A recent study proposes a student modeling framework called INSIDE to improve the performance of Large Language Model (LLM)-based simulators. Traditional LLM simulators can reproduce observable actions but often fail to capture the underlying reasoning behind them. This gap is especially pronounced in education, where student simulation is increasingly used for various applications such as evaluating tutoring systems. The INSIDE framework generates internal dialogue grounded in Bloom's Taxonomy across cognitive, affective, and action dimensions and fine-tunes models on paired think traces and actions. The results show that INSIDE improves simulation fidelity in both action fidelity, matching code generation of real students, and reasoning alignment, achieving the highest alignment across models up to 57.9%. Blogger's Review: This study provides a new perspective for student simulation in the education domain. By combining LLM with internal dialogue generation, it can better understand students' thought processes, thereby improving teaching effectiveness.