集成大型语言模型以刻画学生-AI导师对话中的情感动态
Ensembling Large Language Models to Characterize Affective Dynamics in Student-AI Tutor Dialogues
AI总结：
本研究首次提出集成LLM框架，分析学生与AI导师对话中的情感动态，发现学习伴随轻度积极情感，困惑和好奇心常见，消极情感快速消解，中性时刻是干预的关键转折点。
摘要：
虽然近期研究考察了大型语言模型（LLM）在教育情境中的学习影响，但LLM中介的辅导过程中的情感动态仍未被充分理解。本研究首次提出用于辅导对话中大规模情感感知的集成LLM框架，通过关注学习者不断变化的情感状态，推进关于将生成式AI负责任地融入教育的讨论。为实现这一目标，我们分析了两个学期中，由LLM驱动的AI导师PyTutor与来自美国三所机构的261名本科生之间交换的16,986个对话轮次。为了探究学习者的情感体验，我们从三个前沿LLM（Gemini、GPT-4o、Claude）生成零样本情感标注，包括效价、唤醒度和学习帮助性的标量评分，以及自由文本情感标签。这些估计通过秩加权模型内池化和跨模型的多数共识进行融合，以产生稳健的情感画像。我们的分析表明，在与AI导师互动期间，学生通常报告轻度积极情感和中度唤醒度。然而，学习并非一帆风顺：困惑和好奇心常伴随问题解决过程，而挫败感虽不那么常见，但仍会以可能阻碍进展的方式出现。情感状态是短暂的——积极时刻比中性或消极时刻持续稍长，但它们脆弱且容易被干扰。令人鼓舞的是，消极情感往往迅速消解，有时直接反弹为积极状态。中性时刻常常充当转折点，更常引导学生向上而非向下，这提示导师可以在这些关键节点进行干预。
英文摘要：
While recent studies have examined the leaning impact of large language model (LLM) in educational contexts, the affective dynamics of LLM-mediated tutoring remain insufficiently understood. This work introduces the first ensemble-LLM framework for large-scale affect sensing in tutoring dialogues, advancing the conversation on responsible pathways for integrating generative AI into education by attending to learners' evolving affective states. To achieve this, we analyzed two semesters' worth of 16,986 conversational turns exchanged between PyTutor, an LLM-powered AI tutor, and 261 undergraduate learners across three U.S. institutions. To investigate learners' emotional experiences, we generate zero-shot affect annotations from three frontier LLMs (Gemini, GPT-4o, Claude), including scalar ratings of valence, arousal, and learning-helpfulness, along with free-text emotion labels. These estimates are fused through rank-weighted intra-model pooling and plurality consensus across models to produce robust emotion profiles. Our analysis shows that during interaction with the AI tutor, students typically report mildly positive affect and moderate arousal. Yet learning is not uniformly smooth: confusion and curiosity are frequent companions to problem solving, and frustration, while less common, still surfaces in ways that can derail progress. Emotional states are short-lived--positive moments last slightly longer than neutral or negative ones, but they are fragile and easily disrupted. Encouragingly, negative emotions often resolve quickly, sometimes rebounding directly into positive states. Neutral moments frequently act as turning points, more often steering students upward than downward, suggesting opportunities for tutors to intervene at precisely these junctures.
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。
