HACHIMI-II：从角色扮演到学习者行为建模，实现高保真学生模拟

View PDF HTML (experimental)

Abstract:Simulated students let researchers test tutoring systems and teaching strategies before real learners are available, but only if the simulation reproduces where a particular learner succeeds and fails. The common approach asks a large language model to role-play a student, but a role does not model what this learner knows or will do next, so the model's own competence fills the gap. Simulated students perform at a level set by the model, differ little from one another, and seldom make the real learners' errors. We model the learner's behavior and recast student simulation as third-person prediction of the learner's next action. HACHIMI-II builds a student model from the learner's history, infers the learner's state for the current item or tutor turn, and compiles it into a behavior harness that constrains the next action. On three item-response datasets, the Wasserstein-1 distance between simulated and real student ability is 2.4--8.4$\times$ smaller than that of the strongest baseline. On two dialogue datasets, HACHIMI-II reproduces real student errors with the same knowledge type, locus, and mechanism 1.8--5.1$\times$ more often than the strongest baseline, and ablations trace the gains mainly to the explicit learner state. Predicting the learner makes capable models usable as simulated students.

Comments:	18 pages, 4 figures
Subjects:	Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
Cite as:	arXiv:2610.19654 [cs.CL]
	(or arXiv:2610.19654v1 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2610.19654 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yilin Jiang [view email]
[v1] Wed, 14 Oct 2026 06:24:39 UTC (1,757 KB)