EduZone: A Framework for Evaluating LLM Safety for K-12 Students and Teachers
2026-08-03 • Artificial Intelligence
Artificial IntelligenceComputers and Society
AI summaryⓘ
The authors created EduZone, a tool to check how safe large language models (LLMs) are when used in schools with students and teachers. EduZone looks at different school subjects and various types of risks to see if the AI gives inappropriate or harmful responses. They tested ten LLMs in different conversation styles and found these models often struggle with education-specific risks, especially in longer chats. The authors show that existing safety measures don’t fully protect against these problems and offer EduZone as a better way to test and improve LLMs for safe use in education.
Large Language ModelsK-12 EducationSafety EvaluationAdversarial InteractionsCurriculum ConceptsMulti-turn ConversationsRisk CategoriesAI SafetyEducational Technology
Authors
Junyeong Park, Jieun Han, Haneul Yoo, So-Yeon Ahn, Jinsung Yoon, Alice Oh
Abstract
Large language models (LLMs) are increasingly used across diverse tasks in K-12 education, yet existing safety evaluations rarely examine how harmful or inappropriate content appears in interactions between LLMs and students or teachers. To address this, we present EduZone, an evaluation framework for LLM safety across diverse educational scenarios. Our framework systematically combines (1) student- and teacher-facing LLM usage contexts, (2) fine-grained curriculum concepts, and (3) 6 risk categories and 28 subcategories spanning both conventional and education-specific harms to generate contextually grounded adversarial interactions. We construct these interactions in three settings: single-turn requests, static multi-turn conversations, and dynamic multi-turn conversations. Using these interactions, we evaluate ten LLMs using four safety levels: refusal, safe assistance, risky assistance with safety guidance, and fully risky assistance. Our results reveal greater vulnerability to education-specific risks and dynamic multi-turn interactions, while existing safety guardrails fail to adequately address these risks. EduZone advances LLM safety in education by providing an automated, scalable evaluation framework that supports the development and deployment of safer LLMs in K-12 education.