MUSE: Revealing Responsiveness Gaps of LLM Tutors on Multiple-Solution Programming Problems

Abstract

When a problem admits multiple valid solutions, effective tutoring requires building on a student's strategy while introducing alternatives at appropriate points. However, whether LLM tutors can support this flexibility by appropriately responding to students' diverse solution approaches remains largely unexplored. In this work, we introduce MUSE: Multi-solution Understanding and Simulation Environment, a controlled environment for simulating student-tutor conversations on multiple-solution programming problems. Grounded in learning science on responsive instruction and multiple-solution learning, we investigate which solutions LLM tutors prefer, how they respond to students' approaches, and how they transition between solutions. Experiments suggest that LLM tutors exhibit systematic preferences among valid solutions, often favoring brute-force approaches when not providing general advice. More concerningly, tutors often impose these preferences, redirecting students toward their own preferred approach rather than supporting the student's. Yet this tendency reverses once a solution is completed: tutors often remain anchored to the existing approach when a stronger alternative could deepen understanding, and sometimes even transition to a less optimal approach. Thus, tutors struggle both to build on students' reasoning when appropriate and to extend their reasoning when beneficial. Promisingly, targeted post-training improves both tutor responsiveness and transitions toward more optimal solutions.