To Align Long, Plan Short: Simulating Diverse Profile-Aligned Students by Realizing Short-Term Plans in Long-Horizon Learning Environments

The paper introduces ALPS, a hierarchical simulated student model that alternates between planning profile‑aligned task segments and generating corresponding actions, aiming to maintain consistent student behavior over long‑horizon learning environments. Unlike existing LLM‑based simulators that focus on short, language‑centered interactions, ALPS is conditioned on observable, environment‑specific behavior profiles and evaluated in both data‑sufficient and data‑scarce settings. Experiments in an inquiry‑based physics game and a pharmacy simulation show that ALPS consistently outperforms turn‑level generation, achieving comparable behavioral density and judge pass rates even with limited real student data, though coverage remains lower.

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• llms
• fine-tuning