INSIDE the Student's Mind: Jointly Modeling Latent Reasoning and Action in LLM Student Simulators

Synthesis: Niousha, Kang, & Norouzi (2026) introduce INTERNAL STUDENT DIALOGUE (INSIDE), a student modeling framework that fine-tunes LLMs to both act like students and think like them. Two students may submit identical work for entirely different reasons, so INSIDE generates internal dialogue grounded in Bloom's Taxonomy across cognitive, affective, and action dimensions, fine-tuning on paired think-traces and actions. Evaluated against prompting baselines, INSIDE improves action fidelity (matching real students' code generation) and reasoning alignment (up to 57.9% across models). The work advances Student Modeling and Simulating Students research toward cognitively faithful student simulation.

The Reasoning Gap in Student Simulation

LLM-based simulators often reproduce observable actions but fail to capture the underlying reasoning behind them. In education, where student simulation is used to evaluate tutoring systems, this gap is pronounced: two students may submit identical submissions for entirely different reasons.

The INSIDE Framework

INSIDE fine-tunes LLMs not only to act like students but to think like them. It generates internal dialogue grounded in Bloom's Taxonomy across cognitive, affective, and action dimensions, and is trained on paired think traces and actions.

Evaluation

INSIDE is baselined against different prompting frameworks and evaluated on two axes: fidelity of simulated actions and quality of generated internal dialogue. It improves simulation fidelity in action fidelity (matching real students' code generation) and achieves the highest reasoning alignment across models up to 57.9%. Accepted at the Conference on Language Modeling (COLM) 2026.