SHAPE: Unifying Safety, Helpfulness and Pedagogy for Educational LLMs
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918473840459776 |
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| author | Zhao, Sihang Yu, Kangrui Yuan, Youliang He, Pinjia Wen, Hongyi |
| author_facet | Zhao, Sihang Yu, Kangrui Yuan, Youliang He, Pinjia Wen, Hongyi |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_22134 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SHAPE: Unifying Safety, Helpfulness and Pedagogy for Educational LLMs Zhao, Sihang Yu, Kangrui Yuan, Youliang He, Pinjia Wen, Hongyi Computation and Language 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 |
| title | SHAPE: Unifying Safety, Helpfulness and Pedagogy for Educational LLMs |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2604.22134 |