EduZone: A Framework for Evaluating LLM Safety for K-12 Students and Teachers

EduZone is an automated evaluation framework that generates contextually grounded adversarial interactions to probe LLM safety in K-12 education, revealing that models are more vulnerable to education-specific harms and dynamic multi-turn conversations than existing guardrails address.

Junyeong Park, Jieun Han, Haneul Yoo, So-Yeon Ahn, Jinsung Yoon, Alice Oh — arXiv (cs.CY / cs.AI) preprint, 2026 (KAIST, Google Cloud AI Research, NYU).

Synthesis

Combines student- and teacher-facing LLM usage contexts with fine-grained curriculum concepts and 6 risk categories / 28 subcategories spanning conventional and education-specific harms.

Builds adversarial interactions in three settings: single-turn requests, static multi-turn conversations, and dynamic multi-turn conversations.

Evaluates ten LLMs across four safety levels: refusal, safe assistance, risky assistance with safety guidance, and fully risky assistance.

Results show greater vulnerability to education-specific risks and dynamic multi-turn interactions; existing safety guardrails fail to adequately address these risks.

Connected Concepts

Human In The Loop AI

Formative Assessment

Pedagogical Safety

Pedagogical LLM Training

Reinforcement Learning

Educational Policy AI

Ethics

Affective Tutoring

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Citation

Junyeong Park, Jieun Han, Haneul Yoo, So-Yeon Ahn, Jinsung Yoon, Alice Oh (2026). EduZone: A Framework for Evaluating LLM Safety for K-12 Students and Teachers. arXiv:2608.02024. arXiv (cs.CY / cs.AI) preprint.