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arXiv · 2604.22134

SHAPE: Unifying Safety, Helpfulness and Pedagogy for Educational LLMs

Sihang Zhao·Kangrui Yu·Youliang Yuan·Pinjia He·Hongyi Wen
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Abstract

Large Language Models (LLMs) have been widely explored in educational scenarios. We identify a critical vulnerability in current educational LLMs, pedagogical jailbreaks, where students use answer-inducing prompts to elicit solutions rather than scaffolded instructions. To enable systematic study, we unify and formalize safe, helpful, and pedagogical behaviors with a knowledge-mastery graph and introduce SHAPE, a benchmark of 9,087 student-question pairs for evaluating tutoring behavior under adversarial pressure. We propose a graph-augmented tutoring pipeline that infers prerequisite concepts from queries, identifies mastery gaps, and routes generation between instructing and problem-solving via explicit gating. Experiments across multiple LLMs show that our method yields significantly improved safety under two pedagogical jailbreak settings, while maintaining near-ceiling helpfulness under the same evaluation protocol. Our code and data are available at https://github.com/MAPS-research/SHaPE

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Sihang Zhao, Kangrui Yu, Youliang Yuan, Pinjia He, Hongyi Wen. 2026-04-24. SHAPE: Unifying Safety, Helpfulness and Pedagogy for Educational LLMs. https://arxiv.org/abs/2604.22134

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