Toward Controllable Learner Simulation for Teacher Education: Benchmarking Skill-Profile Control in LLM Answer Behavior

Teacher preparation depends on deliberate practice with learners who have identifiable strengths and weaknesses, yet classroom experience accumulates slowly and is hard to standardize. Language-model-simulated students could offer scalable, repeatable practice, but only if a simulated learner can exhibit a specified answer-performance profile rather than behaving as a uniformly capable assistant. We introduce a benchmark for controllable learner simulation in school mathematics: the target condition is a binary vector of retained and suppressed curricular skill families, and we measure how faithfully a language model’s answer behavior realizes that profile via profile-alignment, retained-versus-forgotten, and cross-skill analyses. Across three models and two grades, selective suppression can be induced and measured, yet higher mathematical capability did not imply stronger controllability among the evaluated models: a model can solve the mathematics well yet be the weakest at selective suppression (bootstrap selectivity index 0.35 to 0.70). Induced errors are systematic: independent models converge on the same wrong answers, and an exploratory single-coder analysis found many compaible with documented error categories. Evidence is answer-level (single turn, multiple choice) at Grades 4 and 5. These results show that answer-level skill-profile controllability can be measured as one prerequisite for studying LLM-based learner simulation in teacher-preparation and assessment research.