The Age of Curiosity Meets the Age of AI: Benchmarking Child Safety in Large Language Models
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 general 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 LLM-as-a-Judge rubric grounded in developmental-psychology. 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 s...
| Authors | Samee Arif, Angana Borah, Rada Mihalcea |
|---|---|
| Primary category | cs.CL |
| Published | 25 May 2026 |
| Last updated at source | 2 September 2026 |
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