How Real Is AI Tutoring? Comparing Simulated and Human Dialogues in One-on-One Instruction

This paper compares AI-simulated tutoring dialogues with authentic human teacher-student dialogues in one-on-one instruction using IRF coding and Epistemic Network Analysis, finding that human dialogues exhibit more cognitively guided and diverse interactional patterns while AI dialogues show structural simplification and behavioral convergence. The study provides empirical evidence for limitations in current LLM-generated tutoring interactions and offers guidance for designing more pedagogically effective AI dialogue systems.

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

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