The Age of Curiosity Meets the Age of AI: Benchmarking Child Safety in Large Language Models

• URL: http://arxiv.org/abs/2605.25510v2
• Date: Tue, 26 May 2026 09:53:31 GMT
• Title: The Age of Curiosity Meets the Age of AI: Benchmarking Child Safety in Large Language Models
• Abstract summary: We introduce KIDBench, a benchmark for evaluating child-facing Large Language Models safety for ages 7-11.<n> KIDBench contains realistic child queries across ten categories, with single-turn prompts and multi-turn child-actor simulations.<n> KIDGuardLlama is a child-oriented response model, showing how KIDBench supports safer child-facing AI.
• Score: 35.56441154476198
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Children increasingly have access to Large Language Models (LLMs), which may expose them to responses that are developmentally inappropriate or require age-sensitive safety, guidance, and boundaries. Existing LLM safety evaluations largely focus on harmful-content avoidance and do not explicitly target child-facing safety. We introduce KIDBench, a benchmark for evaluating child-facing LLM safety for ages 7-11 using a developmental-psychology-grounded LLM-as-a-Judge rubric. KIDBench contains realistic child queries across ten categories, with single-turn prompts and multi-turn child-actor simulations. We compare no-cues prompts with no child context, implicit-cues prompts that suggest a child speaker, and explicit age instructions. Implicit-cues improve scores by 9-47% across models, while explicit age adds a further 10-30% gain. Cross-lingual and cultural evaluations show uneven safety behavior across languages and country contexts. Multi-turn simulations show that child-facing response quality can degrade by 6-24% from the first to worst turn. Beyond evaluation, we introduce KIDGuardLlama, a child-safety evaluator, and KIDLlama, a child-oriented response model, showing how KIDBench supports safer child-facing AI.

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