Effective collaboration requires teams to manage complex cognitive and emotional states through Socially Shared Regulation of Learning (SSRL). Physiological synchrony (i.e., longitudinal alignment in physiological signals) can indicate these states, but is hard to interpret on its own. We investigate the physiological and conversational dynamics of four medical dyads diagnosing a virtual patient case using an intelligent tutoring system. Semantic shifts in dialogue were correlated with transient physiological synchrony peaks. We also coded utterance segments for SSRL and derived cosine similarity using sentence embeddings. The results showed that activating prior knowledge featured significantly lower semantic similarity than simpler task execution. High physiological synchrony was associated with lower semantic similarity, suggesting that such moments involve exploratory and varied language use. Qualitative analysis triangulated these synchrony peaks as ``pivotal moments'': successful teams synchronized during shared discovery, while unsuccessful teams peaked during shared uncertainty. This research advances human-centered AI by demonstrating how biological signals can be fused with dialogues to understand critical moments in problem solving.

翻译：有效协作要求团队通过社会共享学习调节（SSRL）来管理复杂的认知与情绪状态。生理同步（即生理信号在纵向时间上的校准）虽能指示这些状态，但单独解读存在困难。本研究借助智能辅导系统，探究四组医患双人组在诊断虚拟病例时的生理与对话动态。研究发现，对话中的语义转换与瞬时生理同步峰值存在关联。我们进一步对语段进行SSRL编码，并通过句子嵌入计算余弦相似度。结果表明：激活先前知识环节的语义相似性显著低于简单任务执行环节；高生理同步与低语义相似性相关，表明此类时刻涉及探索性且多样化的语言使用。定性分析将这些同步峰值验证为"关键转折点"：高效团队在共同发现时产生同步，低效团队则在共享不确定性时形成峰值。本研究通过揭示生物信号与对话的融合如何理解问题解决中的关键时刻，推动了人本人工智能的发展。