AI辅导有多真实？一对一教学中模拟对话与人类对话的比较
How Real Is AI Tutoring? Comparing Simulated and Human Dialogues in One-on-One Instruction
AI总结：
本研究通过IRF编码和ENA方法对比AI模拟与人类一对一辅导对话，发现人类对话在话语长度、提问及反馈行为上更优，且二者互动模式存在根本差异，为优化生成式教育对话系统提供实证依据。
摘要：
启发式且搭建支架的师生对话被广泛认为对培养学生的高阶思维和深度学习至关重要。然而，大语言模型（LLMs）目前在生成具备丰富教学性的互动方面面临挑战。本研究系统探究了AI模拟的辅导对话与真实人类辅导对话在结构和行为上的差异。我们采用发起-回应-反馈（IRF）编码方案和认知网络分析（Epistemic Network Analysis, ENA）开展了定量比较。结果显示，人类对话在话语长度、提问（I-Q）行为和通用反馈（F-F）行为方面显著优于AI对话。更重要的是，ENA结果揭示了二者互动模式存在根本性差异：人类对话更具认知引导性和多样性，围绕“提问-事实性回应-反馈”的教学循环展开，清晰体现了教学引导和学生驱动的思考；相比之下，模拟对话呈现出结构简化和行为趋同的模式，围绕“解释-简单回应”循环，本质上是师生之间简单的信息传递。这些发现阐明了当前AI生成辅导内容的关键局限性，为设计和评估更具教学有效性的生成式教育对话系统提供了实证指导。
英文摘要：
Heuristic and scaffolded teacher-student dialogues are widely regarded as critical for fostering students' higher-order thinking and deep learning. However, large language models (LLMs) currently face challenges in generating pedagogically rich interactions. This study systematically investigates the structural and behavioral differences between AI-simulated and authentic human tutoring dialogues. We conducted a quantitative comparison using an Initiation-Response-Feedback (IRF) coding scheme and Epistemic Network Analysis (ENA). The results show that human dialogues are significantly superior to their AI counterparts in utterance length, as well as in questioning (I-Q) and general feedback (F-F) behaviors. More importantly, ENA results reveal a fundamental divergence in interactional patterns: human dialogues are more cognitively guided and diverse, centered around a "question-factual response-feedback" teaching loop that clearly reflects pedagogical guidance and student-driven thinking; in contrast, simulated dialogues exhibit a pattern of structural simplification and behavioral convergence, revolving around an "explanation-simplistic response" loop that is essentially a simple information transfer between the teacher and student. These findings illuminate key limitations in current AI-generated tutoring and provide empirical guidance for designing and evaluating more pedagogically effective generative educational dialogue 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。
