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arXiv Machine Learning By Yilin Jiang, Fei Tan, Xuanyu Yin, Jing Leng, Aimin Zhou

HACHIMI-II: From Role-Play to Learner Behavior Modeling for Faithful Student Simulation

Read the original on arXiv Machine Learning →

HACHIMI-II redefines student simulation by modeling a learner’s behavior rather than relying on a language model’s role‑play. It builds a student model from the learner’s history, infers their current state, and uses this to constrain the next simulated action. Across item‑response and dialogue datasets, HACHIMI-II achieves markedly smaller ability gaps and more accurate error reproduction than leading baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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