Mitigating Cognitive Offloading in AI-Assisted Learning Through Structural Gamification

Large language model (LLM) tutors remove exactly the effort that learning requires: students adopt the instant answer, offload the reasoning to the model, and retain less. Friction-based tutors reverse the offloading, yet learners disengage from unbounded friction. This paper tests whether game-structural mechanics—checkpoints, low-stakes failure with retry, and a rescue that guarantees completion—hold the learner inside a tutor's deliberate friction. In a multi-agent simulation of 180 students (three independent draws of 60) across six levels of seeded misconception burden, each student works the same ten-part physics problem under three tutor conditions: instant answer, strict Socratic, and a ten-checkpoint quest in which every part is unlocked only by the student's own attempt. The quest condition removes the measured offloading: students produce 0.999 of the solution steps against 0.302 under the instant answer, self-reported offloading is 1.48 versus 5.58 on a 6-point scale, and every student names the instant-answer condition as the one in which they offloaded most. Structuring the friction also removes its cost: quest sessions complete in 100 percent of cases, against a 14.4 percent abandonment rate in the strict condition, where sessions that run long end when the student's reported motivation reaches the floor. Both friction conditions raise the unaided posttest far above the shared pretest (0.928 and 0.885 versus 0.455); the instant-answer condition reaches only 0.563. Friction that is structured as bounded progression holds the learner inside productive struggle without the attrition that plain withholding carries.