AI 中文总结
研究自然辅导场景下连续用户与AI交互中反馈特征与学生反应的关系，聚焦具体阐述、情感语言和回复长度三个微观特征，通过数据分析发现具体阐述有助于学生理解，凸显跨交互检查反馈的价值。
AI 中文摘要
人工智能辅助反馈研究表明，具体阐述、情感语言和回复长度等微观层面反馈特征与学习成果相关。现有研究主要在会话或任务层面衡量这些特征。本文研究自然辅导环境中，一次用户与人工智能交互中的反馈如何与紧接着的交互中学生的困惑及理解相关。聚焦人工智能反馈的三个微观特征，分析了16851条来自StudyChat数据集的对话交互，识别出1718例学生表达困惑并继续后续交互的情况。通过卡方检验和广义估计方程发现，具体阐述与学生下次交互中更高理解及更低再困惑相关；共情语言与两种结果均无显著关联；更长回复与更低理解独立相关。这些发现凸显了跨连续用户与人工智能交互检查反馈的价值，表明具体阐述可能在支持学生即时理解中起重要作用。
英文摘要
AI-assisted feedback research has shown that micro-level feedback features, such as concrete elaboration, affective language, and response length, are associated with learning outcomes. Existing studies have primarily examined these features using session- or task-level measures. We examine how feedback provided in one user-AI interaction is associated with student confusion and understanding in the immediately following interaction in a naturalistic tutoring setting. We focus on three micro-level features of AI feedback: concrete elaboration (analogies, comparison-based explanations, or worked examples), affective language (encouragement, empathy, or apology), and response length. We analyzed 16,851 conversational user-AI interactions from the StudyChat dataset, a naturalistic record of student interactions with an LLM tutor in an undergraduate AI course, and identified 1,718 cases in which students expressed confusion and continued to a subsequent interaction. Using chi-square tests and Generalized Estimating Equations (GEE), we found that concrete elaboration was associated with higher understanding and lower re-confusion in the student's next interaction. Empathetic language showed no significant association with either outcome, while longer responses were independently associated with lower understanding. These findings highlight the value of examining feedback across consecutive user-AI interactions and suggest that concrete elaboration may play an important role in supporting immediate student understanding.
