用语言干预激发学习者好奇心，让对话更主动深入。
Curiosity as Linguistic Intervention: Using LLM Tutoring Dialogues to Influence Exploratory Learning Behavior
- 设计对话策略，动态调整新奇、复杂度等语言刺激。
- 好奇心干预使对话轮次最多提升2.4倍，且时间不变。
- 适合研究教育对话、学习行为或人机互动的学者。
大型语言模型（LLMs）为研究语言如何塑造探索性认知提供了新契机，因对话策略可在推理时系统性操控。我们提出CURIOBOT框架，将伯尔琳的可变因素（新奇性、复杂性、冲突感、不确定性）转化为自适应的语言干预，用于对话辅导。在覆盖多个模型家族、领域和主题复杂度的270场辅导对话中，以好奇心为导向的干预持续提升了学习者的探索性行为，固定时间预算下对话轮次最多提升2.4倍。为评估这些效果，我们进一步引入以学习者为中心的评估框架，涵盖探索性提问、对话自主性、建设性挣扎和可观测好奇心。即使辅导方的教学质量保持不变，学习者侧收益仍显著，表明好奇心是部分独立的交互级机制。总体而言，本研究证明了基于LLM的对话可作为研究语言如何影响探索性学习行为的可扩展实验框架。
原文摘要 · Abstract (English)
Large Language Models (LLMs) provide a new opportunity to study how language shapes exploratory cognition because conversational strategies can be systematically manipulated at inference time. We introduce CURIOBOT, a framework that operationalizes Berlyne's collative variables, novelty, complexity, conflict, and uncertainty, as adaptive linguistic interventions for conversational tutoring. Across 270 tutoring conversations spanning multiple model families, domains, and topic complexity levels, curiosity-oriented interventions consistently increased exploratory learner behaviors, producing up to 2.4x more conversational turns under fixed time budgets. To measure these effects, we further introduce a learner-centered evaluation framework capturing exploratory questioning, conversational agency, productive struggle, and observable curiosity. Learner-side gains persisted even when tutor-side instructional quality remained unchanged, suggesting that curiosity functions as a partially independent interaction-level mechanism. More broadly, our results demonstrate that LLM-mediated dialogue can serve as a scalable experimental framework for studying how language shapes exploratory learning behavior.
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