用多个大模型融合分析学生与AI导师对话中的情绪变化。
Ensembling Large Language Models to Characterize Affective Dynamics in Student-AI Tutor Dialogues
- 用三个顶尖大模型生成情绪标签，通过加权融合提升准确性。
- 发现学生情绪多为轻度积极，困惑与好奇常伴随解题过程。
- 中性状态是关键转折点，适合导师及时干预。
尽管已有研究探讨大语言模型（LLM）在教育中的学习影响，但其在辅导对话中引发的情绪动态仍不明确。本文提出首个基于集成大模型的规模化情感感知框架，用于分析师生对话中的情绪演变，推动负责任地将生成式AI融入教育。研究基于三所美国高校261名本科生与名为PyTutor的LLM驱动导师之间两个学期的16,986次对话交互。为探究学习者的情绪体验，我们利用三个前沿大模型（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评分，以及自由文本情绪标签。通过模型内排名加权聚合与跨模型多数共识策略融合结果，构建稳健的情绪画像。分析显示，学生与AI导师互动时通常呈现轻度积极情绪与中等唤醒度。然而学习过程并非平顺：困惑与好奇常伴随解题，沮丧虽较少出现，但仍会阻碍进展。情绪状态持续时间短——积极时刻略长于中性或消极时刻，但易被干扰。令人鼓舞的是，负面情绪常快速缓解，有时直接转为积极状态。中性时刻频繁作为转折点，更多引导学生向上发展，提示导师可在此类节点精准介入。
原文摘要 · Abstract (English)
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.
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