Mitigating Scaffolding Collapse in Socratic Tutors via Representation Alignment
For developers of LLM-based educational tools, this work addresses a critical failure mode in multi-turn tutoring, though the gains are demonstrated under a specific red-teaming protocol.
LLM-based Socratic tutors suffer from scaffolding collapse, where they prematurely reveal solutions under student pressure. The proposed Scaffold-Preserving Representation Alignment reduces Collapse Rate to 32% and delays collapse onset beyond nine turns on Qwen3-8B across five STEM disciplines.
Large language model (LLM)-based Socratic tutors increasingly guide students through multi-turn questioning, but they can suffer from scaffolding collapse: under sustained student pressure, a tutor gradually abandons guided inquiry and reveals solutions directly. Prior defenses primarily constrain observable responses through prompting, preference optimization, or filtering, leaving the internal representation drift that precedes trajectory-level collapse largely unaddressed. We propose Scaffold-Preserving Representation Alignment, a two-stage framework that first warms up a Socratic tutor with supervised fine-tuning, then combines trajectory-weighted direct preference optimization with a margin-preserving representation loss anchored to frozen reference states. Our method is designed to maintain separation between scaffold-preserving and collapse-inducing hidden states across dialogue turns. We evaluate our method across five STEM disciplines and five red-teaming attack strategies. On Qwen3-8B, our method lowers Collapse Rate to 32%, delays average collapse onset beyond nine turns, and keeps over-refusal low, suggesting that representation-level alignment can improve the robustness of long-horizon Socratic tutoring under our red-teaming protocol.