Intelligent Tutoring Systems often grant learners shared control over skill and problem selection. This choice brings motivational and metacognitive benefits. At the same time, past literature suggests that learners exhibit diverse preferences and strategies in selecting tasks, for instance, by avoiding challenge. Although underexplored, differences in learner task-selection strategies may interact with mastery learning systems that optimize task-selection based on estimated knowledge, potentially leading to undesirable student-level differences in learning outcomes. Algorithmic constraints on problem selection may help mitigate this issue. However, this possibility has not been comprehensively explored in prior work, in part because testing such constraints in real-world classrooms is costly. We propose a simulation-based framework to observe how varying learner task-selection strategies combined with system constraints shape mastery learning efficiency. Using interaction data from 261 students across two mathematical domains with different problem structures (equation solving, graph interpretation), we simulate common task-selection strategies such as Weakness Targeting and Interleaving, grounded in prior literature. We then evaluate how these strategies affect overpractice as a common measure of mastery learning efficiency. Results show substantial variability in efficiency across strategies, with risk-averse strategies producing higher levels of overpractice, especially for more complex multi-step problems. Targeted system constraints significantly reduce these inefficiencies for maladaptive strategies while having minimal impact on already efficient strategies. Together, these findings demonstrate how simulation grounded in real student data can support data-driven redesign of shared-control tutoring systems prior to classroom deployment.

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