Simulating Students or Sycophantic Problem Solving? On Misconception Faithfulness of LLM Simulators

TL;DR

This paper evaluates whether large language models (LLMs) can simulate student misconceptions during interactions and introduces a framework to measure and improve their misconception faithfulness.

Contribution

The paper presents a novel framework and metrics for assessing and enhancing LLM simulators' ability to maintain and update misconceptions interactively.

Findings

LLMs show near-zero misconception flip scores, indicating poor misconception faithfulness.

Models tend to behave as problem-solvers rather than belief-maintaining students.

Post-training methods improve misconception faithfulness, with reinforcement learning showing consistent gains.

Abstract

Large language models (LLMs) can fluently generate student-like responses, making them attractive as simulated students for training and evaluating AI tutors and human educators. Yet such simulators are typically evaluated by output similarity to real students, not by whether they behave like students with coherent misconceptions during interaction. We introduce a controlled framework for evaluating misconception faithfulness, whether a simulator maintains a misconception-driven belief state and updates selectively when feedback addresses the underlying misconception. Central to our framework is a misconception-contrastive feedback protocol that compares targeted feedback against two controls: misaligned feedback (targeting a different but plausible misconception) and generic feedback (only identifying answer is wrong). We propose Selective Flip Score (SFS), which quantifies how much more often a simulator flips its answer under targeted feedback than under contrastive controls. Across seven LLMs (4B-120B), multiple datasets, and prompting strategies, simulators exhibit near-zero SFS, correcting their answers at similarly high rates regardless of feedback relevance. Further analyses reveal a sycophantic failure mode: models behave less like students with misconceptions but more like problem-solvers who treat any corrective signal as a cue to abandon the simulated belief and re-solve from internal knowledge. To address this, we develop a post-training pipeline spanning supervised fine-tuning (SFT), preference optimization, and reinforcement learning (RL) with an SFS-aligned reward; SFT yields notable gains up to +0.56, and SFS-aligned RL provides more consistent improvements than preference optimization. Our results establish misconception faithfulness as a challenging yet trainable property, motivating a shift from static output matching toward interactive, belief-aware student modeling.