通过对话学习：参与度和效果比解释更重要
Learning Through Dialogue: Engagement and Efficacy Matter More Than Explanations
AI总结：
研究发现，LLM学习效果取决于用户参与状态和认知投入，而非单纯依赖解释质量。
摘要：
大型语言模型（LLMs）正越来越多地被用作学习的对话伙伴，但支持用户学习和参与的互动动态仍被低估。我们分析了LLM和参与者聊天中的语言和互动特征，涵盖397次关于社会政治问题的人与LLM对话，以确定LLM解释如何影响政治知识和信心变化的机制和条件。中介分析显示，LLM解释的丰富性部分通过促进用户的反思洞察来支持信心，而其对知识增长的影响完全通过用户的认知参与发挥作用。调节分析显示，这些影响高度依赖政治效能。信心增长取决于高效能用户如何体验和解决不确定性。知识增长取决于高效能用户利用延长互动的能力，更长的对话主要受益于反思型用户。总之，我们发现从LLM学习是一种互动成就，而不是更好的解释的统一结果。研究结果强调了将LLM解释行为与用户参与状态对齐以支持有效学习的重要性，以设计人机交互系统。
英文摘要：
Large language models (LLMs) are increasingly used as conversational partners for learning, yet the interactional dynamics supporting users' learning and engagement are understudied. We analyze the linguistic and interactional features from both LLM and participant chats across 397 human-LLM conversations about socio-political issues to identify the mechanisms and conditions under which LLM explanations shape changes in political knowledge and confidence. Mediation analyses reveal that LLM explanatory richness partially supports confidence by fostering users' reflective insight, whereas its effect on knowledge gain operates entirely through users' cognitive engagement. Moderation analyses show that these effects are highly conditional and vary by political efficacy. Confidence gains depend on how high-efficacy users experience and resolve uncertainty. Knowledge gains depend on high-efficacy users' ability to leverage extended interaction, with longer conversations benefiting primarily reflective users. In summary, we find that learning from LLMs is an interactional achievement, not a uniform outcome of better explanations. The findings underscore the importance of aligning LLM explanatory behavior with users' engagement states to support effective learning in designing Human-AI interactive systems.
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。
