Learner2Skill externalizes learner interaction history into reusable simulation skills, reproducing fine-grained learner behavior more faithfully at lower token cost
The work proposes Learner2Skill, which externalizes the simulation capability acquired from historical interactions into a persistent and reusable Simulation Skill that captures the learner's current learning state and recurring response patterns, evolves as new real interactions arrive, and can be adapted to a new LLM through lightweight executor calibration; experiments show it more faithfully reproduces fine-grained learner behavior while reducing overall token cost, and that the same constructed Skills can be effectively reused across different LLM executors.