极或消极的情绪状态会影响学生的学习能力。为了弥合这一差距，我们提出了MathBuddy，一个具有情感意识的LLM驱动的数学辅导系统，它动态地建模学生的情绪，并将其映射到相关的教学策略，使辅导者与学生的对话更具同理心。学生的情绪从对话文本以及面部表情中捕获。学生的情绪从这两种模态中聚合，以自信地提示我们的LLM辅导系统产生具有情感意识的回应。我们使用自动评估指标在八个教学维度上以及用户研究评估了我们的模型。我们报告了使用胜率的大幅23个百分点的性能提升，以及使用DAMR分数在整体水平上3个百分点的提升，这有力地支持了我们的假设，即通过建模学生的情绪来改进基于LLM的辅导系统的教学能力。我们的数据集和代码可在以下网址获取：https://github.com/ITU-NLP/MathBuddy。
英文摘要：
The rapid adoption of LLM-based conversational systems is already transforming the landscape of educational technology. However, the current state-of-the-art learning models do not take into account the student's affective states. Multiple studies in educational psychology support the claim that positive or negative emotional states can impact a student's learning capabilities. To bridge this gap, we present MathBuddy, an emotionally aware LLM-powered Math Tutor, which dynamically models the student's emotions and maps them to relevant pedagogical strategies, making the tutor-student conversation a more empathetic one. The student's emotions are captured from the conversational text as well as from their facial expressions. The student's emotions are aggregated from both modalities to confidently prompt our LLM Tutor for an emotionally-aware response. We have evaluated our model using automatic evaluation metrics across eight pedagogical dimensions and user studies. We report a massive 23 point performance gain using the win rate and a 3 point gain at an overall level using DAMR scores which strongly supports our hypothesis of improving LLM-based tutor's pedagogical abilities by modeling students' emotions. Our dataset and code are available at: https://github.com/ITU-NLP/MathBuddy .
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。
